[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-data-engineering-ai-s-105b-hidden-powerhouse-summary":3,"summaries-facets-categories":110,"summary-related-data-engineering-ai-s-105b-hidden-powerhouse-summary":4515},{"id":4,"title":5,"ai":6,"body":13,"categories":66,"created_at":68,"date_modified":68,"description":59,"extension":69,"faq":68,"featured":70,"kicker_label":68,"meta":71,"navigation":91,"path":92,"published_at":93,"question":68,"scraped_at":94,"seo":95,"sitemap":96,"source_id":97,"source_name":98,"source_type":99,"source_url":100,"stem":101,"tags":102,"thumbnail_url":68,"tldr":107,"tweet":68,"unknown_tags":108,"__hash__":109},"summaries\u002Fsummaries\u002Fdata-engineering-ai-s-105b-hidden-powerhouse-summary.md","Data Engineering: AI's $105B Hidden Powerhouse",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",7975,2250,14314,0.00221315,{"type":14,"value":15,"toc":58},"minimark",[16,21,25,28,32,35,38,42,45,48,51,55],[17,18,20],"h2",{"id":19},"lakehouses-and-open-formats-enable-interoperable-petabyte-scale-storage","Lakehouses and Open Formats Enable Interoperable Petabyte-Scale Storage",[22,23,24],"p",{},"Open table formats like Apache Iceberg, Delta Lake, and Apache Hudi provide ACID transactions, schema evolution, time travel, and efficient management on cloud object storage, replacing proprietary warehouses. Iceberg leads due to broadest support (Snowflake, BigQuery, Databricks, Trino, Dremio, DuckDB) and unique partition evolution without data rewrites, ideal for petabyte datasets. Delta Lake excels in Spark-integrated lakehouses with Change Data Feed; Hudi optimizes streaming upserts via Merge-on-Read. Convergence via Databricks' UniForm and Snowflake's native Iceberg support ensures interoperability, letting data engineers avoid vendor lock-in. Use Iceberg for new lakehouses to maximize engine compatibility.",[22,26,27],{},"Databricks dominates AI workloads at $5.4B ARR ($134B valuation) with Unity Catalog governance, MosaicML foundation models, and Agent Bricks, pulling ahead of Snowflake's $58B cap and $1.21B quarterly revenue focused on SQL\u002FBI via separated compute\u002Fstorage. Run Databricks for ML\u002FAI engineering, Snowflake for analytics—hybrids common in Fortune 500.",[17,29,31],{"id":30},"real-time-streaming-and-transformations-replace-batch-processing","Real-Time Streaming and Transformations Replace Batch Processing",[22,33,34],{},"82% of organizations use real-time streaming; Apache Kafka serves as the event log backbone, with Flink emerging as stateful processing leader via Flink 2.2's SQL-native AI\u002FML inference, disaggregated state, and Process Table Functions. Use Flink for low-latency event-driven apps, Spark Structured Streaming for unified batch\u002Fstreaming with ML. SQL-first tools like ksqlDB, Flink SQL, Materialize enable analysts to handle windowing\u002Fjoins without code.",[22,36,37],{},"dbt redefines transformations for 70% of engineers, applying version control\u002FCI\u002FCD to SQL models; its Semantic Layer ensures metric consistency across BI tools. dbt-Fivetran merger integrates ingestion, evolving dbt into a full platform with Copilot\u002FMesh.",[17,39,41],{"id":40},"orchestration-governance-and-architectures-scale-reliable-pipelines","Orchestration, Governance, and Architectures Scale Reliable Pipelines",[22,43,44],{},"Airflow dominates production via ecosystem scale, but Dagster's asset-centric model (define datasets, auto-build DAGs) and Prefect's dynamic Python flows improve DX with type-checking\u002Flocal dev. Start fresh teams with Dagster\u002FPrefect; stick to Airflow for legacy.",[22,46,47],{},"Governance embeds quality\u002Flineage via Monte Carlo (data downtime monitoring: freshness\u002Fvolume\u002Fschema) and Atlan (unified control plane, Forrester\u002FGartner leader). NASDAQ pairs them for automated checks. Data mesh decentralizes domain-owned products (50% collaboration gains); data fabric automates via metadata\u002FAI (30% faster delivery)—hybrids in 60% of enterprises by 2026.",[22,49,50],{},"Cloud stacks: AWS (S3\u002FGlue\u002FRedshift\u002FKinesis\u002FEMR\u002FSageMaker) for flexibility; GCP BigQuery (Iceberg\u002Fstreaming\u002FML); Azure Fabric\u002FSynapse with Databricks integration.",[17,52,54],{"id":53},"builder-stack-and-ai-symbiosis-drive-production-scale","Builder Stack and AI Symbiosis Drive Production Scale",[22,56,57],{},"Core skills: SQL\u002FPython, cloud (AWS\u002FGCP\u002FAzure), Spark\u002FFlink, dbt, Airflow\u002FDagster, Iceberg. Build lakehouse + Iceberg + dbt + observability (Monte Carlo\u002FGreat Expectations) from day one. AI relies on data readiness over models: real-time features, RAG datasets, vector DBs. Data engineers evolve to platform architects amid 2.9M global vacancies, 20% US growth, $119K-$183K salaries, $105B market (15.38% CAGR to $187B by 2030). Streaming defaults; autonomous platforms hit $15B by 2033.",{"title":59,"searchDepth":60,"depth":60,"links":61},"",2,[62,63,64,65],{"id":19,"depth":60,"text":20},{"id":30,"depth":60,"text":31},{"id":40,"depth":60,"text":41},{"id":53,"depth":60,"text":54},[67],"Data Science & Visualization",null,"md",false,{"content_references":72,"triage":86},[73,78,81],{"type":74,"title":75,"author":76,"context":77},"report","Forrester Wave for Data Governance Solutions (Q3 2025)","Forrester","cited",{"type":74,"title":79,"author":80,"context":77},"2026 Gartner Magic Quadrant for Data & Analytics Governance","Gartner",{"type":82,"title":83,"author":84,"context":85},"other","Data mesh","Zhamak Dehghani","mentioned",{"relevance":87,"novelty":88,"quality":88,"actionability":88,"composite":89,"reasoning":90},5,4,4.35,"Category: Data Science & Visualization. The article provides in-depth insights into modern data engineering practices that are crucial for building AI-powered products, addressing the audience's need for practical applications in data infrastructure. It discusses specific technologies like Apache Iceberg and Databricks, which are directly applicable for engineers looking to implement scalable data solutions.",true,"\u002Fsummaries\u002Fdata-engineering-ai-s-105b-hidden-powerhouse-summary","2026-04-18 11:10:43","2026-04-18 15:50:19",{"title":5,"description":59},{"loc":92},"32ab72d85ce3b219","Towards AI","article","https:\u002F\u002Fpub.towardsai.net\u002Fthe-data-engineering-how-modern-data-infrastructure-is-powering-the-ai-revolution-279d1af04635?source=rss----98111c9905da---4","summaries\u002Fdata-engineering-ai-s-105b-hidden-powerhouse-summary",[103,104,105,106],"data-science","cloud","open-source","devops-cloud","Data engineering underpins all AI success with $105B market, lakehouses via Iceberg\u002FDelta, real-time Flink\u002FKafka streaming, dbt transformations (70% adoption), and Databricks' $134B AI lead over Snowflake.",[106],"QiZe-xBq0qUjWKe1Fnb-nX9qUePF07yPMpOUIlnMLCw",[111,114,116,119,121,124,127,130,133,135,137,139,141,143,145,147,150,152,154,156,158,160,162,165,167,169,171,173,175,177,179,181,183,185,187,189,191,193,195,197,199,201,203,205,207,209,211,213,215,217,219,221,223,225,227,229,231,233,235,237,239,241,243,245,247,249,251,253,255,257,259,261,263,265,267,269,271,273,275,277,279,281,283,285,287,289,291,293,295,297,299,301,303,305,307,309,311,313,315,317,319,321,323,325,327,329,331,333,335,337,339,341,343,345,347,349,351,353,355,357,359,361,363,365,367,369,371,373,375,377,379,381,383,385,387,389,391,393,395,397,399,401,403,405,407,409,411,413,415,417,419,421,423,425,427,429,431,433,435,437,439,441,443,445,447,449,451,453,455,457,459,461,463,465,467,469,471,474,476,478,480,482,484,486,488,490,492,494,496,498,500,502,504,506,508,510,512,514,516,518,520,522,524,526,528,530,532,534,536,538,540,542,544,546,548,550,552,554,556,558,561,563,565,567,569,571,573,575,577,579,581,583,585,587,589,591,593,595,597,599,601,603,605,607,609,611,613,615,617,619,621,623,625,627,629,631,633,635,637,639,641,643,645,647,649,651,653,655,657,659,661,663,665,667,669,671,673,675,677,679,681,683,685,687,689,691,693,695,697,699,701,703,705,707,709,711,713,715,717,719,721,723,725,727,729,731,733,735,737,739,741,743,745,747,749,751,753,755,757,759,761,763,765,767,769,771,773,775,777,779,781,783,785,787,789,791,793,795,797,799,801,803,805,807,809,811,813,815,817,819,821,823,825,827,829,831,833,835,837,839,841,843,845,847,849,851,853,855,857,859,861,863,865,867,869,871,873,875,877,879,881,883,885,887,889,891,893,895,897,899,901,903,905,907,909,911,913,915,917,919,921,923,925,927,929,931,933,935,937,939,941,943,945,947,949,951,953,955,957,959,961,963,965,967,969,971,973,975,977,979,981,983,985,987,989,991,993,995,997,999,1001,1003,1005,1007,1009,1011,1013,1015,1017,1019,1021,1023,1025,1027,1029,1031,1033,1035,1037,1039,1041,1043,1045,1047,1049,1051,1053,1055,1057,1059,1061,1063,1065,1067,1069,1071,1073,1075,1077,1079,1081,1083,1085,1087,1089,1091,1093,1095,1097,1099,1101,1103,1105,1107,1109,1111,1113,1115,1117,1119,1121,1123,1125,1127,1129,1131,1133,1135,1137,1139,1141,1143,1145,1147,1149,1151,1153,1155,1157,1159,1161,1163,1165,1167,1169,1171,1173,1175,1177,1179,1181,1183,1185,1187,1189,1191,1193,1195,1197,1199,1201,1203,1205,1207,1209,1211,1213,1215,1217,1219,1221,1223,1225,1227,1229,1231,1233,1235,1237,1239,1241,1243,1245,1247,1249,1251,1253,1255,1257,1259,1261,1263,1265,1267,1269,1271,1273,1275,1277,1279,1281,1283,1285,1287,1289,1291,1293,1295,1297,1299,1301,1303,1305,1307,1309,1311,1313,1315,1317,1319,1321,1323,1325,1327,1329,1331,1333,1335,1337,1339,1341,1343,1345,1347,1349,1351,1353,1355,1357,1359,1361,1363,1365,1367,1369,1371,1373,1375,1377,1379,1381,1383,1385,1387,1389,1391,1393,1395,1397,1399,1401,1403,1405,1407,1409,1411,1413,1415,1417,1419,1421,1423,1425,1427,1429,1431,1433,1435,1437,1439,1441,1443,1445,1447,1449,1451,1453,1455,1457,1459,1461,1463,1465,1467,1469,1471,1473,1475,1477,1479,1481,1483,1485,1487,1489,1491,1493,1495,1497,1499,1501,1503,1505,1507,1509,1511,1513,1515,1517,1519,1521,1523,1525,1527,1529,1531,1533,1535,1537,1539,1541,1543,1545,1547,1549,1551,1553,1555,1557,1559,1561,1563,1565,1567,1569,1571,1573,1575,1577,1579,1581,1583,1585,1587,1589,1591,1593,1595,1597,1599,1601,1603,1605,1607,1609,1611,1613,1615,1617,1619,1621,1623,1625,1627,1629,1631,1633,1635,1637,1639,1641,1643,1645,1647,1649,1651,1653,1655,1657,1659,1661,1663,1665,1667,1669,1671,1673,1675,1677,1679,1681,1683,1685,1687,1689,1691,1693,1695,1697,1699,1701,1703,1705,1707,1709,1711,1713,1715,1717,1719,1721,1723,1725,1727,1729,1731,1733,1735,1737,1739,1741,1743,1745,1747,1749,1751,1753,1755,1757,1759,1761,1763,1765,1767,1769,1771,1773,1775,1777,1779,1781,1783,1785,1787,1789,1791,1793,1795,1797,1799,1801,1803,1805,1807,1809,1811,1813,1815,1817,1819,1821,1823,1825,1827,1829,1831,1833,1835,1837,1839,1841,1843,1845,1847,1849,1851,1853,1855,1857,1859,1861,1863,1865,1867,1869,1871,1873,1875,1877,1879,1881,1883,1885,1887,1889,1891,1893,1895,1897,1899,1901,1903,1905,1907,1909,1911,1913,1915,1917,1919,1921,1923,1925,1927,1929,1931,1933,1935,1937,1939,1941,1943,1945,1947,1949,1951,1953,1955,1957,1959,1961,1963,1965,1967,1969,1971,1973,1975,1977,1979,1981,1983,1985,1987,1989,1991,1993,1995,1997,1999,2001,2003,2005,2007,2009,2011,2013,2015,2017,2019,2021,2023,2025,2027,2029,2031,2033,2035,2037,2039,2041,2043,2045,2047,2049,2051,2053,2055,2057,2059,2061,2063,2065,2067,2069,2071,2073,2075,2077,2079,2081,2083,2085,2087,2089,2091,2093,2095,2097,2099,2101,2103,2105,2107,2109,2111,2113,2115,2117,2119,2121,2123,2125,2127,2129,2131,2133,2135,2137,2139,2141,2143,2145,2147,2149,2151,2153,2155,2157,2159,2161,2163,2165,2167,2169,2171,2173,2175,2177,2179,2181,2183,2185,2187,2189,2191,2193,2195,2197,2199,2201,2203,2205,2207,2209,2211,2213,2215,2217,2219,2221,2223,2225,2227,2229,2231,2233,2235,2237,2239,2241,2243,2245,2247,2249,2251,2253,2255,2257,2259,2261,2263,2265,2267,2269,2271,2273,2275,2277,2279,2281,2283,2285,2287,2289,2291,2293,2295,2297,2299,2301,2303,2305,2307,2309,2311,2313,2315,2317,2319,2321,2323,2325,2327,2329,2331,2333,2335,2337,2339,2341,2343,2345,2347,2349,2351,2353,2355,2357,2359,2361,2363,2365,2367,2369,2371,2373,2375,2377,2379,2381,2383,2385,2387,2389,2391,2393,2395,2397,2399,2401,2403,2405,2407,2409,2411,2413,2415,2417,2419,2421,2423,2425,2427,2429,2431,2433,2435,2437,2439,2441,2443,2445,2447,2449,2451,2453,2455,2457,2459,2461,2463,2465,2467,2469,2471,2473,2475,2477,2479,2481,2483,2485,2487,2489,2491,2493,2495,2497,2499,2501,2503,2505,2507,2509,2511,2513,2515,2517,2519,2521,2523,2525,2527,2529,2531,2533,2535,2537,2539,2541,2543,2545,2547,2549,2551,2553,2555,2557,2559,2561,2563,2565,2567,2569,2571,2573,2575,2577,2579,2581,2583,2585,2587,2589,2591,2593,2595,2597,2599,2601,2603,2605,2607,2609,2611,2613,2615,2617,2619,2621,2623,2625,2627,2629,2631,2633,2635,2637,2639,2641,2643,2645,2647,2649,2651,2653,2655,2657,2659,2661,2663,2665,2667,2669,2671,2673,2675,2677,2679,2681,2683,2685,2687,2689,2691,2693,2695,2697,2699,2701,2703,2705,2707,2709,2711,2713,2715,2717,2719,2721,2723,2725,2727,2729,2731,2733,2735,2737,2739,2741,2743,2745,2747,2749,2751,2753,2755,2757,2759,2761,2763,2765,2767,2769,2771,2773,2775,2777,2779,2781,2783,2785,2787,2789,2791,2793,2795,2797,2799,2801,2803,2805,2807,2809,2811,2813,2815,2817,2819,2821,2823,2825,2827,2829,2831,2833,2835,2837,2839,2841,2843,2845,2847,2849,2851,2853,2855,2857,2859,2861,2863,2865,2867,2869,2871,2873,2875,2877,2879,2881,2883,2885,2887,2889,2891,2893,2895,2897,2899,2901,2903,2905,2907,2909,2911,2913,2915,2917,2919,2921,2923,2925,2927,2929,2931,2933,2935,2937,2939,2941,2943,2945,2947,2949,2951,2953,2955,2957,2959,2961,2963,2965,2967,2969,2971,2973,2975,2977,2979,2981,2983,2985,2987,2989,2991,2993,2995,2997,2999,3001,3003,3005,3007,3009,3011,3013,3015,3017,3019,3021,3023,3025,3027,3029,3031,3033,3035,3037,3039,3041,3043,3045,3047,3049,3051,3053,3055,3057,3059,3061,3063,3065,3067,3069,3071,3073,3075,3077,3079,3081,3083,3085,3087,3089,3091,3093,3095,3097,3099,3101,3103,3105,3107,3109,3111,3113,3115,3117,3119,3121,3123,3125,3127,3129,3131,3133,3135,3137,3139,3141,3143,3145,3147,3149,3151,3153,3155,3157,3159,3161,3163,3165,3167,3169,3171,3173,3175,3177,3179,3181,3183,3185,3187,3189,3191,3193,3195,3197,3199,3201,3203,3205,3207,3209,3211,3213,3215,3217,3219,3221,3223,3225,3227,3229,3231,3233,3235,3237,3239,3241,3243,3245,3247,3249,3251,3253,3255,3257,3259,3261,3263,3265,3267,3269,3271,3273,3275,3277,3279,3281,3283,3285,3287,3289,3291,3293,3295,3297,3299,3301,3303,3305,3307,3309,3311,3313,3315,3317,3319,3321,3323,3325,3327,3329,3331,3333,3335,3337,3339,3341,3343,3345,3347,3349,3351,3353,3355,3357,3359,3361,3363,3365,3367,3369,3371,3373,3375,3377,3379,3381,3383,3385,3387,3389,3391,3393,3395,3397,3399,3401,3403,3405,3407,3409,3411,3413,3415,3417,3419,3421,3423,3425,3427,3429,3431,3433,3435,3437,3439,3441,3443,3445,3447,3449,3451,3453,3455,3457,3459,3461,3463,3465,3467,3469,3471,3473,3475,3477,3479,3481,3483,3485,3487,3489,3491,3493,3495,3497,3499,3501,3503,3505,3507,3509,3511,3513,3515,3517,3519,3521,3523,3525,3527,3529,3531,3533,3535,3537,3539,3541,3543,3545,3547,3549,3551,3553,3555,3557,3559,3561,3563,3565,3567,3569,3571,3573,3575,3577,3579,3581,3583,3585,3587,3589,3591,3593,3595,3597,3599,3601,3603,3605,3607,3609,3611,3613,3615,3617,3619,3621,3623,3625,3627,3629,3631,3633,3635,3637,3639,3641,3643,3645,3647,3649,3651,3653,3655,3657,3659,3661,3663,3665,3667,3669,3671,3673,3675,3677,3679,3681,3683,3685,3687,3689,3691,3693,3695,3697,3699,3701,3703,3705,3707,3709,3711,3713,3715,3717,3719,3721,3723,3725,3727,3729,3731,3733,3735,3737,3739,3741,3743,3745,3747,3749,3751,3753,3755,3757,3759,3761,3763,3765,3767,3769,3771,3773,3775,3777,3779,3781,3783,3785,3787,3789,3791,3793,3795,3797,3799,3801,3803,3805,3807,3809,3811,3813,3815,3817,3819,3821,3823,3825,3827,3829,3831,3833,3835,3837,3839,3841,3843,3845,3847,3849,3851,3853,3855,3857,3859,3861,3863,3865,3867,3869,3871,3873,3875,3877,3879,3881,3883,3885,3887,3889,3891,3893,3895,3897,3899,3901,3903,3905,3907,3909,3911,3913,3915,3917,3919,3921,3923,3925,3927,3929,3931,3933,3935,3937,3939,3941,3943,3945,3947,3949,3951,3953,3955,3957,3959,3961,3963,3965,3967,3969,3971,3973,3975,3977,3979,3981,3983,3985,3987,3989,3991,3993,3995,3997,3999,4001,4003,4005,4007,4009,4011,4013,4015,4017,4019,4021,4023,4025,4027,4029,4031,4033,4035,4037,4039,4041,4043,4045,4047,4049,4051,4053,4055,4057,4059,4061,4063,4065,4067,4069,4071,4073,4075,4077,4079,4081,4083,4085,4087,4089,4091,4093,4095,4097,4099,4101,4103,4105,4107,4109,4111,4113,4115,4117,4119,4121,4123,4125,4127,4129,4131,4133,4135,4137,4139,4141,4143,4145,4147,4149,4151,4153,4155,4157,4159,4161,4163,4165,4167,4169,4171,4173,4175,4177,4179,4181,4183,4185,4187,4189,4191,4193,4195,4197,4199,4201,4203,4205,4207,4209,4211,4213,4215,4217,4219,4221,4223,4225,4227,4229,4231,4233,4235,4237,4239,4241,4243,4245,4247,4249,4251,4253,4255,4257,4259,4261,4263,4265,4267,4269,4271,4273,4275,4277,4279,4281,4283,4285,4287,4289,4291,4293,4295,4297,4299,4301,4303,4305,4307,4309,4311,4313,4315,4317,4319,4321,4323,4325,4327,4329,4331,4333,4335,4337,4339,4341,4343,4345,4347,4349,4351,4353,4355,4357,4359,4361,4363,4365,4367,4369,4371,4373,4375,4377,4379,4381,4383,4385,4387,4389,4391,4393,4395,4397,4399,4401,4403,4405,4407,4409,4411,4413,4415,4417,4419,4421,4423,4425,4427,4429,4431,4433,4435,4437,4439,4441,4443,4445,4447,4449,4451,4453,4455,4457,4459,4461,4463,4465,4467,4469,4471,4473,4475,4477,4479,4481,4483,4485,4487,4489,4491,4493,4495,4497,4499,4501,4503,4505,4507,4509,4511,4513],{"categories":112},[113],"Business & SaaS",{"categories":115},[113],{"categories":117},[118],"AI News & Trends",{"categories":120},[],{"categories":122},[123],"AI Automation",{"categories":125},[126],"Marketing & Growth",{"categories":128},[129],"Design & Frontend",{"categories":131},[132],"Software Engineering",{"categories":134},[123],{"categories":136},[],{"categories":138},[129],{"categories":140},[129],{"categories":142},[123],{"categories":144},[129],{"categories":146},[129],{"categories":148},[149],"AI & LLMs",{"categories":151},[129],{"categories":153},[129],{"categories":155},[],{"categories":157},[129],{"categories":159},[129],{"categories":161},[149],{"categories":163},[164],"Developer Productivity",{"categories":166},[149],{"categories":168},[149],{"categories":170},[149],{"categories":172},[118],{"categories":174},[149],{"categories":176},[123],{"categories":178},[113],{"categories":180},[118],{"categories":182},[126],{"categories":184},[],{"categories":186},[],{"categories":188},[123],{"categories":190},[123],{"categories":192},[123],{"categories":194},[126],{"categories":196},[149],{"categories":198},[164],{"categories":200},[118],{"categories":202},[],{"categories":204},[],{"categories":206},[],{"categories":208},[67],{"categories":210},[],{"categories":212},[123],{"categories":214},[132],{"categories":216},[123],{"categories":218},[123],{"categories":220},[149],{"categories":222},[126],{"categories":224},[123],{"categories":226},[],{"categories":228},[],{"categories":230},[],{"categories":232},[129],{"categories":234},[129],{"categories":236},[123],{"categories":238},[126],{"categories":240},[164],{"categories":242},[129],{"categories":244},[149],{"categories":246},[132],{"categories":248},[149],{"categories":250},[],{"categories":252},[123],{"categories":254},[149],{"categories":256},[164],{"categories":258},[164],{"categories":260},[],{"categories":262},[126],{"categories":264},[113],{"categories":266},[149],{"categories":268},[113],{"categories":270},[113],{"categories":272},[123],{"categories":274},[126],{"categories":276},[123],{"categories":278},[113],{"categories":280},[123],{"categories":282},[129],{"categories":284},[149],{"categories":286},[129],{"categories":288},[149],{"categories":290},[113],{"categories":292},[149],{"categories":294},[126],{"categories":296},[],{"categories":298},[149],{"categories":300},[113],{"categories":302},[],{"categories":304},[118],{"categories":306},[132],{"categories":308},[],{"categories":310},[149],{"categories":312},[129],{"categories":314},[149],{"categories":316},[129],{"categories":318},[],{"categories":320},[123],{"categories":322},[],{"categories":324},[],{"categories":326},[],{"categories":328},[149],{"categories":330},[],{"categories":332},[149],{"categories":334},[149],{"categories":336},[129],{"categories":338},[149],{"categories":340},[164],{"categories":342},[123],{"categories":344},[126],{"categories":346},[164],{"categories":348},[164],{"categories":350},[164],{"categories":352},[126],{"categories":354},[126],{"categories":356},[149],{"categories":358},[149],{"categories":360},[129],{"categories":362},[113],{"categories":364},[129],{"categories":366},[132],{"categories":368},[113],{"categories":370},[113],{"categories":372},[113],{"categories":374},[129],{"categories":376},[],{"categories":378},[],{"categories":380},[149],{"categories":382},[149],{"categories":384},[132],{"categories":386},[149],{"categories":388},[149],{"categories":390},[],{"categories":392},[149],{"categories":394},[149],{"categories":396},[],{"categories":398},[149],{"categories":400},[118],{"categories":402},[118],{"categories":404},[],{"categories":406},[],{"categories":408},[126],{"categories":410},[126],{"categories":412},[132],{"categories":414},[149],{"categories":416},[],{"categories":418},[],{"categories":420},[123],{"categories":422},[149],{"categories":424},[149],{"categories":426},[],{"categories":428},[149,113],{"categories":430},[149],{"categories":432},[],{"categories":434},[149],{"categories":436},[149],{"categories":438},[],{"categories":440},[],{"categories":442},[123],{"categories":444},[149],{"categories":446},[149],{"categories":448},[123],{"categories":450},[149],{"categories":452},[],{"categories":454},[],{"categories":456},[149],{"categories":458},[],{"categories":460},[149],{"categories":462},[149],{"categories":464},[],{"categories":466},[123],{"categories":468},[129],{"categories":470},[],{"categories":472},[123,473],"DevOps & Cloud",{"categories":475},[149],{"categories":477},[123],{"categories":479},[149],{"categories":481},[],{"categories":483},[],{"categories":485},[],{"categories":487},[],{"categories":489},[149],{"categories":491},[123],{"categories":493},[],{"categories":495},[123],{"categories":497},[],{"categories":499},[149],{"categories":501},[],{"categories":503},[],{"categories":505},[],{"categories":507},[],{"categories":509},[123],{"categories":511},[129],{"categories":513},[149],{"categories":515},[126],{"categories":517},[118],{"categories":519},[113],{"categories":521},[164],{"categories":523},[],{"categories":525},[123],{"categories":527},[123],{"categories":529},[149],{"categories":531},[],{"categories":533},[],{"categories":535},[],{"categories":537},[123],{"categories":539},[],{"categories":541},[123],{"categories":543},[123],{"categories":545},[118],{"categories":547},[123],{"categories":549},[149],{"categories":551},[],{"categories":553},[149],{"categories":555},[],{"categories":557},[118],{"categories":559},[123,560],"Product Strategy",{"categories":562},[132],{"categories":564},[473],{"categories":566},[560],{"categories":568},[149],{"categories":570},[123],{"categories":572},[],{"categories":574},[118],{"categories":576},[118],{"categories":578},[123],{"categories":580},[],{"categories":582},[123],{"categories":584},[149],{"categories":586},[149],{"categories":588},[164],{"categories":590},[149],{"categories":592},[],{"categories":594},[149,132],{"categories":596},[118],{"categories":598},[149],{"categories":600},[118],{"categories":602},[123],{"categories":604},[118],{"categories":606},[],{"categories":608},[132],{"categories":610},[113],{"categories":612},[],{"categories":614},[123],{"categories":616},[123],{"categories":618},[123],{"categories":620},[123],{"categories":622},[113],{"categories":624},[129],{"categories":626},[126],{"categories":628},[],{"categories":630},[123],{"categories":632},[],{"categories":634},[118],{"categories":636},[118],{"categories":638},[118],{"categories":640},[123],{"categories":642},[118],{"categories":644},[149],{"categories":646},[164],{"categories":648},[149],{"categories":650},[132],{"categories":652},[149,164],{"categories":654},[164],{"categories":656},[164],{"categories":658},[164],{"categories":660},[164],{"categories":662},[149],{"categories":664},[],{"categories":666},[],{"categories":668},[126],{"categories":670},[],{"categories":672},[149],{"categories":674},[164],{"categories":676},[149],{"categories":678},[129],{"categories":680},[132],{"categories":682},[],{"categories":684},[149],{"categories":686},[164],{"categories":688},[126],{"categories":690},[118],{"categories":692},[132],{"categories":694},[149],{"categories":696},[],{"categories":698},[132],{"categories":700},[129],{"categories":702},[113],{"categories":704},[113],{"categories":706},[],{"categories":708},[129],{"categories":710},[113],{"categories":712},[118],{"categories":714},[164],{"categories":716},[123],{"categories":718},[123],{"categories":720},[149],{"categories":722},[149],{"categories":724},[118],{"categories":726},[118],{"categories":728},[164],{"categories":730},[118],{"categories":732},[],{"categories":734},[560],{"categories":736},[123],{"categories":738},[118],{"categories":740},[118],{"categories":742},[118],{"categories":744},[149],{"categories":746},[123],{"categories":748},[123],{"categories":750},[113],{"categories":752},[113],{"categories":754},[149],{"categories":756},[118],{"categories":758},[],{"categories":760},[149],{"categories":762},[113],{"categories":764},[123],{"categories":766},[123],{"categories":768},[123],{"categories":770},[129],{"categories":772},[123],{"categories":774},[164],{"categories":776},[118],{"categories":778},[118],{"categories":780},[118],{"categories":782},[118],{"categories":784},[118],{"categories":786},[],{"categories":788},[],{"categories":790},[164],{"categories":792},[118],{"categories":794},[118],{"categories":796},[118],{"categories":798},[],{"categories":800},[149],{"categories":802},[],{"categories":804},[],{"categories":806},[129],{"categories":808},[113],{"categories":810},[],{"categories":812},[118],{"categories":814},[123],{"categories":816},[123],{"categories":818},[123],{"categories":820},[126],{"categories":822},[123],{"categories":824},[],{"categories":826},[118],{"categories":828},[118],{"categories":830},[149],{"categories":832},[],{"categories":834},[126],{"categories":836},[126],{"categories":838},[149],{"categories":840},[118],{"categories":842},[113],{"categories":844},[132],{"categories":846},[149],{"categories":848},[],{"categories":850},[149],{"categories":852},[149],{"categories":854},[132],{"categories":856},[149],{"categories":858},[149],{"categories":860},[149],{"categories":862},[126],{"categories":864},[118],{"categories":866},[149],{"categories":868},[149],{"categories":870},[118],{"categories":872},[123],{"categories":874},[164],{"categories":876},[113],{"categories":878},[149],{"categories":880},[164],{"categories":882},[164],{"categories":884},[],{"categories":886},[126],{"categories":888},[118],{"categories":890},[118],{"categories":892},[164],{"categories":894},[123],{"categories":896},[123],{"categories":898},[123],{"categories":900},[123],{"categories":902},[129],{"categories":904},[149],{"categories":906},[149],{"categories":908},[560],{"categories":910},[149],{"categories":912},[149],{"categories":914},[123],{"categories":916},[113],{"categories":918},[126],{"categories":920},[],{"categories":922},[113],{"categories":924},[113],{"categories":926},[],{"categories":928},[129],{"categories":930},[149],{"categories":932},[],{"categories":934},[],{"categories":936},[118],{"categories":938},[118],{"categories":940},[118],{"categories":942},[118],{"categories":944},[],{"categories":946},[118],{"categories":948},[149],{"categories":950},[149],{"categories":952},[],{"categories":954},[118],{"categories":956},[118],{"categories":958},[113],{"categories":960},[149],{"categories":962},[],{"categories":964},[],{"categories":966},[118],{"categories":968},[118],{"categories":970},[118],{"categories":972},[149],{"categories":974},[118],{"categories":976},[118],{"categories":978},[118],{"categories":980},[118],{"categories":982},[118],{"categories":984},[],{"categories":986},[123],{"categories":988},[149],{"categories":990},[126],{"categories":992},[113],{"categories":994},[123],{"categories":996},[149],{"categories":998},[],{"categories":1000},[126],{"categories":1002},[118],{"categories":1004},[118],{"categories":1006},[118],{"categories":1008},[118],{"categories":1010},[164],{"categories":1012},[132],{"categories":1014},[],{"categories":1016},[149],{"categories":1018},[123],{"categories":1020},[123],{"categories":1022},[123],{"categories":1024},[473],{"categories":1026},[123],{"categories":1028},[149],{"categories":1030},[149],{"categories":1032},[132],{"categories":1034},[473],{"categories":1036},[67],{"categories":1038},[149],{"categories":1040},[67],{"categories":1042},[],{"categories":1044},[126],{"categories":1046},[126],{"categories":1048},[129],{"categories":1050},[473],{"categories":1052},[123],{"categories":1054},[149],{"categories":1056},[149],{"categories":1058},[123],{"categories":1060},[123],{"categories":1062},[123],{"categories":1064},[164],{"categories":1066},[164],{"categories":1068},[123],{"categories":1070},[123],{"categories":1072},[],{"categories":1074},[123],{"categories":1076},[123],{"categories":1078},[149],{"categories":1080},[67],{"categories":1082},[123],{"categories":1084},[123],{"categories":1086},[123],{"categories":1088},[123],{"categories":1090},[113],{"categories":1092},[129],{"categories":1094},[118],{"categories":1096},[132],{"categories":1098},[473],{"categories":1100},[132],{"categories":1102},[67],{"categories":1104},[],{"categories":1106},[132],{"categories":1108},[],{"categories":1110},[],{"categories":1112},[132],{"categories":1114},[149],{"categories":1116},[],{"categories":1118},[],{"categories":1120},[],{"categories":1122},[113],{"categories":1124},[],{"categories":1126},[],{"categories":1128},[67],{"categories":1130},[149],{"categories":1132},[473],{"categories":1134},[149],{"categories":1136},[],{"categories":1138},[123],{"categories":1140},[164],{"categories":1142},[164],{"categories":1144},[126],{"categories":1146},[126],{"categories":1148},[126],{"categories":1150},[473],{"categories":1152},[132],{"categories":1154},[123],{"categories":1156},[113],{"categories":1158},[113],{"categories":1160},[132],{"categories":1162},[129],{"categories":1164},[67],{"categories":1166},[129],{"categories":1168},[],{"categories":1170},[149],{"categories":1172},[123],{"categories":1174},[123],{"categories":1176},[164],{"categories":1178},[123],{"categories":1180},[123],{"categories":1182},[129],{"categories":1184},[129],{"categories":1186},[123],{"categories":1188},[473],{"categories":1190},[149],{"categories":1192},[],{"categories":1194},[126],{"categories":1196},[123],{"categories":1198},[113],{"categories":1200},[123],{"categories":1202},[123],{"categories":1204},[],{"categories":1206},[149],{"categories":1208},[123],{"categories":1210},[123],{"categories":1212},[164],{"categories":1214},[123],{"categories":1216},[149],{"categories":1218},[],{"categories":1220},[123],{"categories":1222},[],{"categories":1224},[129],{"categories":1226},[164],{"categories":1228},[149],{"categories":1230},[132],{"categories":1232},[129],{"categories":1234},[164],{"categories":1236},[67],{"categories":1238},[164],{"categories":1240},[],{"categories":1242},[149],{"categories":1244},[149],{"categories":1246},[560],{"categories":1248},[132],{"categories":1250},[149,123],{"categories":1252},[123],{"categories":1254},[149],{"categories":1256},[123],{"categories":1258},[123,132],{"categories":1260},[123],{"categories":1262},[149],{"categories":1264},[],{"categories":1266},[164],{"categories":1268},[149],{"categories":1270},[123],{"categories":1272},[149],{"categories":1274},[],{"categories":1276},[132],{"categories":1278},[113],{"categories":1280},[123],{"categories":1282},[],{"categories":1284},[67],{"categories":1286},[132],{"categories":1288},[123],{"categories":1290},[132],{"categories":1292},[],{"categories":1294},[123],{"categories":1296},[],{"categories":1298},[123],{"categories":1300},[],{"categories":1302},[],{"categories":1304},[129],{"categories":1306},[164],{"categories":1308},[149],{"categories":1310},[123],{"categories":1312},[],{"categories":1314},[123],{"categories":1316},[132],{"categories":1318},[149],{"categories":1320},[149],{"categories":1322},[132],{"categories":1324},[132],{"categories":1326},[164],{"categories":1328},[113],{"categories":1330},[],{"categories":1332},[149],{"categories":1334},[149],{"categories":1336},[149],{"categories":1338},[123],{"categories":1340},[149],{"categories":1342},[],{"categories":1344},[129],{"categories":1346},[149],{"categories":1348},[123],{"categories":1350},[],{"categories":1352},[149],{"categories":1354},[],{"categories":1356},[149],{"categories":1358},[],{"categories":1360},[],{"categories":1362},[],{"categories":1364},[149],{"categories":1366},[149],{"categories":1368},[149],{"categories":1370},[149],{"categories":1372},[],{"categories":1374},[149],{"categories":1376},[149],{"categories":1378},[149],{"categories":1380},[],{"categories":1382},[149],{"categories":1384},[],{"categories":1386},[126],{"categories":1388},[149],{"categories":1390},[],{"categories":1392},[],{"categories":1394},[],{"categories":1396},[149],{"categories":1398},[118],{"categories":1400},[118],{"categories":1402},[],{"categories":1404},[123],{"categories":1406},[149],{"categories":1408},[],{"categories":1410},[149],{"categories":1412},[149],{"categories":1414},[118],{"categories":1416},[],{"categories":1418},[149],{"categories":1420},[118],{"categories":1422},[123],{"categories":1424},[149],{"categories":1426},[],{"categories":1428},[],{"categories":1430},[],{"categories":1432},[123],{"categories":1434},[123],{"categories":1436},[123],{"categories":1438},[123],{"categories":1440},[149],{"categories":1442},[129],{"categories":1444},[129],{"categories":1446},[123],{"categories":1448},[123],{"categories":1450},[164],{"categories":1452},[560],{"categories":1454},[164],{"categories":1456},[164],{"categories":1458},[149],{"categories":1460},[123],{"categories":1462},[149],{"categories":1464},[164],{"categories":1466},[149],{"categories":1468},[123],{"categories":1470},[123],{"categories":1472},[123],{"categories":1474},[123],{"categories":1476},[123],{"categories":1478},[149],{"categories":1480},[164],{"categories":1482},[164],{"categories":1484},[126],{"categories":1486},[123],{"categories":1488},[],{"categories":1490},[123],{"categories":1492},[],{"categories":1494},[118],{"categories":1496},[149],{"categories":1498},[],{"categories":1500},[113],{"categories":1502},[129],{"categories":1504},[129],{"categories":1506},[123],{"categories":1508},[123],{"categories":1510},[149],{"categories":1512},[149],{"categories":1514},[118],{"categories":1516},[118],{"categories":1518},[473],{"categories":1520},[123],{"categories":1522},[118],{"categories":1524},[],{"categories":1526},[149],{"categories":1528},[123],{"categories":1530},[123],{"categories":1532},[123],{"categories":1534},[123],{"categories":1536},[149],{"categories":1538},[149],{"categories":1540},[149],{"categories":1542},[149],{"categories":1544},[123],{"categories":1546},[123],{"categories":1548},[123],{"categories":1550},[123],{"categories":1552},[],{"categories":1554},[129],{"categories":1556},[149],{"categories":1558},[149],{"categories":1560},[149],{"categories":1562},[],{"categories":1564},[126],{"categories":1566},[],{"categories":1568},[164],{"categories":1570},[],{"categories":1572},[123],{"categories":1574},[164],{"categories":1576},[129],{"categories":1578},[164],{"categories":1580},[],{"categories":1582},[164],{"categories":1584},[164],{"categories":1586},[],{"categories":1588},[129],{"categories":1590},[123],{"categories":1592},[123],{"categories":1594},[164],{"categories":1596},[149],{"categories":1598},[149],{"categories":1600},[],{"categories":1602},[118],{"categories":1604},[],{"categories":1606},[126],{"categories":1608},[],{"categories":1610},[129],{"categories":1612},[118],{"categories":1614},[129],{"categories":1616},[129],{"categories":1618},[129],{"categories":1620},[129],{"categories":1622},[129],{"categories":1624},[129],{"categories":1626},[129],{"categories":1628},[129],{"categories":1630},[129],{"categories":1632},[129],{"categories":1634},[],{"categories":1636},[123],{"categories":1638},[129],{"categories":1640},[149],{"categories":1642},[149],{"categories":1644},[129],{"categories":1646},[129],{"categories":1648},[129],{"categories":1650},[129],{"categories":1652},[129],{"categories":1654},[129],{"categories":1656},[129],{"categories":1658},[149,129],{"categories":1660},[129],{"categories":1662},[129],{"categories":1664},[129],{"categories":1666},[129],{"categories":1668},[],{"categories":1670},[129],{"categories":1672},[129],{"categories":1674},[129],{"categories":1676},[129],{"categories":1678},[129],{"categories":1680},[129],{"categories":1682},[129],{"categories":1684},[129],{"categories":1686},[129],{"categories":1688},[129,149],{"categories":1690},[129],{"categories":1692},[129],{"categories":1694},[],{"categories":1696},[118],{"categories":1698},[],{"categories":1700},[149],{"categories":1702},[],{"categories":1704},[123],{"categories":1706},[473],{"categories":1708},[560],{"categories":1710},[123],{"categories":1712},[123],{"categories":1714},[],{"categories":1716},[123],{"categories":1718},[],{"categories":1720},[123],{"categories":1722},[],{"categories":1724},[],{"categories":1726},[149],{"categories":1728},[149],{"categories":1730},[149],{"categories":1732},[118],{"categories":1734},[118],{"categories":1736},[118],{"categories":1738},[118],{"categories":1740},[],{"categories":1742},[118],{"categories":1744},[],{"categories":1746},[118],{"categories":1748},[149],{"categories":1750},[118],{"categories":1752},[118],{"categories":1754},[118],{"categories":1756},[118],{"categories":1758},[149],{"categories":1760},[118],{"categories":1762},[123],{"categories":1764},[],{"categories":1766},[123],{"categories":1768},[118],{"categories":1770},[149],{"categories":1772},[118],{"categories":1774},[118],{"categories":1776},[118],{"categories":1778},[149],{"categories":1780},[149],{"categories":1782},[149],{"categories":1784},[],{"categories":1786},[],{"categories":1788},[149],{"categories":1790},[118],{"categories":1792},[],{"categories":1794},[149],{"categories":1796},[123],{"categories":1798},[149],{"categories":1800},[123],{"categories":1802},[123],{"categories":1804},[149],{"categories":1806},[],{"categories":1808},[],{"categories":1810},[123],{"categories":1812},[123],{"categories":1814},[123],{"categories":1816},[123],{"categories":1818},[123],{"categories":1820},[123],{"categories":1822},[123],{"categories":1824},[123],{"categories":1826},[],{"categories":1828},[123],{"categories":1830},[123],{"categories":1832},[123],{"categories":1834},[149],{"categories":1836},[149],{"categories":1838},[149],{"categories":1840},[118],{"categories":1842},[149],{"categories":1844},[149],{"categories":1846},[149],{"categories":1848},[123],{"categories":1850},[126],{"categories":1852},[126],{"categories":1854},[126],{"categories":1856},[123],{"categories":1858},[],{"categories":1860},[149],{"categories":1862},[],{"categories":1864},[],{"categories":1866},[149],{"categories":1868},[],{"categories":1870},[123],{"categories":1872},[129],{"categories":1874},[164],{"categories":1876},[67],{"categories":1878},[149],{"categories":1880},[123],{"categories":1882},[129],{"categories":1884},[],{"categories":1886},[123],{"categories":1888},[126,113],{"categories":1890},[123],{"categories":1892},[123],{"categories":1894},[473],{"categories":1896},[132],{"categories":1898},[126],{"categories":1900},[164],{"categories":1902},[149],{"categories":1904},[],{"categories":1906},[149],{"categories":1908},[],{"categories":1910},[149],{"categories":1912},[149],{"categories":1914},[123],{"categories":1916},[],{"categories":1918},[149],{"categories":1920},[123],{"categories":1922},[149],{"categories":1924},[164],{"categories":1926},[123],{"categories":1928},[149],{"categories":1930},[149,164],{"categories":1932},[164],{"categories":1934},[],{"categories":1936},[149],{"categories":1938},[149],{"categories":1940},[149],{"categories":1942},[],{"categories":1944},[],{"categories":1946},[123],{"categories":1948},[126],{"categories":1950},[118],{"categories":1952},[123],{"categories":1954},[149],{"categories":1956},[118],{"categories":1958},[],{"categories":1960},[164],{"categories":1962},[118],{"categories":1964},[],{"categories":1966},[67],{"categories":1968},[126],{"categories":1970},[113],{"categories":1972},[118],{"categories":1974},[149],{"categories":1976},[123],{"categories":1978},[149],{"categories":1980},[123],{"categories":1982},[123],{"categories":1984},[118],{"categories":1986},[164],{"categories":1988},[129],{"categories":1990},[113],{"categories":1992},[149],{"categories":1994},[149],{"categories":1996},[],{"categories":1998},[],{"categories":2000},[149],{"categories":2002},[],{"categories":2004},[149],{"categories":2006},[118],{"categories":2008},[],{"categories":2010},[123],{"categories":2012},[164],{"categories":2014},[118],{"categories":2016},[164],{"categories":2018},[123],{"categories":2020},[149],{"categories":2022},[],{"categories":2024},[123],{"categories":2026},[123],{"categories":2028},[129],{"categories":2030},[123],{"categories":2032},[129],{"categories":2034},[123],{"categories":2036},[123],{"categories":2038},[129],{"categories":2040},[],{"categories":2042},[],{"categories":2044},[129],{"categories":2046},[129],{"categories":2048},[129],{"categories":2050},[132],{"categories":2052},[164],{"categories":2054},[164],{"categories":2056},[123],{"categories":2058},[118],{"categories":2060},[164],{"categories":2062},[164],{"categories":2064},[126],{"categories":2066},[129],{"categories":2068},[123],{"categories":2070},[123],{"categories":2072},[149],{"categories":2074},[164],{"categories":2076},[149],{"categories":2078},[],{"categories":2080},[473],{"categories":2082},[560],{"categories":2084},[],{"categories":2086},[],{"categories":2088},[123],{"categories":2090},[118],{"categories":2092},[126],{"categories":2094},[126],{"categories":2096},[67],{"categories":2098},[129],{"categories":2100},[67],{"categories":2102},[67],{"categories":2104},[123],{"categories":2106},[],{"categories":2108},[],{"categories":2110},[67],{"categories":2112},[132],{"categories":2114},[149],{"categories":2116},[132],{"categories":2118},[67],{"categories":2120},[132],{"categories":2122},[67],{"categories":2124},[113],{"categories":2126},[132],{"categories":2128},[164],{"categories":2130},[149],{"categories":2132},[],{"categories":2134},[67],{"categories":2136},[473],{"categories":2138},[],{"categories":2140},[149],{"categories":2142},[149],{"categories":2144},[],{"categories":2146},[],{"categories":2148},[149],{"categories":2150},[149],{"categories":2152},[118],{"categories":2154},[149],{"categories":2156},[],{"categories":2158},[118],{"categories":2160},[],{"categories":2162},[],{"categories":2164},[118],{"categories":2166},[118],{"categories":2168},[149],{"categories":2170},[149],{"categories":2172},[149],{"categories":2174},[149],{"categories":2176},[149],{"categories":2178},[149],{"categories":2180},[126],{"categories":2182},[],{"categories":2184},[149],{"categories":2186},[],{"categories":2188},[],{"categories":2190},[123],{"categories":2192},[164],{"categories":2194},[],{"categories":2196},[473],{"categories":2198},[149,473],{"categories":2200},[149],{"categories":2202},[],{"categories":2204},[129],{"categories":2206},[129],{"categories":2208},[129],{"categories":2210},[129],{"categories":2212},[129],{"categories":2214},[],{"categories":2216},[],{"categories":2218},[],{"categories":2220},[132],{"categories":2222},[123],{"categories":2224},[113],{"categories":2226},[132],{"categories":2228},[164],{"categories":2230},[129],{"categories":2232},[],{"categories":2234},[126],{"categories":2236},[560],{"categories":2238},[67],{"categories":2240},[67],{"categories":2242},[67],{"categories":2244},[164],{"categories":2246},[560],{"categories":2248},[164],{"categories":2250},[],{"categories":2252},[113],{"categories":2254},[132],{"categories":2256},[149],{"categories":2258},[129],{"categories":2260},[126],{"categories":2262},[132],{"categories":2264},[126],{"categories":2266},[149],{"categories":2268},[129],{"categories":2270},[132],{"categories":2272},[473],{"categories":2274},[149],{"categories":2276},[118],{"categories":2278},[132],{"categories":2280},[],{"categories":2282},[149],{"categories":2284},[132],{"categories":2286},[132],{"categories":2288},[123],{"categories":2290},[],{"categories":2292},[126],{"categories":2294},[126],{"categories":2296},[126],{"categories":2298},[123],{"categories":2300},[149],{"categories":2302},[],{"categories":2304},[113],{"categories":2306},[164],{"categories":2308},[164],{"categories":2310},[67],{"categories":2312},[113],{"categories":2314},[118],{"categories":2316},[67],{"categories":2318},[],{"categories":2320},[118],{"categories":2322},[118],{"categories":2324},[118],{"categories":2326},[149],{"categories":2328},[113],{"categories":2330},[149],{"categories":2332},[],{"categories":2334},[],{"categories":2336},[],{"categories":2338},[132],{"categories":2340},[123],{"categories":2342},[],{"categories":2344},[164],{"categories":2346},[129],{"categories":2348},[],{"categories":2350},[126],{"categories":2352},[],{"categories":2354},[129],{"categories":2356},[149],{"categories":2358},[164],{"categories":2360},[113],{"categories":2362},[],{"categories":2364},[129],{"categories":2366},[129],{"categories":2368},[149],{"categories":2370},[],{"categories":2372},[],{"categories":2374},[132],{"categories":2376},[149],{"categories":2378},[],{"categories":2380},[123],{"categories":2382},[149],{"categories":2384},[],{"categories":2386},[132],{"categories":2388},[123],{"categories":2390},[149],{"categories":2392},[67],{"categories":2394},[149],{"categories":2396},[],{"categories":2398},[67],{"categories":2400},[149],{"categories":2402},[132],{"categories":2404},[149],{"categories":2406},[67],{"categories":2408},[123],{"categories":2410},[149],{"categories":2412},[149],{"categories":2414},[149,123],{"categories":2416},[123],{"categories":2418},[123],{"categories":2420},[123],{"categories":2422},[129],{"categories":2424},[164],{"categories":2426},[149],{"categories":2428},[164],{"categories":2430},[129],{"categories":2432},[149],{"categories":2434},[],{"categories":2436},[],{"categories":2438},[149],{"categories":2440},[149],{"categories":2442},[149],{"categories":2444},[123],{"categories":2446},[149],{"categories":2448},[],{"categories":2450},[149],{"categories":2452},[149],{"categories":2454},[123],{"categories":2456},[123],{"categories":2458},[149],{"categories":2460},[149],{"categories":2462},[],{"categories":2464},[149],{"categories":2466},[],{"categories":2468},[149],{"categories":2470},[149],{"categories":2472},[149],{"categories":2474},[149],{"categories":2476},[149],{"categories":2478},[149],{"categories":2480},[149],{"categories":2482},[],{"categories":2484},[149],{"categories":2486},[118],{"categories":2488},[118],{"categories":2490},[],{"categories":2492},[],{"categories":2494},[149],{"categories":2496},[],{"categories":2498},[149],{"categories":2500},[149,473],{"categories":2502},[],{"categories":2504},[118],{"categories":2506},[],{"categories":2508},[149],{"categories":2510},[],{"categories":2512},[],{"categories":2514},[],{"categories":2516},[149],{"categories":2518},[],{"categories":2520},[149],{"categories":2522},[],{"categories":2524},[149],{"categories":2526},[149],{"categories":2528},[],{"categories":2530},[],{"categories":2532},[149,473],{"categories":2534},[473,149],{"categories":2536},[118],{"categories":2538},[],{"categories":2540},[149],{"categories":2542},[],{"categories":2544},[149],{"categories":2546},[149],{"categories":2548},[],{"categories":2550},[118],{"categories":2552},[149,113],{"categories":2554},[118],{"categories":2556},[132],{"categories":2558},[],{"categories":2560},[123],{"categories":2562},[149],{"categories":2564},[126],{"categories":2566},[149],{"categories":2568},[164],{"categories":2570},[164],{"categories":2572},[473],{"categories":2574},[118],{"categories":2576},[149],{"categories":2578},[473],{"categories":2580},[132],{"categories":2582},[149],{"categories":2584},[164],{"categories":2586},[],{"categories":2588},[149],{"categories":2590},[],{"categories":2592},[],{"categories":2594},[149],{"categories":2596},[],{"categories":2598},[149],{"categories":2600},[132],{"categories":2602},[113],{"categories":2604},[164],{"categories":2606},[126],{"categories":2608},[123],{"categories":2610},[164],{"categories":2612},[],{"categories":2614},[126],{"categories":2616},[],{"categories":2618},[],{"categories":2620},[149],{"categories":2622},[118],{"categories":2624},[126],{"categories":2626},[],{"categories":2628},[149],{"categories":2630},[118],{"categories":2632},[118],{"categories":2634},[126],{"categories":2636},[118],{"categories":2638},[149],{"categories":2640},[118],{"categories":2642},[149],{"categories":2644},[],{"categories":2646},[149],{"categories":2648},[149],{"categories":2650},[149],{"categories":2652},[118],{"categories":2654},[],{"categories":2656},[],{"categories":2658},[129],{"categories":2660},[118],{"categories":2662},[],{"categories":2664},[149],{"categories":2666},[149],{"categories":2668},[149],{"categories":2670},[149],{"categories":2672},[149],{"categories":2674},[149],{"categories":2676},[149],{"categories":2678},[149],{"categories":2680},[149],{"categories":2682},[126],{"categories":2684},[149,129],{"categories":2686},[118],{"categories":2688},[118],{"categories":2690},[149],{"categories":2692},[132],{"categories":2694},[67],{"categories":2696},[149],{"categories":2698},[149],{"categories":2700},[],{"categories":2702},[],{"categories":2704},[149],{"categories":2706},[149],{"categories":2708},[],{"categories":2710},[129],{"categories":2712},[129],{"categories":2714},[164],{"categories":2716},[149],{"categories":2718},[164],{"categories":2720},[149],{"categories":2722},[149],{"categories":2724},[],{"categories":2726},[149],{"categories":2728},[],{"categories":2730},[],{"categories":2732},[149],{"categories":2734},[],{"categories":2736},[],{"categories":2738},[118],{"categories":2740},[],{"categories":2742},[149],{"categories":2744},[149],{"categories":2746},[149],{"categories":2748},[],{"categories":2750},[149],{"categories":2752},[118],{"categories":2754},[560],{"categories":2756},[123],{"categories":2758},[149],{"categories":2760},[],{"categories":2762},[123],{"categories":2764},[149],{"categories":2766},[],{"categories":2768},[149],{"categories":2770},[],{"categories":2772},[123],{"categories":2774},[],{"categories":2776},[],{"categories":2778},[123],{"categories":2780},[123],{"categories":2782},[123],{"categories":2784},[149],{"categories":2786},[],{"categories":2788},[123],{"categories":2790},[123],{"categories":2792},[],{"categories":2794},[],{"categories":2796},[123],{"categories":2798},[149],{"categories":2800},[118],{"categories":2802},[560],{"categories":2804},[126],{"categories":2806},[],{"categories":2808},[129],{"categories":2810},[149],{"categories":2812},[149],{"categories":2814},[113],{"categories":2816},[118],{"categories":2818},[118],{"categories":2820},[118],{"categories":2822},[118],{"categories":2824},[],{"categories":2826},[123],{"categories":2828},[123],{"categories":2830},[123],{"categories":2832},[123],{"categories":2834},[164],{"categories":2836},[149],{"categories":2838},[113],{"categories":2840},[],{"categories":2842},[164],{"categories":2844},[123],{"categories":2846},[129],{"categories":2848},[129],{"categories":2850},[129],{"categories":2852},[129],{"categories":2854},[129],{"categories":2856},[129],{"categories":2858},[149,113],{"categories":2860},[123],{"categories":2862},[113],{"categories":2864},[118],{"categories":2866},[118],{"categories":2868},[164],{"categories":2870},[],{"categories":2872},[],{"categories":2874},[126],{"categories":2876},[],{"categories":2878},[149],{"categories":2880},[126],{"categories":2882},[149],{"categories":2884},[132],{"categories":2886},[123],{"categories":2888},[113],{"categories":2890},[123],{"categories":2892},[132],{"categories":2894},[164],{"categories":2896},[123],{"categories":2898},[],{"categories":2900},[164],{"categories":2902},[],{"categories":2904},[],{"categories":2906},[123],{"categories":2908},[123],{"categories":2910},[123],{"categories":2912},[149],{"categories":2914},[149],{"categories":2916},[149],{"categories":2918},[149],{"categories":2920},[149],{"categories":2922},[],{"categories":2924},[473],{"categories":2926},[149],{"categories":2928},[],{"categories":2930},[],{"categories":2932},[],{"categories":2934},[164],{"categories":2936},[],{"categories":2938},[149],{"categories":2940},[],{"categories":2942},[118],{"categories":2944},[149],{"categories":2946},[118],{"categories":2948},[149],{"categories":2950},[123],{"categories":2952},[],{"categories":2954},[149],{"categories":2956},[149],{"categories":2958},[],{"categories":2960},[67],{"categories":2962},[67],{"categories":2964},[132],{"categories":2966},[129],{"categories":2968},[],{"categories":2970},[149],{"categories":2972},[123],{"categories":2974},[],{"categories":2976},[],{"categories":2978},[149],{"categories":2980},[132],{"categories":2982},[123],{"categories":2984},[113],{"categories":2986},[164,132],{"categories":2988},[132],{"categories":2990},[149],{"categories":2992},[123],{"categories":2994},[],{"categories":2996},[],{"categories":2998},[],{"categories":3000},[],{"categories":3002},[],{"categories":3004},[],{"categories":3006},[149],{"categories":3008},[],{"categories":3010},[],{"categories":3012},[149],{"categories":3014},[],{"categories":3016},[],{"categories":3018},[],{"categories":3020},[149],{"categories":3022},[118],{"categories":3024},[],{"categories":3026},[],{"categories":3028},[],{"categories":3030},[149],{"categories":3032},[],{"categories":3034},[149],{"categories":3036},[149],{"categories":3038},[],{"categories":3040},[149],{"categories":3042},[132],{"categories":3044},[],{"categories":3046},[164],{"categories":3048},[164],{"categories":3050},[],{"categories":3052},[126],{"categories":3054},[],{"categories":3056},[],{"categories":3058},[],{"categories":3060},[129],{"categories":3062},[118],{"categories":3064},[123],{"categories":3066},[149],{"categories":3068},[113],{"categories":3070},[149],{"categories":3072},[],{"categories":3074},[],{"categories":3076},[113],{"categories":3078},[126],{"categories":3080},[123],{"categories":3082},[],{"categories":3084},[473],{"categories":3086},[],{"categories":3088},[126],{"categories":3090},[149],{"categories":3092},[149],{"categories":3094},[126],{"categories":3096},[149],{"categories":3098},[129],{"categories":3100},[123],{"categories":3102},[149],{"categories":3104},[123],{"categories":3106},[149],{"categories":3108},[123],{"categories":3110},[164],{"categories":3112},[164],{"categories":3114},[129],{"categories":3116},[],{"categories":3118},[149],{"categories":3120},[149],{"categories":3122},[126],{"categories":3124},[560],{"categories":3126},[164],{"categories":3128},[118],{"categories":3130},[149],{"categories":3132},[118],{"categories":3134},[149],{"categories":3136},[149],{"categories":3138},[],{"categories":3140},[149],{"categories":3142},[],{"categories":3144},[149],{"categories":3146},[126],{"categories":3148},[149],{"categories":3150},[149],{"categories":3152},[149],{"categories":3154},[],{"categories":3156},[149],{"categories":3158},[149],{"categories":3160},[560],{"categories":3162},[],{"categories":3164},[118],{"categories":3166},[473],{"categories":3168},[132],{"categories":3170},[],{"categories":3172},[67],{"categories":3174},[],{"categories":3176},[],{"categories":3178},[118],{"categories":3180},[149],{"categories":3182},[],{"categories":3184},[149],{"categories":3186},[149],{"categories":3188},[123],{"categories":3190},[149],{"categories":3192},[118],{"categories":3194},[118],{"categories":3196},[129],{"categories":3198},[129],{"categories":3200},[129],{"categories":3202},[149],{"categories":3204},[67],{"categories":3206},[118],{"categories":3208},[164],{"categories":3210},[],{"categories":3212},[129],{"categories":3214},[129],{"categories":3216},[473],{"categories":3218},[129],{"categories":3220},[129],{"categories":3222},[123],{"categories":3224},[118],{"categories":3226},[473],{"categories":3228},[149],{"categories":3230},[149],{"categories":3232},[149],{"categories":3234},[149],{"categories":3236},[],{"categories":3238},[123],{"categories":3240},[149],{"categories":3242},[129],{"categories":3244},[],{"categories":3246},[],{"categories":3248},[118],{"categories":3250},[],{"categories":3252},[123],{"categories":3254},[123],{"categories":3256},[123],{"categories":3258},[123],{"categories":3260},[123],{"categories":3262},[123],{"categories":3264},[123],{"categories":3266},[123],{"categories":3268},[],{"categories":3270},[],{"categories":3272},[149],{"categories":3274},[],{"categories":3276},[123],{"categories":3278},[164],{"categories":3280},[164],{"categories":3282},[67],{"categories":3284},[113],{"categories":3286},[],{"categories":3288},[],{"categories":3290},[],{"categories":3292},[129],{"categories":3294},[149],{"categories":3296},[],{"categories":3298},[113],{"categories":3300},[113],{"categories":3302},[129],{"categories":3304},[164],{"categories":3306},[67],{"categories":3308},[129],{"categories":3310},[129],{"categories":3312},[],{"categories":3314},[123],{"categories":3316},[113],{"categories":3318},[113],{"categories":3320},[149],{"categories":3322},[123],{"categories":3324},[132],{"categories":3326},[129],{"categories":3328},[],{"categories":3330},[126],{"categories":3332},[67],{"categories":3334},[118],{"categories":3336},[118],{"categories":3338},[118],{"categories":3340},[473],{"categories":3342},[],{"categories":3344},[123],{"categories":3346},[],{"categories":3348},[123],{"categories":3350},[123],{"categories":3352},[149],{"categories":3354},[149],{"categories":3356},[132],{"categories":3358},[123],{"categories":3360},[132],{"categories":3362},[],{"categories":3364},[123],{"categories":3366},[129],{"categories":3368},[129],{"categories":3370},[129],{"categories":3372},[149],{"categories":3374},[123],{"categories":3376},[149],{"categories":3378},[113],{"categories":3380},[118],{"categories":3382},[129],{"categories":3384},[118],{"categories":3386},[149],{"categories":3388},[],{"categories":3390},[118],{"categories":3392},[123],{"categories":3394},[118],{"categories":3396},[118],{"categories":3398},[118],{"categories":3400},[118],{"categories":3402},[],{"categories":3404},[],{"categories":3406},[118],{"categories":3408},[118],{"categories":3410},[],{"categories":3412},[118],{"categories":3414},[118],{"categories":3416},[149],{"categories":3418},[149],{"categories":3420},[118],{"categories":3422},[118],{"categories":3424},[149],{"categories":3426},[],{"categories":3428},[149],{"categories":3430},[123],{"categories":3432},[149],{"categories":3434},[149],{"categories":3436},[],{"categories":3438},[149],{"categories":3440},[149],{"categories":3442},[149],{"categories":3444},[118],{"categories":3446},[],{"categories":3448},[],{"categories":3450},[],{"categories":3452},[],{"categories":3454},[149],{"categories":3456},[149],{"categories":3458},[],{"categories":3460},[126],{"categories":3462},[118],{"categories":3464},[],{"categories":3466},[],{"categories":3468},[],{"categories":3470},[],{"categories":3472},[],{"categories":3474},[149],{"categories":3476},[],{"categories":3478},[],{"categories":3480},[149],{"categories":3482},[],{"categories":3484},[123],{"categories":3486},[123],{"categories":3488},[123],{"categories":3490},[113],{"categories":3492},[],{"categories":3494},[126],{"categories":3496},[132],{"categories":3498},[132],{"categories":3500},[473],{"categories":3502},[118],{"categories":3504},[],{"categories":3506},[149],{"categories":3508},[149],{"categories":3510},[113],{"categories":3512},[],{"categories":3514},[113],{"categories":3516},[],{"categories":3518},[],{"categories":3520},[],{"categories":3522},[132],{"categories":3524},[123],{"categories":3526},[123],{"categories":3528},[123],{"categories":3530},[123],{"categories":3532},[123],{"categories":3534},[],{"categories":3536},[118],{"categories":3538},[149],{"categories":3540},[149],{"categories":3542},[149],{"categories":3544},[],{"categories":3546},[113],{"categories":3548},[],{"categories":3550},[129],{"categories":3552},[67],{"categories":3554},[129],{"categories":3556},[],{"categories":3558},[],{"categories":3560},[149],{"categories":3562},[123],{"categories":3564},[],{"categories":3566},[149],{"categories":3568},[149],{"categories":3570},[149],{"categories":3572},[123],{"categories":3574},[123],{"categories":3576},[149],{"categories":3578},[67],{"categories":3580},[123],{"categories":3582},[],{"categories":3584},[149],{"categories":3586},[],{"categories":3588},[560],{"categories":3590},[132],{"categories":3592},[67],{"categories":3594},[132],{"categories":3596},[473],{"categories":3598},[149],{"categories":3600},[132],{"categories":3602},[118],{"categories":3604},[473],{"categories":3606},[132],{"categories":3608},[129],{"categories":3610},[129],{"categories":3612},[],{"categories":3614},[132],{"categories":3616},[],{"categories":3618},[164],{"categories":3620},[132],{"categories":3622},[],{"categories":3624},[67],{"categories":3626},[67],{"categories":3628},[560],{"categories":3630},[],{"categories":3632},[149],{"categories":3634},[132],{"categories":3636},[473],{"categories":3638},[123],{"categories":3640},[123],{"categories":3642},[67],{"categories":3644},[149],{"categories":3646},[164],{"categories":3648},[149],{"categories":3650},[],{"categories":3652},[],{"categories":3654},[],{"categories":3656},[126],{"categories":3658},[149],{"categories":3660},[129],{"categories":3662},[132],{"categories":3664},[132],{"categories":3666},[149],{"categories":3668},[126],{"categories":3670},[164],{"categories":3672},[149],{"categories":3674},[132],{"categories":3676},[149],{"categories":3678},[132],{"categories":3680},[164],{"categories":3682},[164],{"categories":3684},[123],{"categories":3686},[164],{"categories":3688},[132],{"categories":3690},[113],{"categories":3692},[132],{"categories":3694},[132],{"categories":3696},[132],{"categories":3698},[132],{"categories":3700},[],{"categories":3702},[118],{"categories":3704},[],{"categories":3706},[67],{"categories":3708},[149],{"categories":3710},[149],{"categories":3712},[],{"categories":3714},[],{"categories":3716},[],{"categories":3718},[149],{"categories":3720},[118],{"categories":3722},[149],{"categories":3724},[149],{"categories":3726},[],{"categories":3728},[149],{"categories":3730},[129],{"categories":3732},[149],{"categories":3734},[149],{"categories":3736},[149],{"categories":3738},[],{"categories":3740},[],{"categories":3742},[],{"categories":3744},[473],{"categories":3746},[473],{"categories":3748},[113],{"categories":3750},[123],{"categories":3752},[113,126],{"categories":3754},[149],{"categories":3756},[118],{"categories":3758},[],{"categories":3760},[129],{"categories":3762},[67],{"categories":3764},[149],{"categories":3766},[132],{"categories":3768},[149],{"categories":3770},[],{"categories":3772},[67],{"categories":3774},[473],{"categories":3776},[123],{"categories":3778},[113],{"categories":3780},[473],{"categories":3782},[123],{"categories":3784},[164],{"categories":3786},[123],{"categories":3788},[164],{"categories":3790},[149],{"categories":3792},[164],{"categories":3794},[164],{"categories":3796},[132],{"categories":3798},[67],{"categories":3800},[149],{"categories":3802},[126],{"categories":3804},[],{"categories":3806},[149],{"categories":3808},[129],{"categories":3810},[67],{"categories":3812},[113],{"categories":3814},[149],{"categories":3816},[67],{"categories":3818},[164],{"categories":3820},[149],{"categories":3822},[149],{"categories":3824},[67],{"categories":3826},[149],{"categories":3828},[164],{"categories":3830},[149],{"categories":3832},[],{"categories":3834},[149],{"categories":3836},[149],{"categories":3838},[149],{"categories":3840},[149],{"categories":3842},[],{"categories":3844},[123],{"categories":3846},[473],{"categories":3848},[],{"categories":3850},[],{"categories":3852},[149],{"categories":3854},[113],{"categories":3856},[126],{"categories":3858},[113],{"categories":3860},[113],{"categories":3862},[123],{"categories":3864},[],{"categories":3866},[149],{"categories":3868},[118],{"categories":3870},[149],{"categories":3872},[149],{"categories":3874},[],{"categories":3876},[123],{"categories":3878},[118],{"categories":3880},[149,473],{"categories":3882},[123,473],{"categories":3884},[473],{"categories":3886},[149],{"categories":3888},[123],{"categories":3890},[123],{"categories":3892},[132],{"categories":3894},[132],{"categories":3896},[132],{"categories":3898},[149],{"categories":3900},[129],{"categories":3902},[123],{"categories":3904},[],{"categories":3906},[473],{"categories":3908},[],{"categories":3910},[473],{"categories":3912},[473],{"categories":3914},[113],{"categories":3916},[123],{"categories":3918},[],{"categories":3920},[473],{"categories":3922},[149],{"categories":3924},[118],{"categories":3926},[149],{"categories":3928},[129],{"categories":3930},[132],{"categories":3932},[132],{"categories":3934},[132],{"categories":3936},[473],{"categories":3938},[],{"categories":3940},[],{"categories":3942},[],{"categories":3944},[149],{"categories":3946},[132],{"categories":3948},[149],{"categories":3950},[132],{"categories":3952},[473],{"categories":3954},[473],{"categories":3956},[149],{"categories":3958},[123],{"categories":3960},[],{"categories":3962},[149],{"categories":3964},[149],{"categories":3966},[149],{"categories":3968},[],{"categories":3970},[],{"categories":3972},[473],{"categories":3974},[473],{"categories":3976},[149,473],{"categories":3978},[123],{"categories":3980},[123],{"categories":3982},[123],{"categories":3984},[123],{"categories":3986},[123],{"categories":3988},[123],{"categories":3990},[],{"categories":3992},[132],{"categories":3994},[149],{"categories":3996},[132],{"categories":3998},[126],{"categories":4000},[149],{"categories":4002},[560],{"categories":4004},[560],{"categories":4006},[123],{"categories":4008},[132],{"categories":4010},[],{"categories":4012},[123],{"categories":4014},[149],{"categories":4016},[],{"categories":4018},[129],{"categories":4020},[],{"categories":4022},[149],{"categories":4024},[123],{"categories":4026},[118],{"categories":4028},[149],{"categories":4030},[],{"categories":4032},[],{"categories":4034},[129],{"categories":4036},[129],{"categories":4038},[164],{"categories":4040},[129],{"categories":4042},[123],{"categories":4044},[],{"categories":4046},[123],{"categories":4048},[118],{"categories":4050},[149],{"categories":4052},[149],{"categories":4054},[],{"categories":4056},[149],{"categories":4058},[164],{"categories":4060},[149],{"categories":4062},[],{"categories":4064},[67],{"categories":4066},[132],{"categories":4068},[132],{"categories":4070},[113],{"categories":4072},[113],{"categories":4074},[113],{"categories":4076},[123],{"categories":4078},[113],{"categories":4080},[123],{"categories":4082},[473],{"categories":4084},[560],{"categories":4086},[118],{"categories":4088},[118],{"categories":4090},[118],{"categories":4092},[473],{"categories":4094},[118,113],{"categories":4096},[67],{"categories":4098},[123],{"categories":4100},[],{"categories":4102},[149],{"categories":4104},[],{"categories":4106},[132],{"categories":4108},[67],{"categories":4110},[129],{"categories":4112},[132],{"categories":4114},[164],{"categories":4116},[],{"categories":4118},[123],{"categories":4120},[],{"categories":4122},[560],{"categories":4124},[],{"categories":4126},[129],{"categories":4128},[129],{"categories":4130},[67],{"categories":4132},[],{"categories":4134},[149],{"categories":4136},[67],{"categories":4138},[],{"categories":4140},[149],{"categories":4142},[149],{"categories":4144},[],{"categories":4146},[164],{"categories":4148},[149],{"categories":4150},[],{"categories":4152},[149],{"categories":4154},[],{"categories":4156},[],{"categories":4158},[123],{"categories":4160},[123],{"categories":4162},[],{"categories":4164},[132],{"categories":4166},[132],{"categories":4168},[132],{"categories":4170},[149,123],{"categories":4172},[123],{"categories":4174},[123],{"categories":4176},[123],{"categories":4178},[67],{"categories":4180},[67],{"categories":4182},[],{"categories":4184},[118],{"categories":4186},[149],{"categories":4188},[67],{"categories":4190},[67],{"categories":4192},[118],{"categories":4194},[113],{"categories":4196},[123],{"categories":4198},[132],{"categories":4200},[149],{"categories":4202},[149],{"categories":4204},[123],{"categories":4206},[132],{"categories":4208},[123],{"categories":4210},[149],{"categories":4212},[126],{"categories":4214},[],{"categories":4216},[149],{"categories":4218},[],{"categories":4220},[149],{"categories":4222},[149],{"categories":4224},[132],{"categories":4226},[],{"categories":4228},[67],{"categories":4230},[149],{"categories":4232},[123],{"categories":4234},[123],{"categories":4236},[132],{"categories":4238},[164],{"categories":4240},[164],{"categories":4242},[118],{"categories":4244},[149],{"categories":4246},[123],{"categories":4248},[],{"categories":4250},[123],{"categories":4252},[149],{"categories":4254},[118],{"categories":4256},[149],{"categories":4258},[149],{"categories":4260},[149],{"categories":4262},[123],{"categories":4264},[67],{"categories":4266},[149],{"categories":4268},[129],{"categories":4270},[149],{"categories":4272},[149],{"categories":4274},[149],{"categories":4276},[149],{"categories":4278},[],{"categories":4280},[149],{"categories":4282},[67],{"categories":4284},[129],{"categories":4286},[149],{"categories":4288},[129],{"categories":4290},[],{"categories":4292},[],{"categories":4294},[],{"categories":4296},[149],{"categories":4298},[],{"categories":4300},[],{"categories":4302},[],{"categories":4304},[],{"categories":4306},[123],{"categories":4308},[164],{"categories":4310},[123],{"categories":4312},[123],{"categories":4314},[132],{"categories":4316},[113],{"categories":4318},[149],{"categories":4320},[149],{"categories":4322},[149],{"categories":4324},[113],{"categories":4326},[164],{"categories":4328},[],{"categories":4330},[67],{"categories":4332},[126],{"categories":4334},[149],{"categories":4336},[129],{"categories":4338},[164],{"categories":4340},[164],{"categories":4342},[560],{"categories":4344},[123],{"categories":4346},[149],{"categories":4348},[149],{"categories":4350},[164],{"categories":4352},[149],{"categories":4354},[],{"categories":4356},[],{"categories":4358},[473],{"categories":4360},[129],{"categories":4362},[164],{"categories":4364},[149],{"categories":4366},[118],{"categories":4368},[164],{"categories":4370},[113],{"categories":4372},[123],{"categories":4374},[123],{"categories":4376},[118],{"categories":4378},[149],{"categories":4380},[],{"categories":4382},[],{"categories":4384},[],{"categories":4386},[149],{"categories":4388},[],{"categories":4390},[118],{"categories":4392},[],{"categories":4394},[149],{"categories":4396},[],{"categories":4398},[118],{"categories":4400},[123],{"categories":4402},[149],{"categories":4404},[473],{"categories":4406},[149],{"categories":4408},[164],{"categories":4410},[149],{"categories":4412},[164],{"categories":4414},[164],{"categories":4416},[],{"categories":4418},[],{"categories":4420},[164],{"categories":4422},[164],{"categories":4424},[164],{"categories":4426},[],{"categories":4428},[164],{"categories":4430},[123],{"categories":4432},[123],{"categories":4434},[],{"categories":4436},[149],{"categories":4438},[126],{"categories":4440},[67],{"categories":4442},[149],{"categories":4444},[],{"categories":4446},[164],{"categories":4448},[149],{"categories":4450},[560],{"categories":4452},[164],{"categories":4454},[164],{"categories":4456},[126],{"categories":4458},[132],{"categories":4460},[132],{"categories":4462},[],{"categories":4464},[132],{"categories":4466},[149],{"categories":4468},[],{"categories":4470},[],{"categories":4472},[123],{"categories":4474},[],{"categories":4476},[123],{"categories":4478},[123],{"categories":4480},[118],{"categories":4482},[149],{"categories":4484},[118],{"categories":4486},[164],{"categories":4488},[118],{"categories":4490},[132],{"categories":4492},[132],{"categories":4494},[132],{"categories":4496},[118],{"categories":4498},[149],{"categories":4500},[123],{"categories":4502},[473],{"categories":4504},[113],{"categories":4506},[473],{"categories":4508},[473],{"categories":4510},[132],{"categories":4512},[473],{"categories":4514},[473],[4516,4596,4817,4887],{"id":4517,"title":4518,"ai":4519,"body":4524,"categories":4575,"created_at":68,"date_modified":68,"description":59,"extension":69,"faq":68,"featured":70,"kicker_label":68,"meta":4576,"navigation":91,"path":4582,"published_at":4583,"question":68,"scraped_at":4584,"seo":4585,"sitemap":4586,"source_id":4587,"source_name":4588,"source_type":99,"source_url":4589,"stem":4590,"tags":4591,"thumbnail_url":68,"tldr":4593,"tweet":68,"unknown_tags":4594,"__hash__":4595},"summaries\u002Fsummaries\u002Fspark-s-50k-small-files-kill-downstream-query-spee-summary.md","Spark's 50k Small Files Kill Downstream Query Speed",{"provider":7,"model":8,"input_tokens":4520,"output_tokens":4521,"processing_time_ms":4522,"cost_usd":4523},3935,1699,16262,0.00112965,{"type":14,"value":4525,"toc":4571},[4526,4530,4533,4540,4544,4547,4563],[17,4527,4529],{"id":4528},"avoid-small-file-outputs-for-production-spark-jobs","Avoid Small-File Outputs for Production Spark Jobs",[22,4531,4532],{},"Spark jobs tuned only for write completion produce 50,000 files of ~200MB each for 10TB datasets. This creates production issues: downstream systems like Spark, Presto, or Trino face high latency because the driver's first step—listing and scheduling across 50k files—takes minutes before any data processing starts. Result: dashboards go red hours after successful writes, frustrating consuming teams.",[22,4534,4535,4539],{},[4536,4537,4538],"strong",{},"Fix the root cause upfront:"," Target output files in the 128MB–1GB range to enable locality (data on fewer nodes) and efficient batching, matching big-data engines' core assumptions. A 10TB job should aim for hundreds, not tens of thousands, of files—reducing metadata load and speeding reads by orders of magnitude.",[17,4541,4543],{"id":4542},"metadata-overhead-and-engine-assumptions","Metadata Overhead and Engine Assumptions",[22,4545,4546],{},"Each small file adds listing overhead: for 50k files, Spark's driver catalogs paths, sizes, and partitions before task assignment, burning time on coordination rather than compute. Individually, 200MB files read fine in isolation, but collectively they fragment HDFS\u002FS3 directories, preventing optimizations like:",[4548,4549,4550,4557],"ul",{},[4551,4552,4553,4556],"li",{},[4536,4554,4555],{},"Locality:"," Data spread across too many objects, forcing cross-node shuffles.",[4551,4558,4559,4562],{},[4536,4560,4561],{},"Batching:"," Engines expect larger files for vectorized I\u002FO and predicate pushdown.",[22,4564,4565,4566,4570],{},"Trade-off: Larger files improve reads but may increase write time slightly—prioritize downstream velocity over upstream completion speed. In interviews, demonstrate by repartitioning writes (e.g., ",[4567,4568,4569],"code",{},"df.repartition(1000).write...",") to hit optimal sizing based on cluster size and data volume.",{"title":59,"searchDepth":60,"depth":60,"links":4572},[4573,4574],{"id":4528,"depth":60,"text":4529},{"id":4542,"depth":60,"text":4543},[67],{"content_references":4577,"triage":4578},[],{"relevance":87,"novelty":4579,"quality":88,"actionability":88,"composite":4580,"reasoning":4581},3,4.15,"Category: Data Science & Visualization. The article provides a practical solution to a common issue in Spark jobs, addressing the pain point of query speed due to small file outputs. It offers actionable advice on file size optimization and includes a specific code example for repartitioning, making it relevant and useful for the target audience.","\u002Fsummaries\u002Fspark-s-50k-small-files-kill-downstream-query-spee-summary","2026-04-28 07:37:59","2026-04-28 15:15:44",{"title":4518,"description":59},{"loc":4582},"679e1a5369afda4f","Data and Beyond","https:\u002F\u002Fmedium.com\u002Fdata-and-beyond\u002Fdata-engineering-interview-question-fix-your-spark-output-from-50-000-tiny-files-to-fast-10b9d56e4343?source=rss----b680b860beb1---4","summaries\u002Fspark-s-50k-small-files-kill-downstream-query-spee-summary",[103,106,4592],"spark","Spark jobs writing 10TB as 50,000 200MB files cause minutes of metadata overhead on reads and break big-data engines' 128MB-1GB file assumptions, slowing queries.",[106,4592],"ATnzbfcyH60ubK4uo00ozg09Ah7mbkkczTfRZ-S5DPo",{"id":4597,"title":4598,"ai":4599,"body":4604,"categories":4802,"created_at":68,"date_modified":68,"description":59,"extension":69,"faq":68,"featured":70,"kicker_label":68,"meta":4803,"navigation":91,"path":4804,"published_at":4805,"question":68,"scraped_at":68,"seo":4806,"sitemap":4807,"source_id":4808,"source_name":4809,"source_type":99,"source_url":4810,"stem":4811,"tags":4812,"thumbnail_url":68,"tldr":4814,"tweet":68,"unknown_tags":4815,"__hash__":4816},"summaries\u002Fsummaries\u002Ffixing-ml-pipelines-for-databricks-constraints-summary.md","Fixing ML Pipelines for Databricks Constraints",{"provider":7,"model":8,"input_tokens":4600,"output_tokens":4601,"processing_time_ms":4602,"cost_usd":4603},4526,1389,13512,0.0015772,{"type":14,"value":4605,"toc":4796},[4606,4610,4617,4620,4656,4659,4663,4670,4695,4698,4702,4713,4754,4757,4782,4785,4789,4792],[17,4607,4609],{"id":4608},"adapt-storage-to-unity-catalog-for-governed-workflows","Adapt Storage to Unity Catalog for Governed Workflows",[22,4611,4612,4613,4616],{},"Databricks free environments disable public DBFS root, blocking traditional Delta table paths. Shift all data, checkpoints, and artifacts to Unity Catalog Volumes at ",[4567,4614,4615],{},"\u002FVolumes\u002Fworkspace\u002Fecom\u002Fecom_data\u002F",". This mirrors production shifts from open file systems to governed platforms, ensuring compliance without rework.",[22,4618,4619],{},"For MLflow model logging, specify a volume-based temp dir to avoid governance errors:",[4621,4622,4626],"pre",{"className":4623,"code":4624,"language":4625,"meta":59,"style":59},"language-python shiki shiki-themes github-light github-dark","mlflow.spark.log_model(\n    spark_model=model,\n    artifact_path=\"purchase_prediction_model\",\n    dfs_tmpdir=\"\u002FVolumes\u002Fworkspace\u002Fecom\u002Fecom_data\u002Fmlflow_tmp\"\n)\n","python",[4567,4627,4628,4636,4641,4646,4651],{"__ignoreMap":59},[4629,4630,4633],"span",{"class":4631,"line":4632},"line",1,[4629,4634,4635],{},"mlflow.spark.log_model(\n",[4629,4637,4638],{"class":4631,"line":60},[4629,4639,4640],{},"    spark_model=model,\n",[4629,4642,4643],{"class":4631,"line":4579},[4629,4644,4645],{},"    artifact_path=\"purchase_prediction_model\",\n",[4629,4647,4648],{"class":4631,"line":88},[4629,4649,4650],{},"    dfs_tmpdir=\"\u002FVolumes\u002Fworkspace\u002Fecom\u002Fecom_data\u002Fmlflow_tmp\"\n",[4629,4652,4653],{"class":4631,"line":87},[4629,4654,4655],{},")\n",[22,4657,4658],{},"Model artifacts must align with platform storage policies, preventing deployment failures in restricted setups.",[17,4660,4662],{"id":4661},"switch-to-micro-batch-streaming-for-reliability","Switch to Micro-Batch Streaming for Reliability",[22,4664,4665,4666,4669],{},"Serverless clusters reject continuous triggers in structured streaming. Use ",[4567,4667,4668],{},"availableNow=True"," for micro-batch processing instead:",[4621,4671,4673],{"className":4623,"code":4672,"language":4625,"meta":59,"style":59},"query = stream_df.writeStream \\\n    .format(\"delta\") \\\n    .trigger(availableNow=True) \\\n    .start(\"\u002FVolumes\u002Fworkspace\u002Fecom\u002Fecom_data\u002Fstream_output\")\n",[4567,4674,4675,4680,4685,4690],{"__ignoreMap":59},[4629,4676,4677],{"class":4631,"line":4632},[4629,4678,4679],{},"query = stream_df.writeStream \\\n",[4629,4681,4682],{"class":4631,"line":60},[4629,4683,4684],{},"    .format(\"delta\") \\\n",[4629,4686,4687],{"class":4631,"line":4579},[4629,4688,4689],{},"    .trigger(availableNow=True) \\\n",[4629,4691,4692],{"class":4631,"line":88},[4629,4693,4694],{},"    .start(\"\u002FVolumes\u002Fworkspace\u002Fecom\u002Fecom_data\u002Fstream_output\")\n",[22,4696,4697],{},"This delivers production stability and cost control, as many orgs prefer micro-batches over true continuous streams to avoid instability on e-commerce event pipelines.",[17,4699,4701],{"id":4700},"handle-spark-ml-quirks-and-scale-with-subsets","Handle Spark ML Quirks and Scale with Subsets",[22,4703,4704,4705,4708,4709,4712],{},"Spark ML stores prediction probabilities as VectorUDT, not arrays, causing ",[4567,4706,4707],{},"INVALID_EXTRACT_BASE_FIELD_TYPE"," errors. Convert with ",[4567,4710,4711],{},"vector_to_array",":",[4621,4714,4716],{"className":4623,"code":4715,"language":4625,"meta":59,"style":59},"from pyspark.ml.functions import vector_to_array\n\npredictions_final = predictions.select(\n    \"user_id\",\n    vector_to_array(\"probability\")[1].alias(\"purchase_probability\"),\n    \"prediction\"\n)\n",[4567,4717,4718,4723,4728,4733,4738,4743,4749],{"__ignoreMap":59},[4629,4719,4720],{"class":4631,"line":4632},[4629,4721,4722],{},"from pyspark.ml.functions import vector_to_array\n",[4629,4724,4725],{"class":4631,"line":60},[4629,4726,4727],{"emptyLinePlaceholder":91},"\n",[4629,4729,4730],{"class":4631,"line":4579},[4629,4731,4732],{},"predictions_final = predictions.select(\n",[4629,4734,4735],{"class":4631,"line":88},[4629,4736,4737],{},"    \"user_id\",\n",[4629,4739,4740],{"class":4631,"line":87},[4629,4741,4742],{},"    vector_to_array(\"probability\")[1].alias(\"purchase_probability\"),\n",[4629,4744,4746],{"class":4631,"line":4745},6,[4629,4747,4748],{},"    \"prediction\"\n",[4629,4750,4752],{"class":4631,"line":4751},7,[4629,4753,4655],{},[22,4755,4756],{},"For recommendation models, massive user\u002Fproduct IDs trigger model size overflow. Train on top users only:",[4621,4758,4760],{"className":4623,"code":4759,"language":4625,"meta":59,"style":59},"top_users = interaction_df.groupBy(\"user_id\") \\\n    .count() \\\n    .orderBy(\"count\", ascending=False) \\\n    .limit(50000)\n",[4567,4761,4762,4767,4772,4777],{"__ignoreMap":59},[4629,4763,4764],{"class":4631,"line":4632},[4629,4765,4766],{},"top_users = interaction_df.groupBy(\"user_id\") \\\n",[4629,4768,4769],{"class":4631,"line":60},[4629,4770,4771],{},"    .count() \\\n",[4629,4773,4774],{"class":4631,"line":4579},[4629,4775,4776],{},"    .orderBy(\"count\", ascending=False) \\\n",[4629,4778,4779],{"class":4631,"line":88},[4629,4780,4781],{},"    .limit(50000)\n",[22,4783,4784],{},"This respects memory limits, turning prototypes into scalable systems without full-dataset forcing.",[17,4786,4788],{"id":4787},"production-truth-constraints-drive-engineering","Production Truth: Constraints Drive Engineering",[22,4790,4791],{},"End-to-end pipelines—from raw e-commerce ingestion, feature engineering, training, MLflow tracking, to inference—evolve through constraint-handling, not textbook ideals. Storage policies, compute limits, framework quirks, and scaling pushback separate prototypes from reliable workflows. Focus on platform adaptations yields complete, governed systems that run in real infrastructure.",[4793,4794,4795],"style",{},"html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"title":59,"searchDepth":60,"depth":60,"links":4797},[4798,4799,4800,4801],{"id":4608,"depth":60,"text":4609},{"id":4661,"depth":60,"text":4662},{"id":4700,"depth":60,"text":4701},{"id":4787,"depth":60,"text":4788},[67],{},"\u002Fsummaries\u002Ffixing-ml-pipelines-for-databricks-constraints-summary","2026-04-08 21:21:17",{"title":4598,"description":59},{"loc":4804},"f6260e0e26516379","Learning Data","https:\u002F\u002Funknown","summaries\u002Ffixing-ml-pipelines-for-databricks-constraints-summary",[4813,103,106],"machine-learning","Databricks free workspaces block public DBFS, continuous triggers, and large models—use Unity Catalog volumes, micro-batch streaming, vector_to_array for probs, and top-50k user subsets to ship reliably.",[106],"DDKxQzHNGJWdH7cF4yYPqtK0OMYTkcQDCmvx-XltOl0",{"id":4818,"title":4819,"ai":4820,"body":4825,"categories":4853,"created_at":68,"date_modified":68,"description":59,"extension":69,"faq":68,"featured":70,"kicker_label":68,"meta":4854,"navigation":91,"path":4875,"published_at":68,"question":68,"scraped_at":4876,"seo":4877,"sitemap":4878,"source_id":4879,"source_name":4880,"source_type":99,"source_url":4881,"stem":4882,"tags":4883,"thumbnail_url":68,"tldr":4884,"tweet":68,"unknown_tags":4885,"__hash__":4886},"summaries\u002Fsummaries\u002Ffma-106k-tracks-dataset-for-mir-tasks-summary.md","FMA: 106K Tracks Dataset for MIR Tasks",{"provider":7,"model":8,"input_tokens":4821,"output_tokens":4822,"processing_time_ms":4823,"cost_usd":4824},9280,1963,13390,0.00233065,{"type":14,"value":4826,"toc":4848},[4827,4831,4834,4838,4841,4845],[17,4828,4830],{"id":4829},"dataset-structure-enables-scalable-mir-experiments","Dataset Structure Enables Scalable MIR Experiments",[22,4832,4833],{},"FMA compiles 106,574 tracks (343 days, 917 GiB total) from 16,341 artists across 14,854 albums in a 161-genre hierarchy. Metadata in tracks.csv covers ID, title, artist, genres, tags, play counts; genres.csv defines hierarchy; features.csv has librosa-extracted acoustics; echonest.csv adds EchoNest (Spotify) metrics for 13,129 tracks. Audio comes in subsets: fma_small (8k 30s clips, 8 balanced genres, 7.2 GiB), fma_medium (25k clips, 16 genres, 22 GiB), fma_large (106k clips, 161 genres, 93 GiB), fma_full (untrimmed tracks, 879 GiB). Train\u002Fval\u002Ftest splits proposed in paper; verify downloads via SHA1 checksums like f0df49ffe5f2a6008d7dc83c6915b31835dfe733 for metadata.zip.",[17,4835,4837],{"id":4836},"extract-features-and-run-genre-baselines","Extract Features and Run Genre Baselines",[22,4839,4840],{},"Use features.py to compute spectral, temporal traits from raw audio matching features.csv. baselines.ipynb trains genre classifiers: MFCCs yield 0.45 accuracy on small set (8 genres); full acoustic features hit 0.55; EchoNest reaches 0.60. Scale to full dataset for end-to-end learning. analysis.ipynb generates stats\u002Ffigures; webapi.ipynb queries FMA API for updates; creation.py scrapes\u002Fprocesses originals.",[17,4842,4844],{"id":4843},"quickstart-reproducible-workflow","Quickstart Reproducible Workflow",[22,4846,4847],{},"Clone repo, conda\u002Fmamba env with Python 3.6+, pip install -r requirements.txt (resampy workaround: pip install cython before resampy). Set AUDIO_DIR in .env to decompressed path. Run usage.ipynb for loading CSVs, training models; Binder launches instantly. MIT-licensed code, CC BY 4.0 metadata; cite ISMIR 2017 paper. Repo: 2.6k stars, 456 forks, 100+ citing papers (e.g., zero-shot classification, graph NNs), challenges like WWW 2018 genre contest.",{"title":59,"searchDepth":60,"depth":60,"links":4849},[4850,4851,4852],{"id":4829,"depth":60,"text":4830},{"id":4836,"depth":60,"text":4837},{"id":4843,"depth":60,"text":4844},[67],{"content_references":4855,"triage":4872},[4856,4861,4865,4868],{"type":4857,"title":4858,"author":4859,"url":4860,"context":77},"paper","FMA: A Dataset For Music Analysis","Michaël Defferrard, Kirell Benzi, Pierre Vandergheynst, Xavier Bresson","https:\u002F\u002Farxiv.org\u002Fabs\u002F1612.01840",{"type":4862,"title":4863,"url":4864,"context":85},"tool","librosa","https:\u002F\u002Flibrosa.org\u002F",{"type":4862,"title":4866,"url":4867,"context":85},"pandas","https:\u002F\u002Fpandas.pydata.org\u002F",{"type":4869,"title":4870,"url":4871,"context":85},"dataset","OpenMIC-2018: An Open Data-set for Multiple Instrument Recognition","https:\u002F\u002Fgithub.com\u002Fcosmir\u002Fopenmic-2018",{"relevance":4579,"novelty":60,"quality":88,"actionability":4579,"composite":4873,"reasoning":4874},3.05,"Category: Data Science & Visualization. The article provides a detailed overview of the FMA dataset and its structure, which is relevant for machine learning tasks in music information retrieval (MIR). However, while it offers some practical insights, it lacks a direct connection to building or shipping AI-powered products, making it less actionable for the target audience.","\u002Fsummaries\u002Ffma-106k-tracks-dataset-for-mir-tasks-summary","2026-04-15 15:26:07",{"title":4819,"description":59},{"loc":4875},"9e0f52779d245b96","__oneoff__","https:\u002F\u002Fgithub.com\u002Fmdeff\u002Ffma","summaries\u002Ffma-106k-tracks-dataset-for-mir-tasks-summary",[4813,103,105],"FMA dataset offers 106,574 CC-licensed tracks from Free Music Archive with metadata, precomputed features, and audio subsets for MIR tasks like genre recognition on 161 genres.",[],"cVRPbKE7n4b9_FJK2YhpANHRrEInvU-8tqyWMrlK_bQ",{"id":4888,"title":4889,"ai":4890,"body":4895,"categories":4982,"created_at":68,"date_modified":68,"description":59,"extension":69,"faq":68,"featured":70,"kicker_label":68,"meta":4983,"navigation":91,"path":4999,"published_at":5000,"question":68,"scraped_at":5001,"seo":5002,"sitemap":5003,"source_id":5004,"source_name":5005,"source_type":99,"source_url":5006,"stem":5007,"tags":5008,"thumbnail_url":68,"tldr":5010,"tweet":68,"unknown_tags":5011,"__hash__":5012},"summaries\u002Fsummaries\u002Fflink-treats-batch-as-streaming-for-unified-low-la-summary.md","Flink Treats Batch as Streaming for Unified Low-Latency Processing",{"provider":7,"model":8,"input_tokens":4891,"output_tokens":4892,"processing_time_ms":4893,"cost_usd":4894},8294,1951,22579,0.00212745,{"type":14,"value":4896,"toc":4977},[4897,4901,4904,4907,4911,4914,4917,4965,4968,4972,4975],[17,4898,4900],{"id":4899},"unify-batch-and-streaming-to-eliminate-latency-and-dual-systems","Unify Batch and Streaming to Eliminate Latency and Dual Systems",[22,4902,4903],{},"Real-world data like user clicks, views, and purchases arrives as continuous unbounded streams, but traditional batch processing dumps events into hourly files, introducing up to 60-minute latency—critical for recommendation engines where recent user behavior (e.g., hiking gear searches) must immediately influence suggestions like tents, not laptops. Streaming systems like Storm or Kinesis process events in milliseconds but require separate codebases from batch jobs (e.g., Hadoop\u002FMapReduce), leading to sync issues, duplicate logic, and reconciliation bugs.",[22,4905,4906],{},"Flink resolves this by treating bounded datasets as finite streams that have ended: a 5-year historical dataset is a stream started years ago and stopped today. Point the same Flink job at recent Kafka events for real-time recommendations or historical data for nightly retraining. This shares operators, clusters, and code, avoiding Lambda Architecture's two-system pain. Alibaba processes hundreds of billions of events daily across tens of thousands of machines; Netflix uses it for near-real-time anomaly detection; Uber built its analytical platform on it.",[17,4908,4910],{"id":4909},"build-stateful-pipelines-with-operators-state-and-windows","Build Stateful Pipelines with Operators, State, and Windows",[22,4912,4913],{},"Flink jobs form a dataflow DAG of sources (e.g., Kafka reads), operators (transformations like filtering bots or enriching metadata), and sinks (e.g., Redis writes). Every operator runs in parallel across cluster machines: set parallelism to 4 for a filter, and 4 subtasks process stream portions simultaneously, scaling to billions of events\u002Fday.",[22,4915,4916],{},"State is first-class for context across events—e.g., per-user hash map of recent views (append new item_id, trim >10min old). Flink manages state snapshots to durable storage, restoring on crashes without data loss. Windows slice infinite streams into finite chunks for aggregations: tumbling (non-overlapping, e.g., hourly), sliding (overlapping, e.g., 30min window every 1min), or session-based. Example for recommendations:",[4621,4918,4922],{"className":4919,"code":4920,"language":4921,"meta":59,"style":59},"language-scala shiki shiki-themes github-light github-dark","searches = readFromKafka(\"search-events\")\nclicks = readFromKafka(\"click-events\")\nuserActivity = (searches + clicks)\n  .keyBy(userId)\n  .window(slidingWindow(size=30min, slide=1min))\n  .aggregate(activityAggregator)  \u002F\u002F {userId, recentQueries, recentClicks}\nuserState = userActivity.asyncMap(callUserTowerModel)  \u002F\u002F embedding vector\n\u002F\u002F ... merge ANN\u002Ftrending candidates, rank top 100, writeTo(redis)\n","scala",[4567,4923,4924,4929,4934,4939,4944,4949,4954,4959],{"__ignoreMap":59},[4629,4925,4926],{"class":4631,"line":4632},[4629,4927,4928],{},"searches = readFromKafka(\"search-events\")\n",[4629,4930,4931],{"class":4631,"line":60},[4629,4932,4933],{},"clicks = readFromKafka(\"click-events\")\n",[4629,4935,4936],{"class":4631,"line":4579},[4629,4937,4938],{},"userActivity = (searches + clicks)\n",[4629,4940,4941],{"class":4631,"line":88},[4629,4942,4943],{},"  .keyBy(userId)\n",[4629,4945,4946],{"class":4631,"line":87},[4629,4947,4948],{},"  .window(slidingWindow(size=30min, slide=1min))\n",[4629,4950,4951],{"class":4631,"line":4745},[4629,4952,4953],{},"  .aggregate(activityAggregator)  \u002F\u002F {userId, recentQueries, recentClicks}\n",[4629,4955,4956],{"class":4631,"line":4751},[4629,4957,4958],{},"userState = userActivity.asyncMap(callUserTowerModel)  \u002F\u002F embedding vector\n",[4629,4960,4962],{"class":4631,"line":4961},8,[4629,4963,4964],{},"\u002F\u002F ... merge ANN\u002Ftrending candidates, rank top 100, writeTo(redis)\n",[22,4966,4967],{},"This computes rolling user features, embeddings, ~1000 candidates (500 ANN + 200 trending, deduped), fetches features, and ranks in seconds per user.",[17,4969,4971],{"id":4970},"exactly-once-guarantees-via-lightweight-checkpoints","Exactly-Once Guarantees via Lightweight Checkpoints",[22,4973,4974],{},"Flink ensures state updates apply exactly once, even on failures: periodic checkpoints snapshot operator state using Asynchronous Barrier Snapshotting (ABS). Barriers flow like records; operators snapshot on receipt and forward without pausing. On crash, rollback to last checkpoint, replay only post-checkpoint input (bounded by checkpoint interval, tunable). Partial re-execution avoids full restarts. Batch jobs use the same runtime but with blocked data exchange (upstream finishes before downstream starts), confirming no separate batch engine needed.",[4793,4976,4795],{},{"title":59,"searchDepth":60,"depth":60,"links":4978},[4979,4980,4981],{"id":4899,"depth":60,"text":4900},{"id":4909,"depth":60,"text":4910},{"id":4970,"depth":60,"text":4971},[132],{"content_references":4984,"triage":4996},[4985,4988,4993],{"type":4857,"title":4986,"author":4987,"context":77},"Apache Flink: Stream and Batch Processing in a Single Engine","Carbone, Katsifodimos, Ewen, Markl, Haridi, and Tzoumas",{"type":82,"title":4989,"author":4990,"url":4991,"context":4992},"System Design Series: Apache Kafka from 10,000 feet","Sanil Khurana","https:\u002F\u002Fmedium.com\u002Fbetter-programming\u002Fsystem-design-series-apache-kafka-from-10-000-feet-9c95af56f18d","recommended",{"type":82,"title":4994,"author":4990,"url":4995,"context":4992},"System Design Series: A Step-by-Step Breakdown of Temporal’s Internal Architecture","https:\u002F\u002Fmedium.com\u002Fdata-science-collective\u002Fsystem-design-series-a-step-by-step-breakdown-of-temporals-internal-architecture-52340cc36f30",{"relevance":88,"novelty":4579,"quality":88,"actionability":4579,"composite":4997,"reasoning":4998},3.6,"Category: Data Science & Visualization. The article discusses how Apache Flink unifies batch and streaming data processing, addressing a specific pain point for product builders who need real-time data handling for applications like recommendation engines. It provides insights into Flink's architecture and its practical applications, but lacks detailed actionable steps for implementation.","\u002Fsummaries\u002Fflink-treats-batch-as-streaming-for-unified-low-la-summary","2026-05-01 20:29:41","2026-05-03 17:00:38",{"title":4889,"description":59},{"loc":4999},"7828397ca7d069ee","Level Up Coding","https:\u002F\u002Flevelup.gitconnected.com\u002Fsystem-design-series-apache-flink-from-10-000-feet-and-building-a-flink-powered-recommendation-b831b72f8d81?source=rss----5517fd7b58a6---4","summaries\u002Fflink-treats-batch-as-streaming-for-unified-low-la-summary",[103,5009,106],"software-engineering","Apache Flink processes unbounded streams and bounded batches with one engine using operators, state, windows, and exactly-once guarantees, eliminating dual codebases for real-time apps like recommendation engines handling millions of events.",[5009,106],"lBiNZOCv4deZZPrjSlDVt_j8PB8mmkQeN6ctexZZ1Ow"]