36 articles tagged with "Etl"

Low-latency AI streams: use Kafka to ingest, Flink to build features and score, with replay, lateness handling, and exactly-once delivery.

Seven practical signals that reveal when your Python data pipeline needs profiling to find code, orchestration, or infrastructure bottlenecks.

Match serverless settings to each workload's tightest limit: latency for inference, throughput for ETL, dbt warehouse, joins for features.

Checklist of Spark exam essentials: DataFrames/SQL, joins, partitions, performance, streaming, and testing for hands-on prep.

Core Snowflake interview topics: architecture, warehouses, recovery, loading, and security — emphasize trade-offs in cost, speed, and risk.

Map bounded contexts, classify relationships, and choose integration patterns to reduce rework, schema drift, and pipeline breakage.

Commands change state, events record facts, and projections build read models—covers aggregates, snapshots, concurrency, and replay.

Quickly compare ETL and ELT: when to transform data, plus trade-offs in cost, security, scalability, and use cases.

Matching AWS services to workload beats memorization—use access pattern, latency, and control to choose S3, Glue, Redshift, or Athena.

Measuring the right ETL metrics—throughput, freshness, quality, cost, and scalability—prevents silent failures and runaway cloud spend.

Tune Airflow concurrency across global, DAG, task, and executor levels using pools, metrics, and incremental tests to remove scheduling bottlenecks.

Guide to tuning Databricks for petabyte ETL: cluster sizing, Delta Lake layout, Auto Loader, AQE, and predictive optimization.