Etl
27 articles tagged with "Etl"
ETL Pipeline Benchmarking: Metrics to Track
Measuring the right ETL metrics—throughput, freshness, quality, cost, and scalability—prevents silent failures and runaway cloud spend.
How to Tune Concurrency in Apache Airflow
Tune Airflow concurrency across global, DAG, task, and executor levels using pools, metrics, and incremental tests to remove scheduling bottlenecks.
Databricks ETL Optimization for Petabyte Data
Guide to tuning Databricks for petabyte ETL: cluster sizing, Delta Lake layout, Auto Loader, AQE, and predictive optimization.
Snowflake Bottlenecks: Troubleshooting Tips
Query design, not warehouse size, is often the real reason Snowflake slows; profile queries, reduce I/O, optimize loads, and right-size resources.
How Airflow Supports Analytics Monitoring
Setup and monitor analytics pipelines with Airflow: UI views, logs, alerts, Prometheus/Grafana, and best practices for reliability.
How Airflow Enhances Bootcamp Learning
Covers Airflow setup, DAG best practices, dbt/Snowflake integrations, and capstone projects for bootcamp learners.
Hive Query Optimization Questions Explained
Practical Hive optimization: partitioning, bucketing, compression, Tez, vectorized execution and CBO to speed queries and cut storage and compute costs.
Structured Streaming for Live Video on Databricks
Build low-latency live video pipelines with a unified lakehouse streaming approach, efficient state stores, and medallion data layers.
Databricks vs. Airflow for Event-Driven Workflows
Compare Databricks and Airflow for event-driven workflows—native triggers, Spark scaling, integration trade-offs, and cost differences.
Checklist for Building a Cloud Data Engineer Portfolio
Two to three production-ready cloud data projects beat dozens of tutorials for landing data engineering interviews.
5 Tools To Showcase Data Engineering Skills
Learn how Airflow, AWS, Snowflake, dbt, and Spark projects can power a standout data engineering portfolio with real end-to-end workflows.
How To Add Data Quality Checks in Pipelines
Automated data validations for ingestion and transformations using Great Expectations and dbt-expectations to catch errors early and keep analytics trustworthy.