Etl
36 articles tagged with "Etl"
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.
Green Data Pipelines vs. Traditional Pipelines
Compare green and traditional data pipelines: energy use, cost savings, scalability, and techniques like lazy evaluation, sparse models, and carbon-aware scheduling.
Open Source ETL Tools: Comparison Guide 2026
Compare six open-source ETL tools—Airbyte, Airflow, NiFi, Pentaho, Meltano, and Talend (retired)—to find the best fit for scale, real-time needs, and team skills.
How Databricks Handles Schema Transformations
Guide to schema enforcement, schema evolution, Auto Loader, mergeSchema, type widening, and streaming best practices in Databricks.