Data Engineering
38 articles in "Data Engineering"
Checklist for Choosing Stream Processing Tools
A practical checklist for selecting stream processing tools based on scalability, latency, cost, and support.
Databricks for Financial Market Analysis
Use Databricks Lakehouse to combine real-time and historical market data, build streaming Delta pipelines, and train scalable predictive models.
Scaling with Databricks and Snowflake: Strategies
Compare horizontal vs vertical scaling for cloud data platforms, explore autoscaling policies, cost trade-offs, and hybrid best practices for performance and savings.
Polyglot Persistence: Database Per Service Pattern
How polyglot persistence and the database-per-service pattern let microservices pick optimal databases, scale independently, and manage consistency trade-offs.
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.
How to Optimize Query Concurrency in Snowflake
Reduce Snowflake query slowdowns by tuning MAX_CONCURRENCY_LEVEL, using auto-scaling, clustering keys, materialized views, and monitoring.
Error Handling in dbt: Best Practices
Practical dbt error-handling guide: diagnose compilation, model, and database errors; use tests, safe casts, macros, logs, and CI/CD to prevent failures.
Snowflake in Hybrid Cloud Data Architecture
Unify storage, compute, and governance across hybrid clouds using hybrid tables, micro-partitioning, secure cross-cloud sharing, and pay-per-use scaling.
Error Handling in Airflow with Python Pipelines
Reliable Airflow pipelines require intentional error handling: retries, idempotent tasks, targeted exceptions, alerts, and robust logging.
Backward Compatibility in Schema Evolution: Guide
Evolve schemas without breaking pipelines: learn safe changes, compatibility modes (BACKWARD vs BACKWARD_TRANSITIVE), registry best practices, and rollout tips.
Case Study: Optimizing Analytics with dbt and Snowflake
How dbt and Snowflake modernize analytics: three-layer pipelines, faster queries, lower costs, and AI-enabled features with real-world results.