Data Engineering
69 articles in "Data Engineering"
Case Study: Caching with Databricks for Faster Analytics
Cut scans from 2.3TB to 8GB and reduce compute costs 73% using Disk Cache, Spark cache, SQL result cache and improved file layout.
Git Workflows for Data Teams
Use one Git branch model, short-lived branches with reviews and CI, map Dev/Stage/Prod, and keep notebooks and large files out of Git.
Top Tools for Data Lakehouse and Data Warehouse
Choose a lakehouse for unified SQL, ML, and streaming - use open formats and governance to avoid lock-in and control costs.
Caching with Redis: Best Practices for Engineers
Practical Redis caching guide: design keys, set TTLs with jitter, choose eviction policies, monitor, scale, and secure production caches.
How to Monitor Security in Databricks Lakehouses
Use Unity Catalog, system tables, SAT, and SIEM integrations to monitor lakehouse security, detect threats, and automate response.
Snowflake for Data Retention: Best Practices
Set Time Travel, Fail-safe, storage tiers and lifecycle policies to balance compliance, recovery, and storage cost in Snowflake.
ETL Pipeline Benchmarking: Metrics to Track
Measuring the right ETL metrics—throughput, freshness, quality, cost, and scalability—prevents silent failures and runaway cloud spend.
Managing Domain Events in Event-Driven Architectures
Treat domain events as versioned API contracts—design for consumers, use outbox/CDC for reliable delivery, and enforce clear ownership.
Snowflake Query Tuning: Best Practices for Low Latency
Practical Snowflake tuning: right-size warehouses, improve micro-partitioning, optimize SQL and caching to cut query latency.
How to Optimize Data Flow in Distributed ML Pipelines
Profile pipelines, optimize storage and formats, parallelize loading and shuffling, and cache to boost GPU utilization and cut costs.
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.
How to Troubleshoot Cloud Data Warehouse Issues
Diagnose root causes—connections, slow queries, storage, and security—and apply targeted fixes to cut costs and boost cloud data warehouse performance.