Data Engineering
83 articles in "Data Engineering"
Ultimate Guide to Data Storytelling with Dashboards
Turn dashboards into decision tools: start with one business question, design for one audience, show the insight and next steps.
Scaling Self-Service Analytics with Databricks
Standardize Gold tables, Unity Catalog metric views, and SQL Warehouses to deliver governed, consistent self-service analytics and BI access.
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.