Data Engineering & Databases posts

SQL That Scales: Joins, Windows, and Real-World Patterns

Write queries that stay readable and fast as tables grow.

Explain Plans Demystified: Find the Slow Part Fast

Learn to read query plans and fix bottlenecks with confidence.

Star Schema vs Snowflake: Modeling for Analytics

Choose the right model for BI, speed, and cost.

Postgres Power Moves: Indexes, JSONB, and Partitioning

Features that turn Postgres into a serious data platform.

Warehouse vs Lake vs Lakehouse: What to Pick in 2026

A practical comparison for cost, governance, and workloads.

dbt in Practice: The Clean Way to Build Transformations

Models, tests, docs, and deployment patterns that scale.

Airflow vs Prefect: Picking an Orchestrator

Tradeoffs for teams, complexity, and reliability.

Kafka Basics: Events, Partitions, and Consumer Groups

Build streaming pipelines without confusion.

Change Data Capture (CDC): Sync Data Without Full Reloads

How CDC works, common pitfalls, and design patterns.

Data Quality That Sticks: Tests, Contracts, and Alerts

Prevent silent data bugs with pragmatic guardrails.

Data Governance for Builders: Lineage, Ownership, and SLAs

A lightweight approach that avoids meetings and still works.

Warehouse Cost Control: The Queries That Burn Money

Spot expensive patterns and keep usage predictable.

NoSQL When It’s Actually the Right Choice

Document, key-value, graph, time-series—use cases and tradeoffs.

Index Design: Composite, Covering, Partial—What to Use When

A practical guide to index strategy beyond ‘add an index.’

Schema Migrations Without Downtime

Safe patterns for evolving schemas in production.

ETL vs ELT: The Decision That Shapes Your Stack

Choose based on compute, governance, and team workflow.

Build a Mini Lakehouse at Home: A Portfolio Project

A hands-on project to learn ingestion, transforms, and BI.

Python for Data Engineers: Clean Pipelines, Not Notebooks

Write production-friendly data code with structure and tests.