10 min read
CI/CD Pipelines for Data Engineers
Data pipelines are production software. Here's how to build CI/CD that catches bad transforms before they corrupt dashboards: testing strategy, environment promotion, slim runs, and rollback patterns.
The Augmented Dev169 posts · 6 themes
Notes from the edges of enterprise software.
Field-tested patterns, honest postmortems, and short tactical fixes on Claude, MCP, system design, Dynamics 365, and the modern web. Written for engineers who ship.
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Guides and insights for Dynamics 365 Finance & Operations, Sales, Customer Service, and Copilot.
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System design, modern web development with Next.js, React, .NET, Azure, and practical coding guides.
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Deep dives into designing scalable, reliable systems — URL shorteners, chat apps, rate limiters, caching, and more.
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Practical guides on Claude, the Model Context Protocol (MCP), agent architectures, prompt caching, and tool use in production.
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Columnar Storage: How Analytical Databases Work Under the Hood
Why column-oriented databases run analytical queries 100x faster than row-oriented ones — covering physical layout, compression algorithms, vectorized execution, and predicate pushdown with concrete examples.
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dbt Best Practices for Production Data Modeling
Hard-won lessons for structuring dbt projects that scale: layer conventions, testing strategies, slim CI runs, incremental models, and documentation that doesn't rot.
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Kafka Consumer Groups, Partitions, and Offset Management Explained
A deep dive into how Kafka distributes work across consumers, why rebalancing stalls your pipeline, and how to choose an offset commit strategy that matches your delivery guarantee requirements.
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PostgreSQL Connection Pooling with PgBouncer
Why PostgreSQL's connection model breaks under load, how PgBouncer fixes it, and how to configure transaction-mode pooling without getting bitten by prepared statements or advisory locks.
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Elastic Tables in Dataverse: When Cosmos-Backed Storage Beats Standard Tables
What Dataverse elastic tables are, how TTL, partitioning, and JSON columns work, and how to decide between elastic and standard tables.