Weāve covered everything from architecture to performance and cost management in Azure Synapse. Now, letās close out the series with a practical guide to monitoring, troubleshooting, and applying best practices to keep your data warehouse running smoothly. Even the best-designed pipelines need visibility and tuningāthis is where…
In previous posts, weāve talked about designing tables, distributing data, and writing efficient queries. Now letās turn our attention to resource managementāa crucial area for both performance and cost control in Azure Synapse Dedicated SQL Pools. Synapse gives you tools to scale compute, manage concurrency, and minimize…
Once youāve designed your tables and chosen the right distribution strategy, the next big lever for performance is query optimization. In Azure Synapse Dedicated SQL Pools, this means writing efficient SQL, minimizing data movement, and ensuring the engine has the information it needs to generate the best query plan.…
Now that weāve explored why Azure Synapse is such a powerful platform for cloud-scale analytics, itās time to dig into one of the most important aspects of performance tuning: table design and data distribution. Azure Synapse uses an MPP (Massively Parallel Processing) engine under the hood. That means…
Bringing robust DevOps practices to cloud data platforms is crucial for delivering scalable, reliable solutions. In this post, Iāll walk through how we implemented a streamlined CI/CD pipeline for Azure Synapse Analytics using Azure DevOps. This setup enables our team to safely promote changes from development to production…
In the previous post, we explored the foundational difference between SMP and MPP systems, and why MPP is better suited for large-scale analytics. Now, letās dive into how Azure Synapse Analytics builds on that MPP architecture to become one of the most powerful and flexible cloud data platforms available…
Intro Following a dreadful stint in retail and food service, one of my first ārealā jobs was manually keying addresses for the United States Postal Service. Today, Iām a data engineer working with Azure Synapse, ADF, dbt, and Snowflake ā building scalable cloud pipelines and leading technical projects. But my…
When working with data at scale, one of the most important concepts to understand is the distinction between SMP (Symmetric Multiprocessing) and MPP (Massively Parallel Processing). These two architectures form the backbone of how modern data systems are designed and performāand they're especially important when evaluating platforms…