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Why Would I Ever Need to Partition My Big ‘Raw’ Data?

Whether you are running an RDBMS, or a Big Data system, it is important to consider your data-partitioning strategy. As the volume of data grows, so it becomes increasingly important to match the way you partition your data to the way it is queried, to allow 'pruning' optimisation. When you have huge imports of data to consider, it can get complicated. Bartosz explains how to get things right; not perfect but wisely.

External Article

How to Start Big Data with Apache Spark

It is worth getting familiar with Apache Spark because it a fast and general engine for large-scale data processing and you can use you existing SQL skills to get going with analysis of the type and volume of semi-structured data that would be awkward for a relational database. With an IDE such as Databricks you can very quickly get hands-on experience with an interesting technology.

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Five Intelligent Query Processing Features in SQL Server 2022 That Quietly Tune Your Workload

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Checking the Error Log I

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Question of the Day

Checking the Error Log I

On my SQL Server 2025, I want to search the error log from my T-SQL code for potential issues and then inform an administrator. What is the current way to easily query the error log?

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