Scenario I have a notebook that processes hot data every 5 minutes. Meanwhile, another pipeline processes historical data, and I want to create a summary table that uses the hot data incrementally …

Guidance to Maximizing Productivity and Efficiency for your Data Engineering Workloads Data engineering teams often grapple with the complexities of planning and configuring compute resources for their data platforms. This is especially true when working with large-scale, complex datasets and demanding downstream SLAs. A one-size-fits-all approach is rarely effective, as different data layers and datasets … <p class="link-more"><a href="https://blog.fabric.microsoft.com/en-us/blog/optimizing-spark-compute-for-medallion-architectures-in-microsoft-fabric/" class="more-link">Continue reading<span class="screen-reader-text"> “Optimizing Spark Compute for Medallion Architectures in Microsoft Fabric”</span></a>

One of the amazing things about Microsoft Fabric is the number of options you have for moving data. For example, you can use Dataflow Gen2, Pipelines, Notebooks, or any combination of the three. However, with all the options available, it can also ma...