Ingest tables in parallel with an Apache Spark notebook using multithreading

If we want to kick off a single Apache Spark notebook to process a list of tables we can write the code easily. The simple code to loop through the list of tables ends up running one table after another (sequentially). If none of these tables are very big, it is quicker to have Spark load tables concurrently (in parallel) using threads. There are some different options of how to do this, but I am sharing the easiest way I have found when working with a notebook in Databricks, Azure Synapse Spark, Jupyter, or Zeppelin.

Querying Log Analytics using KQL

Intro Let’s walk through the fundamentals of using Kusto Query Language (KQL) to query your logs in Azure Log Analytics. Check out the video to see it in action and keep reading for more code examples and written steps to run queries. This covers a few basics as well as a complex query used to… Continue Reading

Monitoring Azure Databricks with Log Analytics

Log Analytics provides a way to easily query Spark logs and setup alerts in Azure. This provides a huge help when monitoring Apache Spark. In this video I walk through the setup steps and quick demo of this capability for the Azure Databricks log4j output and the Spark metrics. I include written instructions and troubleshooting… Continue Reading

Spark Monitoring video series

In this series I share about monitoring Apache Spark with Azure Databricks. Most of the content is relevant even if using open source Apache Spark or any other managed Spark service. I will be adding to this playlist and would love suggestions on what questions you still have about monitoring your Apache Spark workloads.