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


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.


Spark Summit Takeaways

Wrapping up my attendance at Spark + AI Summit 2020 and I found a lot of value. Here are my quick takeaways to try and save you time. To keep it real, some sessions were a big miss for me either due to too much detail or not enough focus, but some were awesome. If… Continue Reading


Data Lake Introduction

Hearing a lot of mention of Data Lakes but still not sure what that means or why anyone cares? This video will cover a brief introduction to what a Data Lake is and why so many organizations are adding them to their analytics ecosystem. To show what interacting with a data lake may look like for a typical data analyst, I included a demo of how you would use Spark SQL to query the data lake from Azure Databricks.