Data + AI Summit 2023 has just completed with many announcements and deep dives. I attended virtually this year but was just as excited as the in-person attendees for some of the new capabilities that were shared. After watching the keynote presentations and tracking additional posts about new features, I want to summarize the top… Continue Reading
Every Spark pipeline involves reading data from a data source or table. For data engineers we usually end the pipelines by writing the transformed data. In this tutorial we walk through some of the most common format and cloud storage locations for reading and writing with Spark. We’ll save some of the advanced Delta Lake… Continue Reading
Get hands on with Spark SQL (no Python or Scala) to build your first data pipeline. In this video I walk you through how to read, transform, and write the NYC Taxi dataset with Spark SQL. This dataset can be found on Databricks, Azure Synapse, or downloaded from the web to wherever you run Apache… Continue Reading
Get hands on with Python and PySpark to build your first data pipeline. In this video I walk you through how to read, transform, and write the NYC Taxi dataset which can be found on Databricks, Azure Synapse, or downloaded from the web to wherever you run Apache Spark. Once you have watched and followed… Continue Reading
I received many questions on my tutorial Ingest tables in parallel with an Apache Spark notebook using multithreading. In this video and post I address some of the questions that I couldn’t just answer in the YouTube comments. Watch the video for more complete answers but here are quick responses with links to examples where… Continue Reading
I am honored to speak at Current 22. The example notebook that I walk through towards the end is available at https://github.com/datakickstart/datakickstart-databricks-workspace/blob/main/stackoverflow/stackoverflow_streaming.py.
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.
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
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
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.
