Monitor Synapse Spark 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 Synapse Spark log4j output. I include written instructions and troubleshooting guidance in this… Continue Reading


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.


Azure Synapse Spark: External Python Packages

When working with an Apache Spark environment you may need to install external libraries or custom packages. In this post I share the steps for installing Python packages to Azure Synapse serverless Apache Spark pools. For Python code the libraries are packages as wheel (.whl) files. You can also install Python packages that are available… 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.


Spark Streaming join to Slow Changing Data

f you are building data pipelines for a video streaming site, you would need to consume analytics about video views in real time. Assume you need to look up additional user attributes like the subscription level, that information will change very infrequently. However, once that change happens its important to start tying usage to the correct subscription right away. So you need to find the best way to lookup that info in Apache Spark. With Delta Lake format, the batch data frame will update in memory without restarting the stream. The video in this post shows an example of this in action. Delta Lake supports updates via the merge statement so you keep the data up to date in your file system and Spark will also update its in memory data frame.


Which language for Apache Spark

Best Language for Apache Spark

The question is raised often, “What programming language should we choose for our Apache Spark project?” The short answer I give is to choose between Scala or Python. I admit, this is only slightly more helpful than saying it depends, which I try to avoid. The real question is what are the tradeoffs between the… Continue Reading


Apache Spark with Python

Azure Synapse Spark with Python

In this video, I share with you about Apache Spark using the Python language, often referred to as PySpark. We’ll walk through a quick demo on Azure Synapse Analytics, an integrated platform for analytics within Microsoft Azure cloud. This short demo is meant for those who are curious about PySpark or just want to get… Continue Reading