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
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.
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.
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
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
Azure Synapse Analytics just went Public Preview so now you can access all kinds of capability. Here is a quick introduction to what it is and why it matters.
This is part 2 of my Journey of a Data Engineer series which all started from the question “What’s the best path to be a great data engineer?” Check out Part 1: From College to BI Developer for the path from college through my first role as a BI consultant. In this post I’ll cover the steps… Continue Reading
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.
In the world of data science we often default to processing in nightly or hourly batches, but that pattern is not enough any more. Our customers and business leaders see information is being created all the time and realize it should be available much sooner. While the move to stream processing adds complexity, the tools we have available make it achievable for teams of any size.
This presentation covers why we need to shift some of our workloads from batch data jobs to streaming in real-time. We dive into how Spark Structured Streaming in Azure Databricks enables this along with streaming data systems such as Kafka and EventHub. We will discuss the concepts, how Azure Databricks enables stream processing, and review code examples on a sample data set.
With the shift to data lakes that use distributed file storage as the foundation, we have been missing the reliability that relational databases provides. Databricks Delta is a data management system focused on bringing more reliability and performance into our data lakes. It sits on top of existing storage and the API is very similar to reading and writing to files from Spark already. This session will present the overview of Delta Lake, why it may be a better option than standard data lake storage, and how you can use it from Azure Databricks.