My name is Bartosz Konieczny. I'm a freelance data engineer and author of the Data Engineering Design Patterns (O'Reilly) book. When I'm not helping clients solve data engineering challenges to drive business value, I enjoy sharing what I learned here.
Consulting β Courses & trainings β Data Engineering Design Patterns Book β Github β Most recent blog posts βOne of the recommended ways of sharing a library on Databricks is to use the Unity Catalog to store the packages in the volumes. That's the theory but the question is, how to connect the dots between the release preparation and the release process? I'll try to answer this in the blog post.
MERGE, aka UPSERT, is a useful operation to combine two datasets if records identity is preserved. It appears then as a natural candidate for idempotent operations. Although it's true, there will be some challenges when things go wrong and you need to reprocess the data.
Even though data engineers enjoy discussing table file formats, distributed data processing, or more recently, small data, they still need to deal with legacy systems. By "legacy," I mean not only the code you or your colleagues wrote five years ago but also data formats that have been around for a long time. Despite being challenging for data engineers, these formats remain popular among business users. One of them is Excel.
Timely and accurate data is a Holy Grail for each data practitioner. To make it real, data engineers have to be careful about the transformations they make before exposing the dataset to consumers, but they also need to understand the timeline of the data.
It's the second blog post about laterals in Apache Spark SQL. Previously you discovered how to combine queries with lateral subquery and lateral views. Now it's time to see a more local feature, lateral column aliases.
Seven (!) years have passed since my blog post about Join types in Apache Spark SQL (2017). Coming from a software engineering background, I was so amazed that the world of joins doesn't stop on LEFT/RIGHT/FULL joins that I couldn't not blog about it ;) Time has passed but lucky me, each new project teaches me something.
When I was learning about watermarks in Apache Flink, I saw they were taking the smallest event times instead of the biggest ones in Apache Spark Structured Streaming. From that I was puzzled... How is it possible the pipeline doesn't go back to the past? The answer came when I reread the Streaming Systems book. There was one keyword I had missed that clarified everything.
One of the biggest changes for PySpark has been the DataFrame API. It greatly reduces the JVM-to-PVM communication overhead and improves the performance. However, it also complexities the code. Probably, some of you have already seen, written, or worked with the code like this...
In the last blog post of the data quality on Databricks series we're going to discover a Databricks Labs product, the DQX library.
Previously we learned how to control data quality with Delta Live Tables. Now, it's time to see an open source library in action, Spark Expectations.
Data quality is one of the key factors of a successful data project. Without a good quality, even the most advanced engineering or analytics work will not be trusted, therefore, not used. Unfortunately, data quality controls are very often considered as a work item to implement in the end, which sometimes translates to never.
Data contracts was a hot topic in the data space before LLMs and GenAI came out. They promised a better world with less communication issues between teams, leading to more reliable and trustworthy data. Unfortunately, the promise has been too hard to put into practice. Has been, or should I write "was"?
If you have been working with Apache Airflow already, you certainly met XComs at some point. You know, these variables that you can "exchange" between tasks within the same DAG. If after switching to Databricks Workflows for data orchestration you're wondering how to do the same, there is good news. Databricks supports this exchange capability natively with Task values.
For over two years now you can leverage file triggers in Databricks Jobs to start processing as soon as a new file gets written to your storage. The feature looks amazing but hides some implementation challenges that we're going to see in this blog post.
Is there an easier way to address the insertInto position-based data writing in Apache Spark SQL? Totally, if you use a column-based method such as saveAsTable with append mode.
Bartosz Konieczny
