A 3-day bug hunt on a 3-person team costs up to β¬7,200 in lost engineering time. This workshop teaches you to prevent that β unit tests, data tests, and integration tests for PySpark and Databricks Lakeflow, including Spark Declarative Pipelines.
Idempotence is something I appreciate, maybe the most, in data engineering. If you write an idempotent logic you don't need to worry when your logic is reprocessed. You don't need to worry that it will generate duplicates or inconsistent results between runs. However, using it is not always easy and I'm actively looking for all related patterns to it. This time I will focus on idempotent consumer implementation in Apache Camel. Even though it may sounds old-school with modern streaming and messaging solutions, it's a good solution to know.
In the previous post from Big Data patterns implemented series, I wrote about a pattern called fan-in ingress. The idea was to consolidate the data coming from different sources. This time I will cover its companion called fan-out ingress, doing exactly the opposite.
The series about the implementation of Big Data patterns continues. This time I will focus on a streaming pattern called fan-in ingress.
Sometimes metadata is disregarded but very often it helps to retrieve the information easier and faster. One of such use cases are the headers of Apache Parquet where the stats about the column's content are stored. The reader can, without parsing all the lines, know whether what is he looking for is in the file or not. The metadata is also a part of one of Big Data patterns called automated processing metadata insertion.
Some time ago I found a site listing Big Data patterns (link in "Read also" section). However, that site describes them from a very general point of view and it's not always obvious to figure out the what, why and how. That's why I decided to start a new series of posts where I will try to describe these patterns and give some more technical context.
