I’ve managed this blog since 2011. I’m also a primary contributor to the documentation managed by my team. For example: on-boarding guide for new colleagues or a tourist, technical guides, definition of done, architecture decision records and more. As you perhaps already know, software engineers don’t like writing t...
Go is a modern programming language designed at Google to improve developer productivity. But how to improve productivity? By limiting engineers’ power and flexibility 😂
Recently I’ve read “Software Engineering at Google” curated by Titus Winters, Tom Manshreck and Hyrum Wright.
If you are a BigData engineer who develops batch data pipelines, you might often hear that stream processing is the future. It unlocks the full potential of data that’s often unbounded in nature. You don’t need batch pipelines anymore, implement everything in a streaming fashion. There are plenty of modern and ea...
Last week I virtually attended Apache Beam Summit 2022 held in Austin, Texas. The event concentrates around Apache Beam and the runners like Dataflow, Flink, Spark or Samza. Below you can find my overall impression of the conference and notes from several interesting sessions. If some aspect was particularly appeal...
When you deploy any non-trivial data pipelines using complex infrastructure you should expect some troubles sooner or later.
Do you check costs of the data pipelines in exactly the same way as you check overall health, latency or throughput? Nowadays, taking care of cost efficiency is an integral part of every data engineer job. I would like to share my own experiences with applying FinOps discipline in the organization within tens of da...
Dataproc is a fully managed and highly scalable Google Cloud Platform service for running Apache Spark. However, “managed” doesn’t relieve you from the proper configuration to squeeze more processing power for less money. Today you will learn the easiest method to configure Dataproc and Spark together to get optimal...
I would love to only develop streaming pipelines but in reality some of them are still batch oriented. Today you will learn how to properly configure Google Cloud Platform scheduler – Cloud Composer.
This is the second part of the stream processing blog post series. In the first part I presented aggregations in a fixed, non-overlapping windows. Now you will learn dynamic aggregations in data-driven windows, for which the size of each window depends on the input data instead of a predefined time based pattern. At...