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When I was preparing my AWS Big Data specialty certification, I was not comfortable with 2 categories, the visualization and security. Because of that I decided to work on them, starting with the latter one which can have a more direct impact. The work that I initiate with this post about data security practices on AWS data services. Continue Reading →
In my previous post I explained how Apache Spark can reorder JOINs based on the logical plan. Today I'll focus on another aspect of reordering which uses cost estimation for the proposed plans. Continue Reading →
January 10, 2020 I successfully passed my AWS Certified Big Data specialty with the overall score of 82%. Despite the fact that it will be replaced soon (April 2020) by AWS Certified Data Analytics - Specialty, I'd like to share with you my learning process and interesting resources. Continue Reading →
One of the reasons why I like my blogging activity is that from time to time the exchange is bidirectional. It happens mostly on Github but also on the comments under the post and I appreciate the situation when I don't know the answer and must dig a little to explain it in a blog post :) I wrote this one thanks to bithw1 issue created on my Spark playground repository (thank you for another interesting question btw :)). Continue Reading →
Even though I've already written a few posts about Apache Kafka as a data source in Apache Spark Structured Streaming, I still had some questions in my head. In this post I will try to answer them and let this Kafka integration in Spark topic for investigation later. Continue Reading →
Containers are with us, data engineers, for several years. The concept was already introduced on YARN but the technology that really made them popular was Docker. In this post I will focus on its recommended practices to make our Apache Spark images better. Continue Reading →
Maybe you didn't like my clickbait title but wanted to test my creativity with it ;) And more seriously, in this post I will cover streams but the ones that you're using in your Scala/Java code rather than the distributed ones provided by Apache Kafka. And I decided to write that because by analyzing a lot of Spark I/O method, I meet streams everywhere and I wanted to shed some light on them. Continue Reading →
When I discovered microk8s I was delighted! An easy installation in very few steps and you can start to play with Kubernetes locally (tried on Ubuntu 16). However, running Apache Spark 2.4.4 on top of microk8s is not an easy piece of cake. In this post I will show you 4 different problems you may encounter, and propose possible solutions. Continue Reading →
I wrote all posts published this year about Apache Kafka (NIO, max in flight requests) to better understand idempotent producers. In this post I'll try to do that before going further and analyze transactions support. Continue Reading →
I've written a lot about data sources, including Apache Kafka. However, Apache Spark is not only about sources but also about targets called sinks. In this post I will focus on Apache Kafka sink integration and try to answer some question in FAQ mode. Continue Reading →
At first glance, managing users access in PostgreSQL is easy, you simply execute a CREATE USER, give him some grants, assign a role, and often that's all. However, after some time "permission denied" errors can appear as new objects are created and not owned by the user. To mitigate the maintenance burden for that case, PostgreSQL proposes ALTER DEFAULT privileges operator. Continue Reading →
I didn't plan to write this post at all. However, when I was analyzing the idempotent producer, it was hard to understand out-of-sequence policy for multiple in-flight requests without understanding what this in-flight requests parameter really means. Continue Reading →
It's rare when in order to write a blog post I need to cover more than 3 other topics. But that's what happens with Apache Kafka idempotent producer that I will publish soon. But before that, I need to understand and explain NIO Selector, its role in Apache Kafka, and finally the in flight requests. Since the first topic was already covered, I will move to the second one. Continue Reading →
On the one hand, I appreciate JSON for its flexibility but also from the other one, I hate it for exactly the same thing. It's particularly painful when you work on a project without good data governance. The most popular pain is an inconsistent field type - Spark can manage that by getting the most common type. Unfortunately, it's a little bit trickier for less common problems, for instance when a same field has different case sensitivity. Continue Reading →
That's the next post I wrote after my unsuccessful analysis of Apache Kafka source. When I was reading the part responsible for sending requests to the broker, I found that it was partially managed by a Java package that I've never seen before. And that will be the topic of this post. Continue Reading →
