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After my January's talk about Apache Kafka integration in Structured Streaming I got an interesting question on off. The question was, how to process 2 topics simultaneously with Structured Streaming? The "small" problem was the fact that both had different schemas. Continue Reading →
Some time ago I wrote a blog post about output invalidation pattern using immutable time-based tables. Today, even though I planned to start to explore new ACID-compliant file formats only by the end of this year, I decided to cheat a little (curiosity beat me) and try to adapt the pattern to one of these formats and use time travel feature to guarantee data consistency. Continue Reading →
The next thing I wanted to understand while still working on transactions was the lookup. I can imagine how to get the first or the last element of a partition but I had no idea how it can work for more fine-grained access, like the one using timestamp. Continue Reading →
_SUCCESS file generated by Apache Spark SQL when you successfully generate a dataset, is often a big question for newcomers. Why does the framework need this file? How is it generated? I will cover these aspects in this article. Continue Reading →
Few weeks ago when I was preparing a talk for one local meetup, I wanted to list the most common operations we can do with Spark for the newcomers. And I found one I haven't used before, namely sortWithinPartitions. Continue Reading →
When I started to work with data on my very first PHP and Java projects, I used only DELETE operator to remove the data. When I switched to (big) data engineering, I found more efficient ways to deal with this operation through TRUNCATE or DROP operations. Continue Reading →
Poor data quality is the reason for big pains of data workers. Data engineers need often to deal with JSON inconsistent schemes, data analysts have to figure out dataset issues to avoid biased reportings whereas data scientists have to spend a big amount of time preparing data for training instead of dedicating this time on model optimization. That's why having a good tool to control data quality is very important. Continue Reading →
Some time ago I watched an interesting Devoxx France 2019 talk about poison pills in streaming systems presented by Loïc Divad. I learned a few interesting patterns like sentinel value that may help to deal with corrupted data but the talk was oriented on Kafka Streams. And since I didn't find a corresponding resource for Apache Spark Structured Streaming [and also because I'm simply an Apache Spark enthusiast ;)], I decided to write one trying to implement Loïc's ideas in the Structured Streaming world. Continue Reading →
Last November I spoke at Paris.py meetup about integrating Cerberus with PySpark to enhance JSON validation. During the talk, I covered some points that I would like to share with you in this blog post, mostly about error definition and normalized validation. Continue Reading →
From time to time I try to help other people on StackOverflow and one of my tagged topics is Apache Airflow. In this blog post I'll try to show you some problems I saw there last few months. Continue Reading →
When I came to the data world, I had no idea what the data governance was. One of the tools which helped me to understand that was AWS Glue. I had a chance to work on it again during my AWS Big Data specialty exam preparation and that's at this moment I asked myself - "DynamicFrames?! What's the difference with DataFrames?" In this post I'll try to shed light on it. Continue Reading →
I didn't know that join reordering is quite interesting, though complex, topic in Apache Spark SQL. The queries not only can be transformed into the ones using JOIN ... ON clauses. They can also be reordered accordingly to the star schema which we'll try to see in this post. Continue Reading →
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 →
