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Apache Airflow Big Data algorithms Big Data problems - solutions Data engineering patterns Databricks General Big Data General data engineering Graphs SQL
If not, below you can find all articles belonging to Data engineering.
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.
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.
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.
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.
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.
My last slides of Spark Summit 2019 were dedicated to an output invalidation pattern that is very useful to build maintainable data pipelines. In this post I will deep delve into it.
Do you imagine a world where everybody speaks the same language? It's difficult. Fortunately, it's much easier to do in data engineering where a single API can apply to batch and streaming processing.
One of patterns that you may implement in batch ETL is sequential execution. It means that the output of one job execution is a part of the input for the next job execution. Even though Apache Airflow comes with 3 properties to deal with the concurrence, you may need another one to avoid bad surprises.
Even data distribution is one of the guarantees of performant data processing. However, it's not a golden rule and sometimes you can encounter uneven distribution called skews.
CASE operator is maybe one of the most unknown by the beginner users of SQL. Often when I see a question how to write an if-else condition in a SQL query, some people advise to write a UDF and use if-else directly inside. As you will see in this post, this solution is a little bit overkill though.
Years ago when I started to work as a software engineer, I was overusing IN/NOT IN operator. One day, one of my colleagues suggested me to replace it in some queries by EXISTS/NOT EXISTS. And it helped to improve the performances of these queries. If among you are some people like "me years ago", I prepared this short post introducing to EXISTS/NOT EXISTS operator by comparing it to IN/NOT IN one.
This next post about data engineering patterns implemented came to my mind when I saw a question about applying custom partitioning on a not pair RDD. If you don't know, it's not supported and IMO one of the reasons for that comes from the dataset decomposition pattern implementation in Apache Spark.
Unit tests are the backbone of any software, data-oriented included. However testing some parts that way may be difficult, especially when they interact with the external world. Apache Airflow sensor is an example coming from that category. Fortunately, thanks to Python's dynamic language properties, testing sensors can be simplified a lot.
Some time ago I found an article presenting ETL patterns. It's quite interesting (link in "Read more" section) but it doesn't provide code examples. That's why I will try to complete it with the implementations for presented patterns in Apache Airflow.
In one of my previous posts, I described orchestration and coordination in the data context. At the end I promised to provide some code proofs to the theory and architecture described there. And that moment of truth is just coming.
In my previous post I presented an implementation of idempotent consumer pattern with Apache Cassandra CDC. One of drawbacks of that solution was the necessity of producing the messages with slower lightweight transactions. In this post I will show you how to do the same with AWS DynamoDB streams and without that constraint.
KISS principle is valid not only for software engineering but also for data pipelines. The pattern called Complex Logic Decomposition illustrates this pretty well.
Recently I wrote posts about idempotent consumer pattern analyzing Apache Camel implementation and CDC applied on NoSQL stores. After that I had an idea, what happened if we would mix both of them?
After several weeks of inactivity, the series about data engineering patterns is back. In this resume's article, I will present a pattern called dataset reduction.
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.
All in one! Ebook with data engineering design patterns on AWS, Azure, and GCP
