David is working as a Data Engineer on Get Your Guide’s Data Platform Team where he focuses on serving internal customers with high-quality, low-latency data products. He has over 5 years of experience in Business Intelligence and Data Engineering roles from the Berlin e-commerce scene. He enjoys developing data pipelines that are easy to maintain, test and evolve, and has a keen interest in functional programming.
October 15, 2019 05:00 PM PT
In this talk we'll present how at GetYourGuide we've built from scratch a completely new ETL pipeline using Debezium, Kafka, Spark and Airflow, which can automatically handle schema changes. Our starting point was an error prone legacy system that ran daily, and was vulnerable to breaking schema changes, which caused many sleepless on-call nights. As most companies, we also have traditional SQL databases that we need to connect to in order to extract relevant data.
This is done usually through either full or partial copies of the data with tools such as sqoop. However another approach that has become quite popular lately is to use Debezium as the Change Data Capture layer which reads databases binlogs, and stream these changes directly to Kafka. As having data once a day is not enough anymore for our business, and we wanted our pipelines to be resilient to upstream schema changes, we've decided to rebuild our ETL using Debezium.
We'll walk the audience through the steps we followed to architect and develop such solution using Databricks to reduce operation time. By building this new pipeline we are now able to refresh our data lake multiple times a day, giving our users fresh data, and protecting our nights of sleep.