Speaker: Roksolana Diachuk

The complexity of tools, distributed systems, and the CAP theorem introduce tradeoffs that practitioners cannot avoid or ignore as they embrace the world of modern data pipelines. What strategies can you employ? This is where data products come into play. Understanding the business objectives of data products helps us make informed decisions about tools, architecture, and services. Join this panel to learn from data thought leaders! 

There are various tasks that the modern data pipelines approach helps us solve in different domains, including advertising. Modern data pipelines allow us to process data in a more efficient manner with a diverse set of data transformation tools for both batch and streaming data processing. AdTech is a traditional industry that constantly changes and innovates. Today, it draws a lot of attention as we’re expanding the reach and movement toward a cookieless world.  

In this talk, you will learn how to use modern data pipelines for reporting and analytics, as well as the case of historical data reprocessing in AdTech. We’ll dive deeper into each case, exploring the problem itself, implementation, challenges, and future improvements. In cases like business rule changes or errors in past data, we need to re-process our historical data, and it’s not a trivial task as it requires a lot of time, precision and computational resources for each step. Due to this, a whole section of the talk will be devoted to approaches to historical data reprocessing and data lifecycle management.