Data processing trends & traditions - pipelines, DWHs, AI feature stores

Imagine three data specialists in the room that do not understand each other, although they are all paid for the same job. First one is ETL/SQL Developer with DWH, the second one is a software engineer with pipelines in Java, and the third one is Data scientist with his/her R scripts. And it is not a joke. Should they learn from each other? Could they collaborate more? Get some inspiration from other best practices.
I will sort out some of the data related buzzwords - e.g. Machine Learning, OOP, ODS, Spark, DWH, Hadoop, CI/CD, Python, AI, Cloud, Agile and prototyping.

DATA SPECIALISTS ACHIEVEMENTS
*SW engineers* made the majority of the applications you use every day, so they probably know something. We all know companies like Google, Microsoft, Adobe, SAP and others, so guess why SW engineering is essential in IT?

*Data Scientists* (Statisticians and Mathematicians) are responsible for many great scientific discoveries, and they are a great asset to any data processing as they do this longer than any IT folk can imagine. Who saved London in 1850s from Cholera? There was no SW engineer or ETL Developer at that time, ask your grandGrandGrandGrandParents (and yes, with computers and location data it would be way faster - which is basically the idea of machine learning)

*ETL/SQL Developers* do most regulatory and accounting statements all over the world in enterprises. They probably manage your KPIs plus much more. The executives rely on them when it comes to business reporting and data-driven decisions, guess why?

AND WHY TO BOTHER WITH THIS?

Together we are much stronger than ever, and for the first time in history, we can collaborate. We need to put aside some of our prejudices and learn things like Python and Cloud computing. If you read until here, you should come.

This meetup well be presented by Honza Procházka from DataSentics - European Data Science Center of Excellence based in Prague. In his current mission of data engineering lead, he is helping implement data analytics with new cloud-based technologies, good old craftsmanship and common sense.

Language: English / Czech (depends on the audience)

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