97 Things Every Data Engineer Should Know (1 ed)

Take advantage of today's sky-high demand for data engineers. With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Contributors from notable companies including Twitter, Google, Stitch Fix, Microsoft, Capital One, and LinkedIn share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges.

Edited by Tobias Macey, host of the popular Data Engineering Podcast, this book presents 97 concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data. Data engineers, data architects, data team managers, data scientists, machine learning engineers, and software engineers will greatly benefit from the wisdom and experience of their peers.

Topics include:

• The Importance of Data Lineage – Julien Le Dem
• Data Security for Data Engineers – Katharine Jarmul
• The Two Types of Data Engineering and Data Engineers – Jesse Anderson
• Six Dimensions for Picking an Analytical Data Warehouse – Gleb Mezhanskiy
• The End of ETL as We Know It – Paul Singman
• Building a Career as a Data Engineer – Vijay Kiran
• Modern Metadata for the Modern Data Stack – Prukalpa Sankar
• Your Data Tests Failed! Now What? – Sam Bail