Data engineering educator Andreas Kretz joins Jon Krohn for a 1-hour primer that covers everything you need to know about the most in-demand role in data. From skills to tools, problem-solving processes and more, growing your knowledge of data engineering only improves your marketability, so tune in today if you're ready to future-proof your data career.

This episode is brought to you by Glean (https://glean.io), the platform for data insights fast, and by epic LinkedIn Learning instructor Keith McCormick (https://linkedin.com/learning/instructors/keith-mccormick). Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

In this episode you will learn:
• Why learn data engineering? [06:55]
• What is data engineering? [08:08]
• What sets Senior Data Engineers apart from junior ones? [13:57]
• The must-know data-engineering tools [20:26]
• The right path to learn data engineering [44:24]
• Are certifications worth it? [51:46]
• The future of data engineering [55:24]
• Andreas's career challenges [58:48]

Additional materials: www.superdatascience.com/657

The Super Data Science Podcast: Machine Learning | AI | Success

657: How to Learn Data Engineering

The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast.

As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact.

Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy.

We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.

New tracks tagged #Data Engineering Tools