Data Engineering is th e newest in-demand role for modern data-driven and analytics focused organi zations. One industry analyst recently described research showing twelve ti mes as many unfilled data engineer jobs as data scientist jobs. Data e ngineers are, in many ways, a unique combination of software engineers and database engineers. They are technical designers and developers, much like software engineers, but with different objectives and challenges. They are modelers and developers, much like database engineers, but again with new c hallenges. The variety, scope, and complexity of modern data - ne w data types, NoSQL databases, smart data management technologies, etc . - make data engineering a complex field that encompasses a wide rang e of disciplines from architecture to coding and testing. Understanding the scope and complexity of data engineering is essential both for data engine ers and for those who work with them.
\nYou Will Learn:
\n\n What data engineering is and why it is essential for analytics-driven organ izations\n How data engineering is similar to software engineering and how it is diffe rent\n How data engineering is similar to database engineering and how it is diffe rent\n The kinds of deliverables that data engineering produces\n The range of skills - architectural, modeling, programming, and technologie s - that are essential to data engineering\n Core data engineering processes, including pipeline design/development and data services design/development\n Data engineering life cycles and methods including agile and DevOps\n\n DTSTART:20181008T130000 SUMMARY:Data Engineering Concepts and Principles DTEND:20181008T161459 LOCATION: See Description END:VEVENT END:VCALENDAR