English | Size: 1.25 GB
Genre: eLearning
Successful data-driven organizations need access to high-quality data. Analytics engineering—an amalgam of data engineering and data analysis—supports this critical requirement by transforming data and by participating in testing, deployment, and documentation. As a discipline, analytics engineering has evolved from providing clean datasets to end users to enabling them to answer their own questions about that data. These expert-led sessions will get you up to speed on this quickly evolving field and take you through the tools and technologies at the forefront of data transformation.
About the Data Superstream Series: This three-part Superstream series is designed to help your organization maximize the business impact of your data. Each day covers different topics, with unique sessions lasting no more than four hours. And they’re packed with insights from key innovators and the latest tools and technologies to help you stay ahead of it all.
What you’ll learn and how you can apply it
Explore the future of analytics engineering and learn how to prepare yourself and your organization for this transformation
Learn the five kinds of work that data teams are taking over because of the democratization of analytics engineering
See how to apply new approaches to datasets through the lens of an analytics engineer, from ingestion to exploration to modeling and presentation
Simplify data discovery at your organization through testing strategies, code review processes, naming conventions, and more
Discover the metrics layer—a new piece of the modern data stack maintained by analytics engineers
Explore the landscape of data orchestration tools within the modern data stack and learn how to implement them
This recording of a live event is for you because…
You’re a data practitioner looking to understand engineering analytics—one of the hottest new areas in data today.
You’re a data analyst, BI analyst, or data warehouse developer who wants to make the move to a career in engineering analytics.
You’re an analytics engineer who wants to deepen your knowledge of the space and explore new tools and methodologies for the modern data stack.
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