Data Engineering Best Practices

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Presented by

Suraj Archarya Director, Engineering . Singh Garewal, Director Marketing

About this talk

Making quality data available in a reliable manner is a major determinant of success for data analytics initiatives be they regular dashboards or reports, or advanced analytics projects drawing on state of the art machine learning techniques. Data engineers tasked with this responsibility need to take account of a broad set of dependencies and requirements as they design and build their data pipelines. Join Suraj Acharya, Director, Engineering at Databricks, and Singh Garewal, Director of Product Marketing, as they discuss the modern IT/ data architecture that a data engineer must operate within, data engineering best practices they can adopt and desirable characteristics of tools to deploy. In this webinar you will learn: - A framework for describing the modern data architecture - Best practices for executing data engineering responsibilities - Characteristics to look for when making technology choices

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Databricks’ mission is to accelerate innovation for its customers by unifying Data Science, Engineering and Business. Founded by the team who created Apache Spark™, Databricks provides a Unified Analytics Platform for data science teams to collaborate with data engineering and lines of business to build data products. Users achieve faster time-to-value with Databricks by creating analytic workflows that go from ETL and interactive exploration to production. The company also makes it easier for its users to focus on their data by providing a fully managed, scalable, and secure cloud infrastructure that reduces operational complexity and total cost of ownership.