There is a growing concern over the complexity of data analysis as data volume, velocity, and variety increases. The concern stems from the number and complexity of steps it takes to get data to a usable state. Often data engineering teams spend most of their time building and optimizing extract, transform, and load (ETL) pipelines. In this session, we walk you through how to create a fully automated data cataloging and ETL pipeline to transform data, which can greatly reduce your or your customers’ time to value and cost of operations.
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Aditya (Adi) is an AWS Solutions Architect who has been with AWS since February 2018. Adi has over 15 years of experience in architecting, designing, building and implementing IT solutions for various verticals including academic, financial and fundraising organizations. Adi has all 13 AWS certifications that are currently available and he’s passionate about cloud technologies, especially AWS.