![]() ![]() “The new capabilities announced today help us move customers toward a zero-ETL future on AWS, reducing the need to manually move or transform data between services. Many of our customers rely on multiple AWS database and analytics services to extract value from their data, and ensuring they have access to the right tool for the job is important to their success,” said Swami Sivasubramanian, vice president of Databases, Analytics, and Machine Learning at AWS. “The vastness and complexity of data that customers manage today means they cannot analyze and explore it with a single technology or even a small set of tools. To learn more about unlocking the value of data using AWS, visit /data. Together, these new capabilities help customers move toward a zero-ETL future on AWS. Customers can also now run Apache Spark applications easily on Amazon Redshift data using AWS analytics and machine learning (ML) services (e.g., Amazon EMR, AWS Glue, and Amazon SageMaker). Today’s announcement enables customers to analyze Amazon Aurora data with Amazon Redshift in near real time, eliminating the need to extract, transform, and load (ETL) data between services. ![]() company (NASDAQ: AMZN), today announced two new integrations that make it easier for customers to connect and analyze data across data stores without having to move data between services. LAS VEGAS-(BUSINESS WIRE)- At AWS re:Invent, Amazon Web Services, Inc. Amazon Aurora zero-ETL integration with Amazon Redshift enables customers to analyze petabytes of transactional data in near real time, eliminating the need for custom data pipelinesĪmazon Redshift integration for Apache Spark makes it easier and faster for customers to run Apache Spark applications on data from Amazon Redshift using AWS analytics and machine learning services
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