Security News > 2020 > December > AWS unveils three analytics capabilities to improve Amazon Redshift performance

AWS unveils three analytics capabilities to improve Amazon Redshift performance
2020-12-03 02:00

Amazon Web Services announced three new analytics capabilities that dramatically improve the performance of Amazon Redshift data warehouses, make it significantly easier for customers to move and combine data across data stores, and make it much simpler for end-users to get more value from their business data using machine learning.

AWS customers use a wide variety of analytics tools for different use cases, including Amazon Athena for serverless querying, Amazon Elasticsearch Service for searching and visualizing log data, Amazon Kinesis for processing real-time data streams, Amazon Redshift for data warehousing, and Amazon EMR for running Apache Spark, Hive, Presto, and other big data frameworks.

"Since migrating to Amazon Redshift in 2014, Amazon Redshift has been the center of our analytics environment and has allowed us to scale to over ten petabytes of uncompressed data with a 10x performance improvement over our prior on-premises system," said Ken Ohta, General Manager of Service Innovation Department, NTT DOCOMO. "As customer demand for data and data volumes grow, continuous innovation from Amazon Redshift has helped us with the flexibility and ease of use needed to scale our systems. We are excited about the launch of AQUA for Amazon Redshift as we continue to increase the performance and scale of our Amazon Redshift data warehouse."

"Our Amazon Redshift cloud data warehouse has made it easy to scale and stay on budget. We're excited about the new AQUA capabilities in Amazon Redshift which will accelerate our queries and reduce our analysts' time to insights. We know with AWS we can focus on our growth, without worrying about how technology will support it."

"With the introduction of RA3 instances for Amazon Redshift we were able to significantly improve the performance of analytical workloads while solving the data storage issue at the same time. We are really excited to explore AQUA for Amazon Redshift as it promises to further improve the performance of our most complex workloads by around 10x with no additional effort."


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