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December 1, 2020

AWS Unveils Batch of Analytics, Database Tools


A trio of analytics tools announced by Amazon Web Services are aimed at improving the performance of the cloud vendor’s Redshift data warehouse by easing data movement across different storage platforms.

The analytics tools along with a serverless configuration tool for its Aurora database were among the first wave of AWS product announcements during this week’s virtual re:Invent gathering.

The analytics package unveiled Tuesday (Dec. 1) includes AQUA, for Advanced Query Accelerator, designed to speed Redshift queries via “hardware-accelerated cache.” Another tool branded as Glue Elastic Views allows developers to draw on scattered data to build applications. The tool provides materialized views used to combine and replicate data across storage, warehouses and databases.

The third piece, called Amazon QuickSight Q, is a machine learning-based tool that allows natural language expressions to be used for business queries.

AQUA is aimed at overcoming performance and scaling bottlenecks as data movement increases between computing nodes and shared data stored in Amazon Redshift cloud instances. Data movement hogs available networking bandwidth, thereby eroding performance.

Hence, AQUA is positioned as a distributed and hardware-accelerated cache for boosting analytics performance on Redshift. The query accelerator “brings compute to the storage layer, so data does not have to move back and forth between the two,” AWS noted.

The cache upgrade utilizes parallel processing across computing nodes to deliver what the cloud giant (NASDAQ: AMZN) claims is a 10-fold increase in its cloud data warehouse.

AQUA is currently in preview with all AWS customers, and is scheduled to be generally available in January 2021.

Glue Elastic Views is a serverless data preparation service designed to help developers build materialized views, or virtual tables, that combine and replicate data across multiple storage platforms. That capability is intended to ease extract, transform and load jobs for analytics and machine learning workloads.

Developers can use SQL to create a materialized view of data combined from different data stores. The tool then copies selected data to create the materialized view of widely dispersed data.

AWS said the new data preparation service is available now in preview.

“We’re delivering an order-of-magnitude performance improvement for Amazon Redshift, new flexible ways to more easily move data between data stores and the ability for customers to ask natural language questions in their business dashboards and receive answers in seconds,” said Rahul Pathak, vice president for analytics at AWS.

Separately, AWS said a new version of its Amazon Aurora Serverless platform used to migrate from SQL Server to Amazon Aurora is also available for preview.