AI and machine learning influence becomes more apparent with latest analytics releases by AWS
One of the messages coming from nearly two weeks of announcements as part of Amazon Web Services Inc.’s re:Invent conference is that artificial intelligence and machine learning are ready to abstract away a lot of manual work involved in moving data around the enterprise IT infrastructure.
Two products unveiled by AWS in December showcase this trend. AWS Glue Elastic Views reduces the time needed to replicate across data stores, monitors for changes, and then automatically updates the target when adjustments are made.
Amazon QuickSight Q is a machine learning-powered solution that uses natural language processing to answer business questions instantly.
“The idea is to make it easier for customers to combine and use data from a variety of different sources,” said Rahul Pathak (pictured), vice president of analytics at AWS. “Any time we can do work so our customers don’t have to, that’s a win for both of us.”
Pathak spoke with Dave Vellante, host of theCUBE, SiliconANGLE Media’s livestreaming studio, during AWS re:Invent. They discussed how the two solutions leverage automation to improve IT efficiency and additional enhancements for the AWS analytics portfolio. (* Disclosure below.)
Less need for custom code
The rollout of AWS Glue Elastic Views is based on the combination of three solutions. The “glue” is AWS’ extract, transform and load serverless technology and data integration service. It’s “elastic” because it can scale up or down to deal with changes based on customer need. And it provides “views” by defining virtual tables using SQL.
“Before AWS Glue Elastic Views, customers would have to use either ETL or data integration software or they’d have to write custom code that could be complex to manage and could be error prone,” Pathak said. “You can now use SQL to define a view across multiple data sources, pick one of many targets, and then the system will monitor the sources for changes and promulgate them into the targets in near real time.”
Amazon QuickSight Q’s natural language processing solution offers a query function that intuits relationships between the entities in data so that it is able to reason about the questions asked. Asking about the top five sales categories in California, for example, will yield one set of metrics, and then a second query about sales in New York will prompt an overlay of both sets of data because the tool understands that a comparison may be desired.
The natural language solution can also be embedded into applications, according to Pathak.
“Blackboard is embedding Amazon QuickSight Q dashboards into information it is providing to thousands of educators to provide data on the effectiveness of online learning,” Pathak said. “It’s a way to give a broad set of people the ability to ask questions of data without requiring them to be fluent in things like SQL.”
Providing unified governance
It has been a busy year for the analytics side of AWS. In addition to AWS Glue Elastic Views and Amazon QuickSight Q, the company rolled out a number of enhancements for its analytics portfolio, which were summarized by Pathak in a re:Invent session and during his interview with theCUBE.
“Customers are looking for unified governance, and that’s why we built AWS Lake Formation,” Pathak said. “It can quickly discover and catalog customer data assets, and it allows customers to define granular access policies centrally around that data. By creating this fine-grained but unified governance model, this actually sets customers free to get broader access to the data because they know their policies and compliance requirements are being met and it gets them out of the way of the analyst.”
Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of AWS re:Invent. (* Disclosure: Amazon Web Services Inc. sponsored this segment of theCUBE. Neither AWS nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)
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