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At the ongoing AWS re:Invent conference, the company focused on empowering enterprise users – from departmental users and developers to data scientists and engineers – with capabilities to quickly find, populate, govern and analyze data in the cloud and move from ingestion to insights in a matter of minutes.
Here’s a quick rundown of everything it announced.
Data loader for Amazon Redshift
First up, Informatica said that its data loader, which is designed to offer a simple, no-cost way to load data into cloud data warehouses, will now come directly embedded in the Amazon Redshift console.
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Redshift is a fully managed, petabyte-scale cloud data warehouse, and with this move, it will offer enterprises the ability to launch the data loader in a few clicks, quickly capturing data from AWS, on-premises, legacy systems, third-party applications and other sources, and ingesting it into the data warehouse. It also offers a guided interface to help companies load and combine data in their data warehouse for easy access to holistic insights from data.
Support for AWS data exchange
Next, the company announced support for AWS data exchange. As part of this, Informatica said its data marketplace will package not only enterprises’ first-party datasets but also third-party datasets from AWS data exchange, giving them a broader set of data products to work on for faster self-service analytics and other applications. The AWS data exchange is already home to more than 3,500 data products and more than 300 data providers.
INFACore supports Amazon SageMaker Studio
First announced earlier this year, Informatica INFACore is a plugin that simplifies the development and maintenance of complex data pipelines by turning thousands of lines of code into a single function that can be deployed in applications using a native UI. Now, this UI is supported on Amazon’s SageMaker Studio, a fully integrated development environment (IDE) for machine learning.
According to Informatica, enterprises can use INFACore to access and prepare data from any source directly on Amazon SageMaker Studio. Once it’s ready, data scientists, data engineers and developers don’t have to switch platforms. They can continue using SageMaker to build, train, tune, debug and deploy their ML models, cutting down the time to value from weeks to a few hours.
“We are thrilled to extend our cloud work with AWS to simplify how departmental users process, manage and, more importantly, make smarter decisions with trusted data,” Jitesh Ghai, chief product officer at Informatica, said. “The strategic investments we are making in integrating the Informatica cloud platform on AWS will continue to drive the success we have had with joint customers in helping them drive positive business outcomes with their data.”
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