{"id":2865,"date":"2021-03-29T14:26:34","date_gmt":"2021-03-29T18:26:34","guid":{"rendered":"https:\/\/solutionsreview.com\/data-management\/?p=2865"},"modified":"2021-03-29T14:26:44","modified_gmt":"2021-03-29T18:26:44","slug":"informatica-introduces-new-nvidia-powered-data-integration-engine","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/data-management\/informatica-introduces-new-nvidia-powered-data-integration-engine\/","title":{"rendered":"Informatica Introduces New NVIDIA-Powered Data Integration Engine"},"content":{"rendered":"
\"Informatica

Source: Informatica<\/p><\/div>\n

Informatica recently announced<\/a> a new, serverless, Spark-based data integration engine that features improved performance using the NVIDIA RAPIDS Accelerator for Apache Spark. For the first time, Informatica explains “users have access to end-to-end machine learning operations (MLOps) capabilities by operationalizing machine learning models, and to the power of data management with the scalability and speed delivered by RAPIDS data science software and NVIDIA infrastructure.” The release comes on the heels of Informatica’s January launch<\/a> of SaaS MDM.<\/p>\n

Our Buyer’s Guide for Data Management Platforms<\/em><\/a> helps you evaluate the best solution for your use case and features profiles of the leading providers, as well as a category overview of the marketplace.<\/div><\/p>\n

Informatica\u2019s big data management platform allows organizations to access, integrate, clean, master, govern and secure big data. The tool features purpose-built connectors to hundreds of data sources, real-time streaming, and mass ingestion. Informatica\u2019s visual developer interface also ensures that the best open-source platforms can be adopted without sacrificing usability. Public cloud support for Big Data Management is available on AWS and Microsoft Azure.<\/p>\n

The Informatica NVIDIA data integration engine features increased data processing speeds of up to five times, letting customers hasten their data management workloads and operationalize machine learning. Accelerated computing is used by Informatica to improve the performance of compute-intensive AI and machine learning workloads.<\/p>\n

In a media statement about the news, Informatica Chief Product Officer Jitesh Ghai said “You can\u2019t leverage the power of data and gain valuable insights if you are restricted in your data access. Our collaboration with NVIDIA is valuable to us in bringing enterprise-scale data democratization and narrowing the gap between the data-haves and the data-have-nots within the enterprise. This important milestone with NVIDIA shows our continued commitment to unlock the value of data embedded in organizations across all levels and more importantly empower all key users to gain faster business-critical insights and operationalize data analytics and data science projects at scale.”<\/p>\n

Visit Informatica’s website<\/a> to learn more.<\/p>\n

<\/div><\/p>\n

\t\t\t
\"Download<\/a><\/div>\n\t\t<\/div><\/div><\/p>\n","protected":false},"excerpt":{"rendered":"

Informatica recently announced a new, serverless, Spark-based data integration engine that features improved performance using the NVIDIA RAPIDS Accelerator for Apache Spark. For the first time, Informatica explains “users have access to end-to-end machine learning operations (MLOps) capabilities by operationalizing machine learning models, and to the power of data management with the scalability and speed […]<\/p>\n","protected":false},"author":23,"featured_media":2870,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[130,995,996],"acf":[],"yoast_head":"\nInformatica Introduces New NVIDIA-Powered Data Integration Engine<\/title>\n<meta name=\"description\" content=\"Informatica announced a serverless, Spark-based integration engine that features improved performance using the NVIDIA RAPIDS Accelerator for Apache Spark.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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