Cloud native data management solution for cloud data warehouses, data lakes and lakehouses

Informatica will showcase the Cloud Native Data Management solution at its Intelligent Data Summit for Cloud Data Warehouses, Data Lakes & Lakehouses, the second event in its free, virtual CLAIREview series. The event will kick off with a keynote at 10:00 a.m. PDT with Jitesh Ghai, Senior Vice President, Data Management, Informatica; Mark Beyer, Distinguished VP Analyst, Gartner; Rahul Pathak, General Manager of Analytics, AWS; Sumeet Agrawal, Senior Director, Product Management, Informatica; James Newsom Jr., Managing Director of Data Services, Home Point Financial; and Christopher Eldredge, Senior Director of Business Intelligence, Paycor.

“It’s more critical than ever to deliver rapid business impact from digital transformation initiatives,” said Ghai. “But companies often struggle to see ROI from their cloud data warehouse and data lake investments. Informatica Cloud Native Data Management provides the foundation critical to successfully delivering on businesses’ top priority transformations, while solving common challenges with automation and intelligence to shorten the time to value from cloud data warehouse and data lake investments.”

Most organisations point to a lack of sufficient data integration, data quality, and metadata management as the chief barriers to succeeding with their cloud data warehouses and data lakes. According to Ghai, there are three common reasons organisations fail to maximise value from cloud analytics.

“Using hand coding to address data integration, data quality, and metadata management issues is one of the biggest reasons we see organisations struggle,” said Ghai. “This approach is costly and time-intensive, hampering the enterprise’s ability to innovate swiftly and putting the project’s long-term success at risk. We also see organisations depending on disjointed point products to achieve end-to-end data management, which can lead to inconsistent data governance and quality, and relying on limited solutions from cloud vendors that only offer basic data integration or ingestion, which doesn’t suffice. It’s critical that modern enterprises pursue end-to-end cloud-native data management – including the three pillars of data: metadata management, data integration, and data quality – that supports a multi-cloud strategy and deployment model by design.”

Informatica Cloud Native Data Management is the industry’s only enterprise-class, end-to-end data management solution for lakehouses – as well as data warehouses and data lakes.

Built on the industry leading Informatica Intelligent Cloud Services (IICS), the most advanced enterprise iPaaS (Integration Platform as a Service), the Informatica Cloud Native Data Management solution combines best-of-breed data integration, data quality, and metadata management.

The cloud-native solution is completely automated and has advanced metadata-driven AI capabilities. It addresses the many complex data management challenges facing businesses today.

New intelligence and automation capabilities of Informatica Cloud Native Data Management unveiled at today’s Intelligent Data Summit for Cloud Data Warehouses, Data Lakes & Lakehouses will enable organisations to:

• Automate cloud mass ingestion for files, databases (including change data capture), and streaming with intelligent schema drift functionality.
• Reuse existing workloads in the cloud with minimal disruption, together with enterprise data cataloging, to provide detailed lineage and impact analysis and prioritise datasets and workloads for migration through a comprehensive understanding of the data landscape.
• Ensure trustworthy data in cloud data warehouse and data lake solutions through cloud data quality and metadata management together with cloud data integration and cloud application integration on a modern, microservices-based, cloud-native platform with serverless computing.
• Automate end-to-end data management with AI/machine learning to build and tune data integration jobs and detect anomalies.
• Operationalise data pipelines and machine learning in the cloud with DataOps and MLOps for continuous integration (CI) and continuous deployment (CD).
• Save costs and improve performance of running data management workloads using intelligent pushdown processing to cloud data warehouses and Spark with a serverless run time engine.