Modernizing Data Integration for Cloud Data Warehouses and Lakes
Webinar Speaker: Philip Russom, Senior Research Director for Data Management
Date: Tuesday, January 28, 2020
Time: 9:00 a.m. PT, 12:00 p.m. ET
We all know that data warehouses need to be modernized to keep pace with emerging technologies, evolving data, and innovative business practices. However, don’t forget that user organizations also need to update the systems that contribute to warehouse success, including the tools, architectures, and best practices of data integration.
A number of trends are driving the modernization of data integration:
- Cloud. As a growing number of data warehouses and lakes migrate in whole or in part to cloud and more software-as-a-service (SaaS) applications come online, data integration tools must more deeply support the unique interfaces and functional capabilities of cloud-based data platforms and SaaS apps.
- New data types and sources. Firms involved in the Internet of Things (IoT), multichannel marketing, the digital supply chain, and multicloud SaaS apps are experiencing many new data types from many new sources. Modern data integration can pull all this and more together for comprehensive analytics and operations.
- Data-driven business innovations. Modern data integration can provision the data required for new business practices in self-service data access, predictive analytics, business monitoring, data sync across clouds and apps, and real-time business management.
Attendees will learn about:
- How established skills and experience still apply to modern data integration in the cloud, but with some necessary tool upgrades and practice adjustments
- Why data integration techniques—from traditional ETL to modern data prep—are more relevant than ever for enabling data’s journey across today’s multiplatform, cloud, and hybrid architectures, as well as migrating data to new platforms
- How data integration with cloud systems differs (and doesn’t) compared to on-premises systems
- The new capabilities of vendor-built or open-source data integration tools, such as pipelining, orchestration, transformation, and improved metadata management
- Real-world use cases for modern data integration in data warehousing and analytics, as well as operations and business process monitoring
Philip Russom, Ph.D.