Unlocking the Value of Your Big Data with Dundas BI Visual Analytics – Webinars

Learn how a modern data warehouse increases data accessibility and provides seamless data analytics for Petabyte scale data.

Artificial intelligence (AI) and big data technologies are driving major changes in how organizations think about business intelligence and analytics. Rather than be limited to querying and reporting on just what is in traditional BI systems or data warehouses, many business users and analysts want to tap a fuller range of data in systems running Apache Hadoop, Apache Spark, or on cloud data platforms and storage. At the same time, AI practices and technologies (in particular machine learning and natural language processing) are changing how users explore, analyze, and interact with data and the types of insights they can generate.

When organizations need to move quickly to launch new, digitally transformed applications that rely on terabytes (if not petabytes) of data, they cannot afford to wait for legacy database management systems to catch up. Cloud computing platforms give organizations the ability to stand up systems quickly, but if the data layer cannot offer the linear scalability, low latency, high availability, performance, fault tolerance, security, and agility needed for today’s 24/7 applications, organizations will never realize their objectives.

A great comfort with cloud deployment has emerged. Businesses are migrating databases to the cloud or building databases there as a result of scale challenges with the on-premises model, the cloud becoming the “center of gravity”, on-premises databases reaching capacity or emerging uses cases that are specific to the cloud. But not all organizations! And some struggle mightily with the move!