NoSQL Databases & Docker Containers: From Development to Deployment

The number of sources generating continuous, streaming data has exploded in recent years. From website clickstream data to telemetry data from Internet of Things (IoT) devices, the variety, volume, and velocity of data continues to increase. In response, businesses are evolving their analytics approach from batch to real time, and turning to new tools to deliver actionable insights in seconds instead of hours or days. With AWS, you can easily and cost-effectively collect, process, and analyze real-time streaming data at any scale, so you can learn what your applications and customers are doing right now—and respond immediately.

As organizations strive to be more competitive, they often need real-time insights; no one wants to make decisions based on stale data. TDWI research indicates that real-time data collection is already in the mainstream. Some use cases include inventory management, fulfillment, supply chain, and logistics in which retailers must be able to assess product availability and consumer demand in real time. Forward-looking organizations also want to enrich real-time data with other data types to provide even better analytics.

Staying competitive in today’s economy requires that organizations derive intelligent insights from data and make their processes automated, intelligent, adaptive, and self-optimizing. This is where artificial intelligence (AI) and machine learning (ML) play a central role in business transformation.

Are you planning to implement a data governance program? What are the things that practitioners often overlook or underestimate, and what are the most common mistakes they make? Join us to hear from Katherine Fraser, CEO of 1 to learn more about the key components of a successful data governance program, common mistakes made and to avoid when implementing a data governance program, and tips for maintaining a successful data governance program.