Make IoT Pay Off with Big Data Streaming Analytics

Your team is serious about ensuring database performance at scale. But legacy NoSQL technology could be eroding the impact of your achievements. Following best practices for efficient data modeling, query optimization and observability is fundamental. But their power can be limited – or enhanced – by specific database capabilities. Often-overlooked database innovations can serve as a force multiplier, paving a much smoother path to speed at scale (e.g., millions of read/write operations and millisecond P99 response).

When evaluating a data catalog, every data engineer wants to know one thing: “What’s in it for me?” In this live demo, Mo Dodge answers that question and highlights several high-value use cases data.world solves for:

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!

Much like project management and home improvements, Data Governance sounds a lot simpler than it actually is. In a nutshell, Data Governance can be explained as “managing data with guidance.” In general, the perceived utility of these programs increases with the specificity of desired data and processing improvements. Whether restarting or starting your Data Governance programs, it is critical to be guided by a periodically revised Data Strategy that links support for organizational strategy to specific operational data improvements. Understanding these and other aspects of governance is necessary to eliminate the ambiguity that often surrounds the implementation of effective Data Management and stewardship programs.