Understand the two dimensions of scaling for database query and ingest workloads, and how sharding can make scaling elastic — or not. Scaling throughput and performance are critical design topics for all distributed databases, and sharding is usually a part of the solution. However, a design that increases throughput does not always help with performance and vice versa. Even when a design supports both, scaling them up and down at the same time is not always easy.

Much like wine (😉), having data doesn’t mean you have quality data. Today it's easier than ever to get data on almost anything. But that doesn’t mean that data is inherently good data, let alone information or knowledge that you can use. In many cases, bad data can be worse than no data, and it can easily lead to false conclusions. So, how do you know that your data is reliable and productive? This is what we call data quality.

Every day, businesses monitor system resources for performance, security, performance, and workflows. Otherwise, they jeopardize day-to-day operations when issues go unnoticed. Tableau presents itself as a data-driven monitoring tool that enhances data analysis of physical and virtual server environments. But just how good is it?