Infrastructure
Monitoring Doesn’t
Cut It Anymore
Effective data lake deployment requires one element that is often overlooked: deep understanding of the behavior and performance of each workload (set of queries that serves a business logic). Different workloads have different levels of impact on organizations’ business. Ranging from mission-critical, customer facing, internal dashboards, etc. Each workload, according to its priority, has its budget and the expected performance it requires.
Once organizations start to experiment with serving queries directly on the data lake, they are challenged with the transition from storage-only scaling to effectively serving critical workloads.
Most data platforms enable cluster monitoring of classic, technical metrics, that we know from the old “IT” world; metrics such as CPU load and RAM usage. This approach fails to align the business questions stream (workloads) and the TCO it generates.
Understanding the true cost of analytics requires expanding the classic “server monitoring approach” into a much deeper observability into the logic behind resource consumption, i.e. repeating query patterns, hidden dependencies, failure modes, cost and performance issues, etc.
Stay Focused on Meeting
Business KPIs
Varada delivers deep and actionable workload-level observability that supports this transition to understanding how data is used by different workloads, how resources are allocated among different workloads and users, how and why bottlenecks occur, etc.
The workload perspective enables data teams to focus engineering efforts on meeting business requirements. As infrastructure bills heavily weigh on the momentum of analytics projects, even to the point of negative profitability, Varada delivers a much deeper understanding that empowers data teams to move the knob of cost-performance, such as spend, ratio vis-a-vis the actual business value or importance of the workload at hand. 
