Location, location, location! In real estate, the worst house in a nice neighborhood is typically more valuable than the best house in a neighborhood next to a busy freeway.

The same is true when it comes to analytics and reporting performance. If a beautiful dashboard full of insight runs frustratingly slow, it is not as valuable as a less insightful dashboard that delivers pretty good insights quickly and accurately. 

And why settle when you can have the best of both worlds? You can get a great dashboard that produces powerful business insights blazingly fast. In other words: the nice house in the nice neighborhood.

A data engineer is not unlike a real estate agent. Your goal is to get your stakeholders–business analysts, data scientists, and report consumers–into the high-performing neighborhood. 

Traditional Routes to Success 

When it comes to delivering speedy data, as a data engineer, you have two routes:

  • Pay More: Often, you can pay for better performance if your organization is willing to migrate to a faster data warehouse technology. The reason is that not all data warehouses are created equal. The ones with more sophisticated query optimizers tend to be more expensive. And while cloud-based data warehouses let you increase your cluster size (throw more compute at your workload) to improve performance, these additional resources can be costly. 
  • Manual Optimizations: You can always spend more time on data modeling, manually optimizing your queries, tweaking your database schemas, pre-aggregating data, or materializing or caching common subqueries.

Performance, but at What Cost? 

Unfortunately, both routes come with both direct and indirect costs.  These include”

  • Data Warehouse Migration Cost: Migrating an entire org from one data warehouse to another causes a lot of friction and is a costly process–sometimes taking six or more months to transition fully. 
  • Faster Data Warehouse Cost:  Throwing more money at the problem by increasing the cluster size is not always sustainable while data volumes and usage grow.
  • Data Engineering Resource Cost: Manually optimizing queries requires long data engineering hours. Tedious, time-consuming, error-prone, and brittle, these efforts can seem like a never-ending project. 
  • Data Engineering Opportunity Cost: More importantly, manual optimization efforts take precious time that data engineers could spend doing other essential tasks

Data Learning, A New Route to Optimized Performance

Keebo provides a new route to performance optimization. Our innovative concept, known as Data Learning, is a transparent platform that data engineers can leverage to automatically speed up analytical queries by one or two orders of magnitude. 

Keebo uses machine-learning techniques to intelligently optimize queries automatically based on insights it gathers about query patterns, data sources, and their update frequencies, as well as performance bottlenecks–and more, the longer your queries run. 

Plus, data learning lets you avoid all the costs required to migrate to a faster data warehouse platform or go the manual performance tuning route.  

Here’s a bit more about how data learning works.

Now is the Time to Move to the Nice Neighborhood

Performance, Performance, Performance!  Why make your users settle for less than the best? 

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