With data analytics becoming ever more critical to your organization’s success, there has never been a better time to be a data engineer: Jobs are plentiful. The pay is terrific. The work you do is essential. So, life is good, right?
Unfortunately, like most data engineers, you spend 80% of your time maintaining existing data products and processes. While this “keeping the lights on” work is essential, it means you can only dedicate 20% of your time to true innovation. After a while, this ratio can become a bit of a drag.
What if we told you that you no longer have to settle for this traditional 80/20 mix?
Modern data engineering tools can help you flip the ratio. Keebo’s data learning platform is just one example of such a tool. Let’s explore how you can use it to innovate more and maintain less.
Three Ways Data Learning lets Data Engineers Innovate More and Maintain Less
- Less time optimizing performance: Data learning helps data engineers automate performance optimization, which is a critical activity repeated as you design, develop, test, and deploy new data products as well as their associated queries and models. With the time you free up, you can spend more time building additional innovative data assets.
- Less time maintaining existing data products: Data learning not only optimizes data engineering efforts required for new data products, queries, and models, but it also supports performance SLAs for data assets in production automatically. Using such a “learn and refine” approach means you spend far less time maintaining performance SLAs and more time innovating.
- Leave more of the fine-tuning to the business analysts: As a data engineer, your goal is to deliver data products that meet the majority of your organization’s needs. For business analysts, these assets often need fine-tuning to meet unique requirements beyond their core needs. While self-service visualization and data prep tools make this easier for them, these tools lack the powerful performance optimization techniques used by data engineers. Data learning can fill this gap, automatically optimizing query performance for business analysts. Eliminating this data engineering workload is yet another way to increase time for innovation.
Take The First Step
Change is hard, especially when change is good. But why would you want to continue spending four days a week maintaining existing data assets and just one day doing the exciting new stuff?
With data learning, you don’t have to continue the status quo. Flip that ratio: now is the time.