As more and more companies are embedding AI projects into their systems, attracted by the promise of efficiencies and competitive advantages, data science teams are feeling the growing pains of a relatively immature practice without widespread established and repeatable norms.

A Forbes survey shows that data scientists spend 19% of their time collecting data sets and 60% of their time cleaning and organizing data. All told, data scientists spend around 80% of their time on preparing and managing data for analysis. One of the greatest obstacles that make it so difficult to bring data science initiatives to life is the lack of robust data management tools.

Spark is known for its powerful engine which enables distributed data processing. It provides unmatched functionality to handle petabytes of data across multiple servers and its capabilities and performance unseated other technologies in the Hadoop world. Although Spark provides great power, it also comes with a high maintenance cost. In recent years, innovations to simplify the Spark infrastructure have been formed, supporting these large data processing tasks.