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How to Run Spark Over Kubernetes to Power Your Data Science Lifecycle

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.

Fundamentals for Success in Cloud Data Management

Everybody needs more data and more analytics, with so many different and sometimes often conflicting needs. Data engineers need batch resources, while data scientists need to quickly onboard ephemeral users. Data architects deal with constantly evolving workloads and business analysts must balance the urgency and importance of a concurrent user population that continues to grow.