How Qubole addresses Apache Spark challenges

The Qubole Data Platform brings streamlined configuration, auto-scaling, cost management, and performance optimizations to Spark-as-a-service

Traditional relational databases have been highly effective at handling large sets of structured data. That’s because structured data conforms nicely to a fixed schema model of neat columns and rows that can be manipulated using SQL commands to establish relationships and obtain results. Then big data came along.

Big data required a new way to store, manage, and query the massive sets of messy, unstructured data that are often involved. Traditional data processing tools have failed to meet the performance and reliability requirements of big data for machine learning and advanced analytics applications. Organizations needed a way to build reliable pipelines that could handle these vast, complex workloads.

This led to the emergence of distributed data processing engines such as Apache Spark that split the data into smaller, manageable chunks and process it across multiple compute nodes. Distributed engines greatly improve processing times and enable a wide spectrum of use cases in machine learning and big data analytics, which in turn allow for more experimentation and innovation.

What is Apache Spark?

To continue reading this article register now