Can we take Data Engineering on Spark 10x beyond where it is today?

Yes, we can enable 10x more users on Spark, and make them 10x more productive from day 1. Data engineering can run at scale, and it can still be 10x simpler and faster to develop, deploy, and manage pipelines.

Low code is the key. A modern data engineering platform built on low code will enable all data users, from new graduates to experts, to visually develop high-quality pipelines. With Visual = Code, the visual elements will be stored as PySpark code on Git and deployed using the best software practices taken from DevOps. Search and lineage help data engineers and their customers in analytics understand how each column value was produced, when it was updated, and the associated quality metric.

See how a complete, low-code data engineering platform can reduce complexity and effort, enabling you to rapidly deploy, scale, and use Spark, making data and analytics a strategic asset in your company.