Apache Spark is a popular open source analytics engine for large-scale data processing. Applications can be written in Java, Scala, Python, R, and SQL. These applications have flexible options to run on like Kubernetes or in the cloud.
The company Data Mechanics is a cloud-native Spark platform for data engineers. It runs continuously optimized Apache Spark workloads on a managed Kubernetes cluster within the user’s cloud account. They boast a 50%-75% cost reduction from cloud providers by dynamically scaling applications based on load and automatically tuning app configurations based on the historical Spark pipeline runs. Their Kubernetes clusters are deployed within user accounts so user data never leaves the environment and they handle the cluster management.
In this episode we talk to Jean-Yves Stephan, Co-Founder and CEO at Data Mechanics. Jean-Yves previously worked as a Software Engineer then a Tech Lead Manager at Databricks. We discuss big data engineering in Spark and the unique advantages of using Data Mechanics to make Spark development easier and more cost effective.
Sponsorship inquiries: firstname.lastname@example.org
Transcript provided by We Edit Podcasts. Software Engineering Daily listeners can go to weeditpodcasts.com to get 15% off the first three months of audio editing and transcription services with code: SED. Thanks to We Edit Podcasts for partnering with SE Daily. Please click here to view this show’s transcript.
Pachyderm is an easy-to-use MLOps platform that empowers anyone to build scalable end-to-end machine learning workflows, regardless of whatever language or framework they are built on. Pachyderm provides Git-like data versioning and lineage to automatically track every data change and final output result. Head over to pachyderm.com/sedaily to get over $400 in free credits. But hurry because this offer only lasts for a limited time.
This article is purposely trimmed, please visit the source to read the full article.
The post Data Mechanics: Data Engineering with Jean-Yves Stephan appeared first on Software Engineering Daily.