Introduction Sync recently introduced Gradient, a tool that helps data engineers manage and optimize their compute infrastructure. The primary facet of Gradient is a Project which groups a sequence of runs of a Databricks job. After each run, the Spark eventlog and cluster information is sent to Sync. That accumulated project data is then fed
Sean GorskyBlog
Wow the day is finally here! It’s been a long journey, but we’re so excited to announce our newest product: Gradient for Databricks. Checkout our promo video here! The quick pitch Gradient is a new tool to help data engineers know when and how to optimize and lower their Databricks costs – without sacrificing performance.
Jeffrey ChouBlog
Here at Sync we are passionate about optimizing cloud infrastructure for Apache Spark workloads. One question we receive a lot is “Do Graviton instances help lower costs?” For a little background information, AWS built their own processors which promise to be a “major leap” in performance. Specifically for Spark on EMR, AWS published a report
Jeffrey Chou
Blog, Case Study
The Situation Let’s say you’re a data engineer and you want to run your data/ML Spark job on AWS as fast as possible. You want to avoid slow Apache Spark performance. After you’ve written your code to be as efficient as possible, it’s time to deploy to the cloud. Here’s the problem, there are over
Jeffrey ChouBlog
Here at Sync, we are passionate about optimizing data infrastructure on the cloud, and one common point of confusion we hear from users is what kind of worker instance size is best to use for their job? Many companies run production data pipelines on Apache Spark in the elastic map reduce (EMR) platform on AWS.
Jeffrey Chou
Blog, Case Study
As many previous blog posts have reported, tuning and optimizing the cluster configurations of Apache Spark is a notoriously difficult problem. Especially when a data engineer needs to lower costs or accelerate runtimes on platforms such as EMR or Databricks on AWS, tuning these parameters becomes a high priority. Here at Sync, we will experimentally
Jeffrey Chou
Blog, Case Study
