Here at Sync we are always trying to learn and optimize complex cloud infrastructure, with the goal to help more knowledge to the community. In our previous blog post we outlined a few high level strategies companies employ to squeeze out more efficiency in their cloud data platforms. One very popular response from mid-sized to
Jeffrey Chou
Blog, Case Study
We launched the Autotuner for Apache Spark several months ago, and have worked with many companies on analyzing and optimizing their Apache Spark workloads for EMR and Databricks. In this article, we summarize cluster scaling trends we’ve seen with customers, as well as the theory behind it. The truth is, cluster sizing and configuring is
Jeffrey Chou
Case Study
Sr. Data Engineer at Disney Streaming In the self-written blog post below, a Sr. Data Engineer chronicles his experience with the Spark Autotuner for EMR. In the blog post we helped accelerate a job from 90 to 24 minutes, which was amazing to see! The first job I put into the autotuner went from processing
Jeffrey Chou
Case Study
We’re excited to announce that the Sync Apache Spark Cluster Autotuner Solution now supports Databricks! (our previous blog post was about Spark in EMR) In this blog post we discuss a real use-case with a customer, a cloud-native data based company, and how we lowered their Databricks cluster costs by 34% and accelerated their jobs
Jeffrey Chou
Case Study
Launch to the cloud based on cost and time: This article explains Sync Computing’s Spark Cluster Autotuner Solution and how it was used to reduce Duolingo’s AWS EMR Spark costs by up to 55%. This solution eliminates the inefficient manual tuning and guesswork currently used when configuring Spark clusters and settings to provide the best
Jeffrey Chou
Case Study
