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Sync Computing
Helping developers optimize Apache Spark Clusters
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Is Databricks’s autoscaling cost efficient?
Is Databricks’s autoscaling cost efficient?
We analyze the performance of Databricks’s autoscaling on the TPC-DS workload to see if it is cost efficient
Jeff Chou
Jan 23
The top 6 reasons why companies struggle with improving cloud data infrastructure
The top 6 reasons why companies struggle with improving cloud data infrastructure
We summarize common themes we’ve learned from companies all over the world on why cloud efficiency is so hard to improve.
Jeff Chou
Dec 15, 2022
Sync Autotuner for Apache Spark — API Launch!
Sync Autotuner for Apache Spark — API Launch!
The Sync Autotuner API enables you to continuously monitor and tune your Apache Spark jobs at scale
Jeff Chou
Sep 18, 2022
Top 3 trends we’ve learned about the scaling of Apache Spark (EMR and Databricks)
Top 3 trends we’ve learned about the scaling of Apache Spark (EMR and Databricks)
Configuring and sizing the cluster for Apache Spark jobs is incredibly complex. We show how the Apache Spark Autotuner can make it easy.
Jeff Chou
Aug 2, 2022
Globally optimized data pipelines on the cloud — Airflow + Spark
Globally optimized data pipelines on the cloud — Airflow + Spark
We present a new kind of scheduler, which globally optimizes resources across entire data pipelines in an Airflow + Spark system.
Jeff Chou
May 30, 2022
Provision Databricks clusters based on cost and performance
Provision Databricks clusters based on cost and performance
Configuring compute infrastructure is a daunting task. Sync streamlines this step by provisioning clusters based on cost and performance
Jeff Chou
Apr 11, 2022
How poor provisioning of cloud resources can lead to 10X slower Apache Spark jobs
How poor provisioning of cloud resources can lead to 10X slower Apache Spark jobs
Optimizing your Apache Spark code is only half the battle. In this post we discuss the impact cloud infrastructure has on performance — and…
Jeff Chou
Feb 15, 2022
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