Migrating and Optimizing Amazon EMR Workloads: Best Practices from the Provectus Data Team

Author:
Provectus, AI-first consultancy and solutions provider.

By Daria Koriukova, Data Solutions Architect – Provectus; Artur Zaripov, Data Engineer – Provectus; Alexey Zavialov, Sr. Data Solutions Architect – Provectus; Kalen Zhang, Global Tech Lead, Partner Solutions Architect – AWS

Today, migrating on-premises Apache Spark and Apache Hadoop workloads to the cloud is seen by many organizations as a logical step to rein in rising costs, resolve administrative issues, and alleviate maintenance headaches.

Amazon EMR is the industry-leading big data cloud solution for petabyte-scale data processing, interactive analytics, and machine learning, using open-source frameworks such as Apache Spark, Apache Hadoop, Apache Hive, and Presto. Amazon EMR makes it easier and more cost-efficient to run and scale big data workloads, and streamlines the handling of data used for artificial intelligence (AI), machine learning (ML), and predictive analytics.

Provectus, an AWS Premier Consulting Partner with Data and Analytics Competency, has vast experience in helping clients to resolve issues related to their legacy on-premises data platforms. We implement a wide range of best practices to migrate and optimize Amazon EMR workloads in the most effective manner.

In this blog post, we look into the challenges organizations face when migrating to the cloud, and explore best practices for re-architecting and migrating on-premises data platforms to AWS, including:

• Optimization of storage and compute
• Splitting and decoupling of clusters
• Proper job scheduling and orchestration
• Use of cloud data lakes

Read this article on the AWS blog to learn more about our approach to migrating and optimizing Amazon EMR workloads!