Why use Mactores to migrate to Amazon EMR
- Automation to analyze your legacy systems and rapidly migrate to Spark on Amazon EMR
- Our tools enable/support the migration with data quality, data consistency, and data lineage during and after the migration
- De-risk your migration with our in–depth experience in transforming Petabytes of Hadoop clusters to Amazon EMR
- Expertise enabled library of best practices with automation to support complex migrations
- Our contributions to multiple open source communities in big data space has enabled us to deal with complex workloads and platform challenges including Hadoop on Amazon EMR
Example Customer Migrations to Amazon EMR
- Hive/MAP Reduce to Spark
- Presto scalability on EMR
- Impala to Spark + Presto migration
- Pig jobs to Spark on EMR
- Cloudera/HortonWorks to EMR
- Databricks (Cloud) to EMR + Hudi
Register for your Hadoop on AWS Immersion Day
Immersion Day Agenda
This Mactores led On-line Workshop jump-starts your Apache Hadoop/Spark migration to Amazon EMR. We recommend that your Apache Hadoop/Spark Admins, Data Engineers, and Infrastructure Engineers be present. Your Analysts, Data Scientists, or ML Engineers can also attend.
The workshop is customized to fit your specific use cases, goals, SLAs, business needs, and success criteria. A key part of the Workshop is discussing your current on-premises Apache Hadoop/Spark architecture, your workloads, and your desired future architecture.
Complete the form and one of our technical experts will contact you to confirm the best date and time for your team to attend the on-line workshop.
To preview your immersion day agenda please download.
Read our Case Studies and AWS Partner Network (APN) Blogs
Learn about the three migration options Mactores tested and the architecture of the solution Seagate selected. This effort improved the overall efficiency of Seagate’s Amazon EMR cluster and business operations.
How Mactores Cognition helped Seagate Technologies use Spark as the execution engine for Hive running on AWS to lower their TCO and triple the performance of their queries on memory larger than 10 TB.