Hadoop workloads

Scalable, Transient and Cost-effective cloud big data platform with Amazon EMR. Extract data from a variety of sources, process it at scale, and make it available for applications and users.

Are you stuck with your on-premises Hadoop workload?

Fixed Cost
Coupled Storage & Compute
Always On
Unused Capacity
Outages Impact and Downtime
Slow Deployment Cycle
Static and Non Scalable

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/ GO TO CLOUD!

Benefits of migrating to Amazon EMR

Amazon EMR is a cloud big data platform for running large-scale distributed data processing jobs, interactive SQL queries, and machine learning (ML) applications using open-source analytics frameworks such as Apache Spark, Apache Hive, Presto, HBase, Flink, and more big data apps on AWS.

Amazon EMR simplifies building and operating big data environments and applications. Related EMR features include easy provisioning, managed scaling, and reconfiguring of clusters, and EMR Studio for collaborative development.

Let our Compass UOL Data & Analytics Innovation Studio guide your journey on Hadoop Modernization with Amazon EMR.

Get the latest, stable,

open-source frameworks releases

Automatically scale up and down. Manage cluster size based on utilization to reduce costs

Simple and predictable pricing. Per-second pricing, and save 50% - 60% with Amazon EC2 Spot and Reserved Instances

/ FEATURES

Decupled Compute & Storage

EMR has its storage decoupled from the compute, thus you can keep data perennial while computing is transient.

Elastic

You can quickly and easily provision as much capacity as you need, and automatically or manually add and remove capacity.

Built-in Disaster Recovery

You can easily configure high availability for multi-master applications with a single click.

Low Cost

Some of the features that make it low cost include low per-second pricing, Amazon EC2 Spot integration, Amazon EC2 Reserved Instance integration, elasticity, and Amazon S3 integration.

Flexible Data Stores

You can leverage multiple data stores, including Amazon S3, the Hadoop Distributed File System (HDFS), and Amazon DynamoDB.

Spark Performance

Improvements Spark is an industry leading capability for ML, Querying, Streaming and Transformation and makes up ~60% of our workloads. Given the vast adoption, we are keen to provide best performance.

AWS Interoperability: EMR interoperability with AWS Analytics Services

Focusing on the big Data Processing component of the Lake House architecture, we have Amazon EMR - which makes it easy for customers to process petabyte-scale data using the latest open source big data frameworks such as Spark, Hive, Presto, HBase, Flink, and Hudi. Like Athena and other services part of the Lake House architecture, EMR runs directly against the data stored in our customers’ S3 data lake.

/ WORKSHOP

Compass UOL Migration Inception Discovery Workshop

We work on a set of activities that takes just 8 hours divided in two days:

The result of this journey will be a Migration Roadmap, in which we prepared a summary with the main information on which will base the commercial proposal.

Let Compass UOL guide you on your ETL Modernization journey

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AWS COMPETENCIES

Compass UOL is a global company that is part of AI Revolution Company, which drives the transformation of organizations through Artificial Intelligence, Generative AI, and Digital Technologies. We design and build digitally native platforms using cutting-edge technologies to help companies innovate, transform businesses, and drive success in their markets. With a focus on attracting and developing the best talent, we create opportunities that improve lives and highlight the positive impact of disruptive technologies on society.