AWS EMR Implementation

Realize optimal business value within a data-driven culture by implementing AWS EMR.

With the exponential growth of data volumes in the last decade, Apache Hadoop has undisputedly played a vital role in solving the problem of performing analytics on huge data sets. However, despite all the benefits, using Hadoop also brings along quite a few challenges, as well. An on-premises Hadoop cluster with all the applications and utilities you need on it is complex to configure, run and maintain. Hadoop allows users to add nodes and scale up as their workload increases – however, however take time to implement and require advanced payment.

Why Implement AWS EMR?

AWS Amazon Elastic MapReduc (EMR) is a cloud platform which makes it easy to create and manage fully configured, elastic clusters of EC2 instances, running Hadoop and other applications in the Hadoop ecosystem. Create as many clusters as you need in a matter of minutes, enable analytics on large data sets including business critical data, clickstreams, logs, and more. Let your data science team spin up the clusters they need in order to experiment and create value for your business.

Reduce complexity

As a managed service, EMR takes care of the infrastructure requirements, so you can focus on core business activities. Organizations only need to decide how many nodes they want in their cluster, their preferred Hadoop distribution, and to pick the application they want pre-installed, and EMR will take care of creating the cluster.

Optimize costs

Run your clusters only when you need them and pay only for what you have used. Take advantage of EC2 Spot instances to further reduce the cost. Find and terminate any idle instances so you do not pay for resources you are not using.

Gain flexibility and scalability

Create clusters of the required size and capacity in minutes and experiment and pick the instance types that make most sense for your workloads. Thanks to EMR Managed Scaling, your clusters can be dynamically scaled in or out.

Secure big data workloads

Take advantage of all built-in security features of the AWS platform – encrypt your data at rest and in transit, use IAM to securely control the access to the AWS resources used, and EC2 security groups to limit the inbound and outbound traffic to your cluster’s nodes. All security setups can be added to security configurations and then re-used as templates whenever you create new clusters.

Get high-availability and reliability

Launch your clusters in as many Availability Zones as you like in any AWS region. A disaster in one region can be easily worked around by spinning up the same clusters in a different region, within minutes and without blocking workloads.

Integrate seamlessly

As a fully managed AWS service, you can easily integrate your EMR clusters with other AWS services like S3, Kinesis, Redshift and DynamoDB in order to enable data movement and analytics across a wider range of services on the AWS platform.

What We Do

Adastra will help you plan and implement a scalable and secure solution to best fit your organization’s analytics requirements. We’ll help you build an environment that will allow your team to get resources and insights when they need them, at a fraction of the complexity and cost of an on-prem solution. Our solution will also help reduce administration and maintenance costs.


Identify your user personas, current end-to-end environment, and requirements. Based on the findings, Adastra will plan the right approach to sizing and building the environment that will cover your organization needs.

EMR implementation

Our experienced team of professionals will make sure you get a scalable, secure and performant solution at a lower cost, compared to on-prem clusters. We will build the data ingestion and transformation patterns and processes, implement and/or migrate the analytics workloads for you, and put in place the necessary CI/CD processes and security mechanisms.

Knowledge transfer

We will make sure your team is fully capable and comfortable working with the implemented end-to-end solution, including the ability to easily terminate, adjust and spin up new clusters. Optionally, invest in Adastra’s managed services for us to run the EMR clusters for you and maintain and update all applications and analytics workloads.

Approach to AWS EMR Implementation

  • Identify all stakeholders
  • Conduct a series of exploratory workshops to get acquainted with the end-to-end environment – identify data volumes, producers, consumers, analytics requirements, etc.
  • Create a classification of the teams and processes which would benefit from persistent EMR clusters and which would benefit from transient EMR clusters
  • Create a high-level design of the solution, making sure it integrates well with existing environments, while taking into consideration the possibility of future cloud migrations
  • Create an end-to-end implementation plan, including scope, timelines, milestones, and deliverables
    Define the data ingestion strategy for each data producing source system
  • If this is your first cloud project – our team will help you establish all necessary, cloud-based infrastructure and security mechanisms
  • In case of migration from an on-prem cluster – perform shadow tests to identify the right size and configuration of the EMR clusters, so you get on par or better performance at a lower cost, compared to your on-prem solution
  • Automate the provisioning of EMR clusters and create security configurations to easily apply the required security mechanisms to each new cluster
  • Implement data pipelines to ingest data frow any identified source
  • Implement or migrate data transformation and analytics workloads
  • Configure CI/CD pipelines to automate, test and deploy
  • Deliver detailed technical documentation, allowing your team to operate efficiently in the new environment
  • Conduct knowledge transfer and training sessions, making sure all technical and business users are well-acquainted with the delivered solution, and its features and capabilities
AWS Data Lake Implementation in Healthcare
Success Story

AWS Data Lake Implementation

Skylight Health Group is expanding and acquiring new clinics, along with all their data. The group needed to integrate numerous electronic medical records (EMR) systems and provide healthcare practitioners with predictive analytics.

Adastra built a data management solution that makes it easy for Skylight Health’s teams to add users and access real-time data—without more infrastructure.


more productive analytics team


manual effort needed to produce unified and consolidated reports


infrastructure maintenance needed

The level of cooperation between members of our organization and Adastra has always been outstanding. The implementation of the AWS Cloud Analytics Platform powered complex insights into our business in an automated fashion.

Chris Smith | VP Digital Health, Skylight Health Group

Frequently Asked Questions

Apache Hadoop is an open-source framework, which efficiently processes and stores large datasets ( gigabytes  or petabytes). Hadoop takes advantage of using a cluster of commodity hardware to massively parallelize the processing workloads. Hadoop consists of four main modules:

  • Hadoop Distributed File System – a distributed file system, residing on the cluster, which provides large data throughput and fault tolerance
  • Yet Another Resource Negotiation (YARN) – a resource manager
  • MapReduce – a framework which helps programs perform parallel computation on data
  • Hadoop Common – common Java libraries that can be used across all modules

Some of the most popular applications which store, process, analyze, manage big data, and run in Hadoop are Spark, Presto, Hive, HBase, etc.

A Hadoop cluster is a group of commodity hardware that are connected. This cluster runs open-source software and provides distributed and fault-tolerant compute and storage features. A Hadoop cluster implements a primary-replica architecture. Usually, a high-end machine acts as a primary node and hosts various storage and processing management services for the entire clusters, whereas the replica nodes are responsible for storing the data and performing the actual computations on it.

Running Hadoop in AWS (using EMR) has quite a few advantages, compared to running Hadoop on an on-premises cluster:

  • Easy to use – you can launch an EMR cluster in minutes and do not need to worry about configuration and administration overhead
  • Cost – with EMR you pay only for what you use, in the form of an hourly rate for the instances in your cluster

Elasticity – you can easily provision as many compute instances as you like to cope with any unpredicted workload and then scale back in

Let’s Modernize with AWS EMR

Easily ingest data into your cluster with EMR’s many options – uploading data from S3 and DynamoDB or using DataSync, Direct Connect and Snowball to move your on-prem data to the EMR cluster.