The Case for Migrating On-premises Hadoop to Amazon EMR

Apache Hadoop has been a reliable big data storage and processing framework since its release in 2006. Many of the largest companies in the world still use Hadoop today to extract insights from their data. The platform is also growing at a considerable rate and is expected to reach a near $25B valuation by 2027.

However, another big data platform is elevating expectations around what organizations can do with their data – Amazon Elastic MapReduce (EMR). Amazon EMR is a cloud-native solution that alleviates many of the challenges of working with on-premises Hadoop clusters. Amazon EMR also opens the door to new-age capabilities, like predictive analytics and machine learning (ML).

In this post, we dive more deeply into these benefits and help explain why many organizations are migrating on-premises Hadoop to EMR. While Hadoop is still valuable for its robust ecosystem, data source flexibility, and other features, there’s so much more that leaders can do today with their large datasets.

The Benefits of EMR

To appreciate what Amazon EMR offers, we need to understand how it improves upon on-premises Hadoop. Hadoop is built well to store and process diverse data types. But when managed on-premises, it adds administrative burden and introduces unnecessary inefficiencies.

For instance, with on-premises Hadoop, it’s easy to overprovision hardware and waste capital. Managing this hardware requires specialized IT skill. On-premises Hadoop also comes with a higher risk of downtime and less durability compared to current expectations such as 99.999999999% durability (or the 11 9s). Other disadvantages of on-premises Hadoop include lack of scalability, slow innovation, and vendor lock-in. Amazon EMR gracefully mitigates each of these problems.

Amazon EMR comes with fault-tolerant Amazon S3 storage (and does meet the 11 9s standard). Users can also take advantage of transient/serverless on-demand clusters and don’t have to pay licensing fees. Furthermore, Amazon EMR is much more hands-off from a maintenance perspective. The platform offers automated cluster provisioning and auto-scaling. Some organizations have been able to reduce Ops maintenance up to 90% by migrating on-premises Hadoop to Amazon EMR. Amazon EMR is also more cost-efficient, thanks to its pay-per-use nature.

Perhaps the biggest benefit of the cloud-native solution is that it opens up new avenues of innovation. Amazon EMR works with leading, open-source big data frameworks and integrates seamlessly with other sophisticated AWS products. This is what enables the exciting use cases outlined in the following section.

EMR Use Cases

With Amazon EMR, organizations can unlock new sources of value that were previously unattainable. For example, Amazon EMR users can implement efficient data pipelines that take in many data types from countless sources. Amazon EMR also enables companies to process real-time data streams. That way, data engineers can analyze events and take action based on nuanced insights.

In addition, Amazon EMR lays the groundwork for robust data science and ML implementations. Amazon EMR integrates with Amazon SageMaker Studio, AWS’ impressive ML model training solution. Organizations can layer finely tuned ML algorithms on top of their data to accelerate insight generation and launch new products that meet the needs of nuanced customer segments. Moreover, data teams can continue to use open-source frameworks, like Apache Spark, TensorFlow, and more to build big data-powered applications.

Check out this case study to read about one ClearScale-led Amazon EMR implementation.

These are the types of use cases that separate Amazon EMR from on-premises Hadoop. Leaders who are searching for ways to cut costs and drive growth have to consider Amazon EMR if they are still relying on on-premises Hadoop clusters.

Migrating from Hadoop to Amazon EMR

Getting from on-premises Hadoop to cloud-native Amazon EMR can be a complicated process, depending on the approach. Some organizations go with the lift-and-shift approach. These migrations are quicker than other approaches, but they often fail to capitalize on the full potential of the cloud. Others choose to replatform, optimizing certain aspects of their big data workloads to generate incremental business value.

The best approach to achieving long-term success is to rearchitect the data platform completely during the migration process. In on-premises Hadoop to Amazon EMR migrations, architecting involves modernizing core features and leveraging capabilities like advanced multitenancy, data lineage tracking, and serverless compute. The problem is that these migrations are the most complex and risky from an execution perspective.

That’s why it often makes sense to partner with a third-party migration partner, like ClearScale, that has significant experience moving on-premises Hadoop clusters to Amazon EMR. ClearScale has 100+ AWS technical certifications and 11 AWS competencies, including both the Migration and Data & Analytics competencies. ClearScale is also deeply familiar with AWS’ MAP program, a tried-and-true methodology for migrating resources to the AWS cloud.

Our team knows how to prepare for and execute large-scale migrations involving mission-critical assets. We also have access to special AWS funding opportunities that bring down overall migration costs. So, engineers don’t have to worry about losing any data or experiencing downtime. And leaders can trust that their investment in a big data cloud platform is worth it.

If you’re interested to learn more about how we approach on-premises Hadoop to Amazon EMR migrations, schedule a call with one of our AWS experts today. We’ll share more details about the process and make sure you feel 100% confident before moving forward.

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