Apache Hadoop revolutionized enterprise data management by offering an open-source alternative to expensive proprietary data systems. Companies could process massive datasets using the commodity hardware in their existing data centers. In the subsequent two decades, an ecosystem of open source frameworks extended and enhanced Hadoop for other big data applications.
But Hadoop has not aged well. Its core assumptions throttle performance, undermine data democracy, and pressure data management budgets. Even worse, Hadoop’s architecture doesn’t fit with the cloud-based, real-time nature of modern enterprise data.
This post will explore why companies are migrating away from Hadoop data systems in favor of a modern data lakehouse architecture running on Amazon Web Services (AWS).
Why modernize your Hadoop environment?
Hadoop’s limitations, from performance bottlenecks to architectural complexity to user inaccessibility, increase friction within data-driven business cultures. Analyzing a company’s vast data stores takes more time and effort, delaying the insights decision-makers need to move the business forward. Although not without its challenges, modernizing from Hadoop to a data lakehouse positions companies for success.
Benefits of transitioning from Hadoop to a data lakehouse
Hadoop’s weaknesses are inherent to its design. The Hadoop Distributed File System (HDFS) poorly handles the high volume of small files generated by today’s streaming sources. MapReduce’s version of Java is so obscure that the open-source community developed Hive to give it a SQL-like skin. Hive further increased MapReduce’s latency, so the community developed alternatives like Apache Spark that replaced MapReduce entirely.
Migrating to a data lakehouse architecture lets companies benefit from a cloud-native data architecture that is more scalable, cost-effective, and performant. Lakehouses store data on commodity object storage services like Amazon S3 and Microsoft Azure Blob Storage. These services have robust systems that dynamically scale with demand. Modern file and table formats provide the metadata modern distributed query engines need to return results quickly and economically.
Challenges and considerations when modernizing Hadoop
Most companies built their Hadoop-based systems organically, adding other open-source frameworks to the core platform to create a unique data architecture. As a result, there’s no universal migration solution. This kind of transformation requires careful planning to avoid the pitfalls of such a large undertaking.
Hierarchical to flat, rigid to flexible
Object storage’s flat topology is a fundamentally different way to store data than HDFS’ hierarchical directory structure. The legacy system’s schema-on-write paradigm makes Hadoop changes challenging. On the other hand, lakehouses use schema-on-read to make data useful for unanticipated applications. Data teams must consider how moving to the new paradigm will change policies, processes, and use cases.
Modeling the migration
Enterprise data is complex, with hidden dependencies that could easily break during a migration. Engineers must model the move thoroughly to preserve data lineage and quality going into the lakehouse.
Business continuity
Downtime during the migration could significantly impact the business by disrupting inventory systems or corrupting dashboards. To minimize these risks, migration teams must phase in the migration as much as possible.
