The evolution from Hadoop to a data lakehouse architecture represents a significant leap forward in data infrastructure. While Hadoop once ruled the big data landscape with its beefy batch processing power, organizations today seek more agile, cost-effective, and modern solutions. Especially as they increasingly embark on AI initiatives. There is just no way to make Hadoop work for AI. 

Instead, more and more are migrating to a data lakehouse architecture, which combines the best of data lakes and data warehouses, and provides the scalability, performance, and real-time capabilities required to handle modern data workloads.

The Limitations of Hadoop

Hadoop was designed for a different era of data processing. Its monolithic architecture tightly coupled storage (HDFS) and compute (MapReduce), making it impossible to scale each independently or effectively. High operational overhead, reliance on complex interdependent systems like Hive for querying, and slow performance for interactive workloads have made Hadoop less appealing as data needs grow. These limitations have led organizations to rethink their approach to data management and seek alternatives that reduce complexity and costs while improving performance.

Enter the Data Lakehouse

A data lakehouse addresses the shortcomings of Hadoop by blending the flexibility of data lakes with the structure and performance of data warehouses. With a lakehouse architecture, you can store vast amounts of raw and structured data in open table formats in MinIO Enterprise Object Store. This architecture supports both real-time analytics and batch processing with query engines optimized to work over object storage. This approach results in a more flexible, cost-effective, and scalable data infrastructure.

Migration Strategy: A Phased Approach

Migrating from Hadoop to a data lakehouse requires careful planning and execution. The goal is to modernize your data platform with minimal disruption. Here’s a step-by-step guide to facilitate a smooth transition:

1. Dual Ingestion Strategy: Begin with Parallel Operations

Start with a dual ingestion strategy, where you continue feeding data into your Hadoop environment while simultaneously ingesting it into high-performance object storage. This approach allows for testing new workflows without disrupting existing operations and can also serve as a backup strategy to reduce risks during the migration phase.

2. Migrate Data to Cloud-Native Object Storage

The core of a data lakehouse is cloud-native object storage, which offers nearly limitless capacity and lower maintenance costs compared to HDFS. It is important to choose object storage that was purpose built for AI and optimized for large datasets like MinIO Enterprise Object Storage. For the migration process use tools like Apache DistCP for data transfer for bulk migration and a tool like Rclone for ongoing synchronization or smaller datasets.

3. Upgrade Your Query Engine

Switching to modern query engines, such as Trino or Dremio, is essential for improving performance and supporting complex, high-concurrency workloads. These engines offer sub-second query responses and can federate queries across various data sources, providing a unified view of data across the organization. Enhanced query performance not only boosts data accessibility but also democratizes data usage across departments. You can often swap out your query engine early on in your migration process before migrating your data to get your end-users on board and trained with the new process before turning off the taps to Hadoop.

4. Reconfigure Data Processing Pipelines

In Hadoop, data processing is commonly performed with MapReduce jobs or Hive scripts. To modernize these workflows, consider converting the pipelines to use open-source tools that support both batch and streaming data processing. For example, Apache Flink and Apache Beam both offer versatile data processing frameworks that work well for diverse workloads.

5. Embrace Open Table Formats for Better Data Governance

Adopting open table formats like Apache Iceberg, Apache Hudi and Delta Lake is a crucial step in enabling features such as ACID transactions, time travel, and schema evolution. These capabilities ensure data integrity and allow seamless data updates while providing fine-grained control over data access. Implementing an open table format enhances governance and simplifies data management across the lakehouse.

Unlocking the Full Potential of Your Data

By migrating from Hadoop to a data lakehouse, organizations can reduce costs, simplify operations, and enable real-time analytics. The move supports scalable data storage and high-performance query capabilities, which are essential for harnessing the full potential of modern data workloads.

The key to a successful migration lies in a phased approach that gradually transitions data and workloads to a lakehouse architecture, minimizing downtime and disruption. With the right planning, your organization can turn its legacy data infrastructure into a robust, future-proof platform.

Start your journey by adopting a phased approach and leveraging modern data technologies to drive business agility and performance. Please feel free to reach out to us as you prepare to modernize your data architecture and unlock the full value of your data.