Transitioning from Hadoop to modern lakehouses

Published: June 20, 2024

As organizations strive to harness the full potential of their data, the limitations of legacy Hadoop systems become increasingly apparent. Hadoop’s architecture has been traditionally centered around its ecosystem components like HDFS for storage and MapReduce for processing, supplemented by tools such as Hive and Impala for SQL-based querying. This setup was robust for batch processing of large datasets but struggled with real-time data analytics and high-concurrency workloads. The architecture required significant manual effort to manage and maintain, with numerous interdependent systems that added complexity. Additionally, the reliance on traditional SQL engines like Hive and Impala led to performance bottlenecks, making it challenging to handle the increasing demands of modern data analytics. The operational overhead, including resource provisioning and system tuning, often resulted in underutilized infrastructure and high costs.

Hadoop, once the cornerstone of big data processing, now faces significant challenges related to performance, scalability, and maintenance.

With the integration of Starburst, Hadoop’s architecture transforms into a modern, efficient lakehouse model. Starburst introduces Trino as the powerful query engine, replacing Hive and Impala, which significantly enhances query performance and scalability. The Icehouse architecture leverages Apache Iceberg for its table format, providing transactional consistency and schema evolution capabilities that streamline data management. By implementing Starburst, organizations can transition from on-premises Hadoop to a hybrid or fully cloud-based environment, such as Starburst Galaxy. This new architecture supports real-time data ingestion, automated data management, and robust governance, reducing complexity and operational overhead. Consequently, organizations benefit from improved performance, cost efficiency, and the ability to handle high-concurrency, real-time analytics workloads, unlocking the full potential of their data.

Why modern lakehouses?

Modern open data lakehouse architectures effectively blend the capabilities of data lakes and data warehouses to provide high performance, scalability, and simplified management. This fusion creates a versatile and powerful data infrastructure that addresses the limitations of traditional Hadoop systems.

Open table formats: Modern lakehouses leverage open table formats like Apache Iceberg to manage data efficiently. Iceberg introduces several advancements over traditional storage formats, including transactional consistency, which ensures that all data operations are atomic, consistent, isolated, and durable (ACID). This guarantees data integrity even during concurrent transactions. Additionally, Iceberg supports schema evolution, allowing changes to the data schema without disrupting ongoing operations. It handles large datasets seamlessly, providing features such as partitioning, hidden partitioning, and data versioning, which optimize query performance and manage data at scale. Iceberg’s design supports complex data structures and large-scale data lakes, enabling robust data management across diverse use cases.

Powerful distributed SQL query engines: Trino, is the cornerstone of query processing in modern lakehouses. Trino’s architecture is optimized for distributed query processing, enabling it to execute SQL queries over large datasets spread across multiple nodes efficiently. It is designed for high concurrency, supporting numerous users and queries simultaneously without significant performance degradation. Trino employs a MPP (Massively Parallel Processing) model, breaking down queries into smaller tasks that run concurrently across a distributed cluster, thus accelerating query execution times. Trino also features advanced query optimization techniques, including dynamic filtering, predicate pushdown, and query federation, allowing it to interact with various data sources seamlessly. This makes it ideal for interactive and ad-hoc analytics, providing fast, real-time insights.

Unified data management: One of the key advantages of modern lakehouse architectures is their ability to manage and govern a central gravity of data within the lake, while also accessing disparate sources outside the lake. This unified approach eliminates data silos, enabling comprehensive analytics across diverse data types. In a lakehouse, data governance is streamlined through integrated metadata management, which maintains a consistent view of data assets and ensures compliance with data policies. This architecture supports various data formats, including JSON, Parquet, and Avro, allowing organizations to store and analyze a wide range of data efficiently. The unified data management capability simplifies data ingestion, transformation, and access, providing a cohesive environment where data engineers and analysts can collaborate effectively. By consolidating data operations, a lakehouse architecture enhances data accessibility, reduces latency, and improves the overall agility of data-driven decision-making processes.

Transition pathways

Starburst offers a phased approach to modernization, allowing organizations to transition at their own pace.

SQL engine upgrade

For organizations looking to gain immediate performance improvements without a full migration, upgrading to Starburst can provide significant benefits. Starburst enhances the open-source Trino engine with enterprise-grade features such as security, performance optimizations, and connector support.


  • Immediate ROI through improved query performance.
  • Reduced operational costs by leveraging existing Hadoop infrastructure.
  • No need for extensive re-platforming, maintaining continuity with current systems.

On-Premises modernization

For those needing to keep data on-premises due to regulatory or operational reasons, the Dell Data Lakehouse powered by Starburst provides a turnkey solution. This includes modern storage (Dell ECS or PowerScale), compute (Dell PowerEdge), and the Starburst Enterprise platform.


  • Modern hardware and software integration for enhanced performance and efficiency.
  • Simplified deployment and management through pre-configured solutions.
  • Seamless integration with existing data infrastructure.

Cloud-centric modernization

For organizations ready to embrace the cloud, Starburst Galaxy offers a fully managed lakehouse platform. Galaxy combines Trino and Apache Iceberg to deliver high-performance analytics with minimal administrative overhead.


  • Scalability and flexibility of cloud infrastructure.
  • Advanced features like real-time data ingestion, automated data management, and AI-driven optimizations.
  • Unified data governance and security across multi-cloud environments.

Achieving 10X Faster Queries and $8 Million in Savings

Optum faced challenges with their Hadoop-based data lake architecture, which struggled to support their needs at scale due to slow query performance and inefficient resource utilization. To address these issues and reduce costs, Optum deployed Starburst on their Hadoop infrastructure. This solution improved query performance, providing secure and fast access to data. The results were impressive: queries ran 10 times faster, infrastructure costs were reduced by 30%, and projected savings reached $8 million. Additionally, Starburst offered a single endpoint for data access, significantly enhancing query efficiency and user satisfaction.


Transitioning from Hadoop to a modern lakehouse architecture with Starburst enables organizations to overcome the limitations of legacy systems and unlock the full potential of their data. Whether through an incremental engine upgrade, a comprehensive on-premises solution, or a full cloud migration, Starburst provides a pathway to modern, efficient, and scalable data analytics.

For more detailed insights and practical steps, refer to the full Hadoop migration technical guide.

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Starburst Galaxy

Up to $500 in usage credits included

  • Query your data lake fast with Starburst's best-in-class MPP SQL query engine
  • Get up and running in less than 5 minutes
  • Easily deploy clusters in AWS, Azure and Google Cloud
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