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THE STARBURST DATA LAKEHOUSE

The fastest path from Hadoop to data lakehouse

Modernize your data lake strategy with 10x analytics performance improvements at a fraction of the cost

Modernize your Hadoop architecture on your terms

Your data architecture is unique to your specific business, governance, and security requirements. Data may continue to reside on-premises, in hybrid, or cloud centric data architectures and your lakehouse platform should be able to support your requirements. With Starburst, you gain greater flexibility in how you modernize your Hadoop ecosystem while benefiting from a modern data lakehouse.

Modernization paths

Data staying on-premises

Get faster and more efficient analytics on data that will stay on-premises in HDFS by upgrading Hive/Impala with Starbursts enhanced SQL query engine built using OS Trino and gain secure access to 50+ data sources for a more integrated data estate.

Hybrid is your reality

Modernize from legacy Hadoop to the Dell Data Lakehouse powered by Starburst to gain more powerful and efficient on-premises compute, storage, and analytics while connecting to data in AWS S3, ADLS, GCS, and many more sources.

Cloud centric data architecture

Build an open and interoperable data lakehouse by migrating Hive to Iceberg table formats to support cross-cloud and cross-region price-performant analytics at petabyte scale while democratizing secure data sharing with a single point of access and governance.

Why an open data lakehouse?

Hadoop is a popular open-source framework for storing and processing large-scale data on-premises. However, Hadoop comes with many practical challenges, such as high maintenance costs, complex administration, scalability issues, and a lack of cloud-native features. 

An open data lakehouse is a cost-effective, performant, and future data architecture that is built on an open foundation. A data lakehouse can help simplify and streamline the migration from Hadoop to the cloud. With Starburst, organizations can modernize from Hadoop to the cloud faster, more easily, and cheaply.

 

Why Starburst?

Price-performant analytics at petabyte scale

Both Starburst Enterprise (software) and Galaxy (SaaS) are powered by enhanced open source Trino and designed for analyzing large and complex data sets in and around your data lake – from gigabyte to petabyte scale.

  • Run internet-scale SQL workloads with the power of enhanced Trino – the engine built to replace Hive and Imapala
  • Accelerate interactive queries 40%+ with Warp Speed smart indexing and caching technology
  • Run long-running, memory intensive workloads without the fear of query failure with enhanced fault-tolerant execution

Flexible and secure modernization with a simple user experience

Starburst makes it easy to discover, govern, and share data that enables the management of all your data assets through an easy-to-use interface.

  • Use Starburst to meet the unique requirements of your modernization strategy, whether you’re keeping data on-premises and updating the engine, moving to a hybrid architecture, or making a cloud data lake the center of gravity
  • Secure data based on any input – where it lives, how it is structured, what it contains, or which teams it is relevant to
  • Streamline secure sharing and collaboration between producers and consumers via purpose-built data products

Single point of access and governance

Every data store is a first-class entity in Starburst. Use the architecture that meets your needs today and easily evolve it for tomorrow.

  • Connect to 50+ data sources and manage access through a single entry point
  • Optimized for Apache Iceberg but works with all modern table and file formats, including Delta Lake, Apache Hudi, and Apache Hive
  • Analyze your data cross-region and cross-cloud from a single query

Value across industries

FinServ & Insurance

A top 3 US bank realized Spark/Impala could not scale to meet their risk assessment needs.

With Starburst’s improved performance, scale, and ability to federate across HDFS and other sources, the bank reduced their end-to-end risk modeling time from 2+ days to minutes.

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Manufacturing

As an F&B giant transitioned to ADLS, they turned to Starburst to eliminate silos between cloud and legacy data sources.

By switching from HDInsight and Hive to Starburst and ADLS, the company achieved 75% savings from autoscaling, 42% faster queries, and a holistic view across their portfolio of brands.

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Telecommunications

Comcast built a hybrid analytics platform, powered by Starburst and Trino, to provide end users easy access to datasets across sources.

With the platform, Hadoop jobs are running 10-20x faster than Hive, storage costs are lower, and they’re able to migrate to the cloud without disrupting data access.

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5 considerations for a successful modernization

Evaluate current environment

Begin with assessing your existing Hadoop setup and being clear about what data is staying on-premises and what is moving to the cloud. Analyze data specifics, workflows, dependencies, and desired outcomes to define project scope and objectives. Clearly document the desired end state of your data estate with measurable KPIs.

Select cloud platform

Compare cloud options based on features, compatibility, and costs. Match these with migration goals to identify the optimal cloud solution, potentially spanning multiple platforms.

Design cloud architecture

Map out storage, compute, and analytics layers. Choose scalable storage (e.g., Azure Data Lake, Amazon S3), compute service (Trino), analytics tools, and account for security, governance, and observability.

Plan data migration

Prioritize batch migration over simultaneous transfer for efficiency. Minimize disruption, monitor the process, and ensure business continuity by maintaining data federations between legacy and new systems. Be deliberate about which use cases to migrate first, start with low complexity to build early wins and learnings.

Agile migration execution

Prepare data by cleansing, transforming, and validating it. Choose migration tools like Azure Copy, AWS Transfer, or BigQuery Data Transfer, and ensure incremental data movement for accuracy. Consider managed options or manual scripts.

“Gartner clients have described plans to replace broad, complex suites of jobs running against large, optimized data warehouses by “moving it to Hadoop.” Not surprisingly, many of these projects have not succeeded.”

Merv Adrian and Rick Greenwald, Gartner

Interested in learning more?

Contact Us

Start Free with
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
For more deployment options:
Download Starburst Enterprise

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