The fastest path
from Hadoop
to a data lakehouse
Modernize your Hadoop architecture with Starburst
Why Modernize?
Performance Issues
Challenges with query performance, throughput, and concurrency to meet increasing data and user demands.
Complex Administration
Managing a Hadoop cluster requires significant expertise and resources
Cost Concerns
Traditional on-premise Hadoop clusters can lead to high operational costs
Data staying on-premises
Embracing hybrid architecture
Cloud-centric architecture
Data staying on-premise
- Boost performance: Upgrade Hive/Impala to Trino SQL engine
- Secure integration: Access 40+ data sources securely
- Reduce costs: Optimize existing infrastructure and save costs
Embracing hybrid architecture
- Unified analytics: Connect on-premises and cloud data seamlessly
- Efficient operations: Improve performance with the Dell Data Lakehouse powered by Starburst
- Flexible deployment: Manage data across on-premises and cloud
Cloud-centric architecture
- Scalable analytics: Build an open data lakehouse at petabyte scale
- Centralized governance: Single access and governance point
- Enhanced flexibility: Build an open data lakehouse for high performance
Value across industries
A Top 3 US bank realized Spark/Impala could not scale to meet their risk assessment needs.
With Starburst’s improved performance and scale and ability to federate across HDFS and other sources, the bank reduced end-to-end risk modeling time from 2+ days to minutes.
A Top 3 US bank realized Spark/Impala could not scale to meet their risk assessment needs.
With Starburst’s improved performance and scale and ability to federate across HDFS and other sources, the bank reduced end-to-end risk modeling time from 2+ days to minutes.
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.
As a 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.
Optum’s mission of providing patients a complete view of their health depends in part on providing its analysts with fast, secure access to data.
By deploying Starburst on their Hadoop infrastructure, they achieved 10X faster queries, reduced infrastructure costs by 30%, and projected $8 million in savings.
