Improve your performance over Hive and Impala

Faster queries. More connectivity. At scale.

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Starburst customers report an average 10x increase in performance, 60% reduction in total cost of ownership, and significant reduction in ETL for ad hoc analytics.

Improve performance by 2-20x

• Improve query speed
• Reduce failed queries
• Enable scale with concurrency

Reduce TCO by 60-75%

• Separation of compute and storage.
• Reduce time and money
spent on ETL

Reduce ETL for ad hoc analytics

• Enable faster time to insights

We know the problem all too well

Hive

Customers look to Starburst Enterprise when they are experiencing an intensely slow query turnaround from their existing Hadoop, Spark, or Hive infrastructures. Hive was created in 2008 at Facebook for long-running ETL workloads, and was used for querying and analytics. In fact, the genesis of Starburst Enterprise’s core open-source engine, Trino, formerly known as Presto® SQL, came about due to these slow Hive query conditions at Facebook back in 2012.

Impala

Impala improves on Hive’s speed, however, it’s best used as a departmental solution with smaller data sets and which don’t require high concurrency. Unlike Impala, Starburst Enterprise is not bound by Hadoop and can federate across numerous other sources. Starburst is a stand-alone technology not tied to any legacy technology. We enable businesses to leverage a real RDBMS, in addition to distributed storage, and offload the majority of workload they have on Impala onto something that delivers the responsiveness they need.


Starburst Enterprise

Starburst Enterprise delivers better performance, more connectivity, and lower total cost of ownership. Customers moving from Hive and Impala to Starburst Enterprise are experiencing the ability to scale, improve performance, federate data across different sources, and continue to maintain security with Ranger, Sentry.  Starburst Enterprise allows you to analyze all your data no matter where it lies, and it can be installed and deployed anywhere.  

Compare the features

 

Features Hive Impala Starburst Enterprise
Connectivity HDFS, S3, ADLS HDFS, S3, ADLS HDFS, S3, ADLS, + 40 other enterprise sources
Data Federation
Concurrency Low Low High
No Dependency on Hadoop
No requirement for YARN
Ranger/Sentry Integration
Latency High High Low

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Starburst customer results

Food & Beverage Giant Shifts to a Single Point of Access for All Data

While transitioning to Azure Data Lake Storage, this company has turned to Starburst to achieve secure, cost-effective data analytics at scale.  In this case study, Starburst Enterprise allows them to analyze data where it lies, with high-performance connectors to several data storage platforms.

Read the case study

Prominent real estate company modernized their data architecture for 1,100 business analysts and ½ petabyte of data.

With their Hive on Cloudera Hadoop delivering slow query response times, a prominent real estate company chose Starburst to scale.

“Worlds Most Admired” in healthcare achieved 2x faster performance, with lower total cost of ownership, and fewer resources

After moving from their Hadoop & MapR environment to Starburst, they are in the process of saving millions, thanks to faster, better claims analytics.

Top 10 global pharmaceutical giant reduced manufacturing time-to-market by 50%

This customer leveraged Starburst to remove Impala and create a single query layer across all their data sets.

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Improve your performance. Starburst will help with faster queries, more connectivity, and scalability. Contact us. We're here to help.

Starburst Enterprise

Fast, free, distributed SQL query engine for big data analytics

Starburst offers a free, unlicensed version which includes:

  • Certified and secure releases
  • Cost-based optimization for federated queries
  • Connector extensions, including table statistics
  • Usage metrics

Data leaders trust Trino (formerly PrestoSQL)

Netflix, Verizon, FINRA, AirBnB, Comcast, Yahoo, and Lyft are powering some of the biggest analytic projects in the world with Trino.

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