To truly unleash the power of Apache™ Hadoop® for business insights and predictive analytics, you need SQL. Hadoop Native SQL with Pivotal HDB, powered by Apache HAWQ (incubating), represents a new generation of high performance, advanced analytics and machine learning that transforms Hadoop into an enterprise analytical database. Move and analyze entire workloads, while simplifying management and expanding the breadth of data access and analytics, all natively in Hadoop.
Quickly unlock business insights with exceptional performance
Run predictive analytics with higher accuracy by using your entire dataset, not just a portion of it, directly in Hadoop. Execute near-real-time, ad-hoc queries at scale. Complete analytics tasks faster – in seconds or minutes, not hours or days.
Integrate SQL BI tools with confidence
Tap into a large ecosystem of data analysis and data visualization tools such as SAS, Tableau and more. Leverage existing SQL skills to ramp up and execute quickly in Hadoop.
Iterate advanced analytics and machine learning in database
Utilize advanced statistical functions and algorithms to spot relationships, patterns, and trends - with speed and at scale. Operate directly in Hadoop data.
The future of SQL on Hadoop is Hadoop Native SQL. Pivotal HDB is an elastic SQL query engine that combines exceptional MPP-based analytics performance, robust ANSI SQL compliance, and integrated Apache MADlib (incubating) machine learning – enabling you to run fast ad hoc queries and fast predictive analytics. Evolved from over a decade’s worth of intellectual property from Pivotal Greenplum™ and open source PostgreSQL, Pivotal HDB operates natively in Hadoop, which simplifies overall system management of cluster resources. Other Pivotal HDB features include:
Pivotal HDB’s parallel processing architecture delivers high performance throughput and low latency (potentially, near-real-time) query responses that can scale to petabyte-sized datasets. Pivotal HDB also features a cutting-edge, cost-based SQL query optimizer and dynamic pipelining technology for efficient performance operation.
Robust ANSI SQL Compliance
Pivotal HDB complies with ANSI SQL-92, -99, and -2003 standards, plus OLAP extensions. Leverage existing SQL expertise and existing SQL-based applications and BI/data visualization tools. Execute complex queries and joins, including roll-ups and nested queries.
Deep Analytics and Machine Learning
10+ years of massively parallel processing development from Greenplum, Postgres, and MADlib at your fingertips. Pivotal HDB integrates statistical and machine learning capabilities that can be natively invoked from SQL and applied natively to large data sets across a Hadoop cluster. Pivotal HDB supports PL/Python, PL/Java and PL/R programming languages.
Flexible Data Format Support
HDB supports multiple data file formats including Apache Parquet and HDB binary data files, plus HBase and Avro via HDB’s Pivotal Extension Framework (PXF) services. HDB interfaces with HCatalog, which enables you to query an even broader range of data formats.
Tight Integration with Hadoop Ecosystem
Pivotal HDB plugs into the Apache Ambari installation, management and configuration framework. This provides a Hadoop-native mechanism for installation and deployment of Pivotal HDB and for monitoring cluster resources across Pivotal HDB and the rest of the Hadoop ecosystem.
ODPi Hadoop Distribution Support
Pivotal HDB integrates out-of-the-box with Hortonworks HDP. With future releases, Pivotal HDB will integrate with the ODPi core.
GE Shores Up Savings For Aviation With a Data Lake
Thank you for your interest!
We will get back to you shortly.