Learn more about enterprise SQL on Hadoop engine
Leverage SQL Applications and Reduce Vendor Lock-In
With a rich and compliant Structured Query Language (SQL) dialect, Pivotal HAWQ® supports application portability and a large ecosystem of data analysis and data visualization tools such as SAS, Tableau and more. Analytic applications written over HAWQ are easily portable to other SQL compliant data engines, and vice versa. This prevents vendor lock-in for the enterprise and fosters innovation, while containing business risk.
Reduce the Cost of Traditional EDW Workloads
Dynamic pipelining and an efficient cost-based query optimizer enable Pivotal HAWQ to perform queries on Hadoop with the speed and scalability required for enterprise data warehouse (EDW) workloads. By leveraging Hadoop on commodity infrastructure, Pivotal HAWQ dramatically reduces the cost per volume of data analyzed, while providing high availability and fault tolerance for business-critical analytics workloads.
Accelerate Analytics Development and Execution
Pivotal HAWQ provides strong support for low-latency analytic SQL queries, coupled with massively parallel machine learning capabilities. This enables discovery-based analysis of large data sets and rapid, iterative development of data analytics applications that apply deep machine learning – significantly shortening data-driven innovation cycles for the enterprise.
Reduced ETL Processing and Data Movement
Pivotal HAWQ natively supports various Hadoop file formats. reduces extract, transform and load (ETL) processing and data movement, and directly contributes to lower cost of ownership of the data analytics solution. Pivotal HAWQ data federation capabilities are the most advanced in the industry. They enable enterprises to implement end-to-end data analytics initiatives, without having to execute major data transformation projects as a prerequisite.
Features
Pivotal HAWQ’s maturity comes from leveraging a decade worth of product development effort that was invested in Greenplum Database™. It provides superior SQL on Hadoop performance, scalability and coverage, massively-parallel machine learning capabilities and support for native Hadoop file formats. In addition, advanced features include support for complex joins, rich and compliant SQL dialect and industry-differentiating data federation capabilities.
High-Performance Architecture
Pivotal HAWQ supports a rich SQL dialect with complex join operations at breakthrough performance. It also leverages a cutting-edge, cost-based SQL query optimizer (called Pivotal Query Optimizer) coupled with dynamic pipelining technology. Together, these features minimize data transport overhead in performing SQL joins. This is an industry-first in SQL on Hadoop implementations and is evidenced from the performance, coverage and completeness of TPC-DS benchmarks.
Fully-Compliant SQL Support
Pivotal HAWQ is 100% ANSI (American National Standards Institute) SQL compliant and supports open database connectivity (ODBC) and Java database connectivity (JDBC). Most business intelligence, data analysis and data visualization tools work with Pivotal HAWQ out of the box. With the Pivotal eXtension Framework (PXF) module, Pivotal HAWQ can federate data from external data sources – like Analytical/Enterprise Data Warehouses, Hbase and Hive instances. In addition to the usual data federation features, PXF provides industry-differentiating capabilities with SQL on Hadoop. This minimizes data movement and makes it expansible to other data sources.
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Deep Analytics and Machine Learning
Pivotal HAWQ integrates statistical and machine learning capabilities that can be natively invoked from SQL or (in the context of PL/Python, PL/Java or PL/R) in massively parallel modes, applied to large data sets across a Hadoop cluster. These capabilities are provided through MADlib – an open source, parallel machine-learning library.
Native Hadoop File Format Support
Pivotal HAWQ supports Apache Avro, Parquet and native HDFS file formats in Hadoop. This minimizes the need for ETL during data ingest, decreases data movement during analytics processing and enables schema-on-read processing.
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