Hadapt 1.0 combines the benefits of Apache Hadoop and relational DBMS for big data applications that rely on multi-structured data analytics
Cambridge, MA – November 8, 2011
Hadapt, creator of the first big data platform that combines the benefits of Hadoop and relational DBMS into a single platform, today announced that its flagship product is available for Early Access for organizations looking to store and effectively analyze large, diverse data sets. Backed by Bessemer Venture Partners, Norwest Venture Partners, and big data industry veterans, Hadapt is evolving big data management to encompass structured and unstructured data management within a single system. The company’s solution offers a scalable, high performance analytic platform for applications that rely on diverse data types. By combining Hadoop with a structured data store and integrating a unique universal query execution layer, Hadapt’s solution is a significant leap forward towards expanding the class of analytic applications that organizations can run. Further, organizations also benefit from the scalability, higher performance, and cost advantages of Hadapt’s all-in-one architecture.
“If you need to do investigative analytics on multi-structured data, Hadoop can be great for some steps in the process, while relational database management systems are best for other stages,” said Curt Monash, President of Monash Research and Editor of DBMS 2. “Hadapt’s approach to integrating Hadoop and RDBMS into a single analytic platform contains some very interesting capabilities.”
Hadapt 1.0 integrates relational DBMS into Hadoop using a unique, adaptive architecture to provide a parallel big data platform capable of querying large volumes of both structured and unstructured data. This technology enables fast, actionable business insights. For instance, structured sales and inventory data can be combined with unstructured customer reviews, email archives, and other diverse data for new insights into operations, sales, company financials and other business-critical information.
The key highlights of Hadapt’s solution include:
- All-in-One System: One platform handles all “multi-structured” data analytics, eliminating the need to manage different data types in separate, expensive silos. For organizations, this translates into lower TCO for big, diverse data volumes; far richer analytics through consumption of diverse data types; and higher performance by eliminating the slow and massive data movement between Hadoop and structured MPP DBMSs that organizations struggle with today.
- Universal SQL support: Data stored in Hadapt is accessible via existing SQL tools, thus making it easier for business analysts already familiar with SQL to adopt Hadoop and to perform new multi-structured data analytics while continuing to use existing SQL-compatible BI tools. Derived from the SQL support of its underlying relational DBMS, Hadapt’s SQL is richer and more standards-compliant than that of Apache Hive.
- Enormous performance improvements over Hadoop+Hive: Hadapt’s benchmarks show that Hadapt v1.0 performs an order of magnitude faster than Hadoop with Hive. In addition, Hadapt’s adaptive query execution technology provides on-the-fly query load balancing and fault tolerance, critical for fast, consistent, and scalable performance in cloud environments.
“Storing and analyzing structured and unstructured data in a single system presents numerous technical challenges,” said Professor Daniel Abadi of Yale University, co-founder and Chief Scientist at Hadapt. “For example, complex, ad-hoc queries over diverse data types are difficult to optimize and data can have significant, unpredictable skew. Hadapt’s adaptive execution engine and tight Hadoop integration with the relational data model are the key technical components towards solving these problems.”
To access Hadapt Version 1.0 and the Early Access program, please visitwww.hadapt.com/earlyaccess.
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