Three Key Features Of EMC’s Hadoop Distribution, Pivotal HD

This week, EMC launched its own distribution of Hadoop under the branding Pivotal HD. Built on technology that EMC obtained through the acquisition of Greenplum in July 2010, Pivotal HD represents EMC’s next iteration on the Greenplum Unified Analytics Platform (UAP) that it launched in December 2011. The Greenplum UAP featured EMC Greenplum HD, an enterprise-grade distribution of Hadoop and Greenplum’s database for structured data. Greenplum UAP also announced Greenplum Chorus, an innovative platform for collaboration amongst data scientists in an organization leveraging Big Data. Pivotal HD, however, marks a significant new chapter in EMC’s Hadoop technology as indicated by its array of features and architectural complexity.

Like many recent Hadoop distributions and technologies, Pivotal HD integrates with SQL to facilitate its maximal usage by developers and business analysts who lack familiarity with MapReduce. But the real innovation of Pivotal HD runs deeper than its integration of SQL with Hadoop and concerns the positioning of Greenplum’s analytic engine alongside HDFS in ways that enable performance enhancements to Hadoop querying over and beyond the simple appendage of a SQL interface. Pivotal HD’s Advanced Database Services (HAWQ) allows for the delivery of a high-performance SQL engine that permits of greater SQL functionality and performance than analogous SQL interfaces such as Hive, Hadapt and Impala. Coupled with Pivotal HD’s virtualization and pluggable storage compatibility features, the platform represents a distinct moment of innovation in the Hadoop space as evinced by the following three features:

Advanced Database Services (HAWQ)
Pivotal HD’s Advanced Database Services (HAWQ) functionality brings Greenplum’s Massively Parallel Processing (MPP) functionality to Hadoop. The result means that HAWQ allows Pivotal HD users to perform complex joins, MADlib in-database analytics and transactions. Moreover, users have the luxury of leveraging virtually any BI tool on the marketplace to obtain advanced reporting and visualization of data as required. HAWQ-based SQL queries outperform Hive in terms of response time by as much as 100x according to EMC benchmarking data.

The Advanced Database Service interfaces with other components of Pivotal HD as follows:

EMC Pivotal HD

Given the recent proliferation of SQL-Hadoop interfaces throughout the industry, customers and analysts should expect more data about the comparative efficiencies of SQL-Hadoop interfaces to emerge as more and more SQL-trained analysts start using SQL to operate on data saved in HDFS.

Hadoop Virtualization Extensions
Hadoop Virtualization Extensions enable the provisioning of Hadoop clusters on VMware virtualized platforms in both public cloud and on-premise environments. HVE provides customers increased flexibility of deployment and enables the construction of high availability infrastructures for the access of Hadoop data.

Pluggable HDFS Storage
Customers can multiply their data storage options by using standard Hadoop direct attached storage in addition to EMC Isilon OneFS Scale-Out NAS Storage, the latter of which features streamlined loading, backup, replication, snapshotting and elastic scalability functionality.

Analysis
Overall, EMC’s launch into the Hadoop-distribution world represents a stunning and significant move to grab Hadoop market share from Cloudera, Hortonworks and MapR. Unlike Intel’s recently launched distribution, EMC’s Pivotal HD claims some proprietary and genuinely innovative Hadoop technology in the form of its Advanced Database Services engine and scale-out storage compatibility. Expect EMC to continue to innovate upon its core technology platform and follow the suit of the likes of Concurrent in developing tools to render Hadoop more accessible to Java-based developers in addition to SQL. What remains unclear, at this point, is the extent to which EMC will open-source its technology as it gains market share within the enterprise. For now, however, the Hadoop world has yet another significant player with cash reserves aplenty to continue to innovate on its platform and disrupt the Hadoop landscape in the process.

Oracle Partners With Cloudera For Newly Available Big Data Appliance

On Tuesday, Oracle declared the availability of the Big Data appliance that it introduced to the world at its October conference Oracle Open World. The appliance runs on Linux and features Cloudera’s version of Apache Hadoop (CDH), Cloudera Manager for managing the Hadoop distribution, the Oracle NoSQL database as well as an open source version of R, the statistical software package. Oracle’s partnership with Cloudera in delivering its Big Data appliance goes beyond the latter’s selection as a Hadoop distributor to include assistance with customer support. Oracle plans to deliver tier one customer support while Cloudera will provide assistance with tier two and tier three customer inquiries, including those beyond the domain of Hadoop.

Oracle will run its Big Data appliance on hardware featuring 864 GB main memory, 216 CPU cores, 648 TB of raw disk storage, 40 Gb/s InfiniBand connectivity and10 Gb/s Ethernet data center connectivity. Oracle also revealed details of four connectors to its appliance with the following functionality:

• Oracle Loader for Hadoop to load massive amounts of data into the appliance by using the MapReduce parallel processing technology.
• Oracle Data Integrator Application Adapter for Hadoop which provides a graphical interface that simplifies the creation of Hadoop MapReduce programs.
• Oracle Connector R which provides users of R streamlined access to the Hadoop Distributed File System (HDFS)
• Oracle Direct Connector for Hadoop Distributed File System (ODCH), which supports the integration of Oracle’s SQL database with its Hadoop Distributed File System.

Oracle’s announcement of the availability of its Big Data appliance comes as the battle for Big Data market share takes shape in a landscape dominated by the likes of Teradata, Microsoft, IBM, HP, EMC, Informatica, MarkLogic and Karmasphere. Oracle’s selection of Cloudera as its Hadoop distributor indicates that it intends to make a serious move into the world of Big Data. For one, the partnership with Cloudera gives Oracle increased access to Cloudera’s universe of customers. Secondly, the partnership enhances the credibility of Oracle’s Big Data offering given that Cloudera represents that most prominent distributor of Apache Hadoop in the U.S.

In October, Microsoft revealed plans for a Big Data appliance featuring Hadoop for Windows Server and Azure, and Hadoop connectors for SQL Server and SQL Parallel Data Warehouse. Whereas Oracle chose Cloudera for Hadoop distribution, Microsoft partnered with Yahoo spinoff Hortonworks to integrate Hadoop with Windows Server and Windows Azure. In late November, HP provided details of Autonomy IDOL (Integrated Data Operating Layer) 10, which features the ability to process large-scale structured data sets in addition to a NoSQL interface for loading and analyzing structured and unstructured data. In December, EMC released its Greenplum Unified Analytics Platform (UAP) marked by the ability to load structured data, enterprise-grade Hadoop for analyzing structured and unstructured data and Chorus, a collaboration and productivity software tool. Bolstered by its partnership with Cloudera, Oracle is set to compete squarely with HP’s Autonomy IDOL 10, EMC’s Greenplum Chorus and IBM’s BigInsights until Microsoft’s appliance officially enters the Big Data doohyoo (土俵) qua sumo ring as well.

Big Data 2011: The Year in Review

If 2011 was the year of Cloud Computing, then 2012 will surely be the year of Big Data. Big Data has yet to arrive in the way cloud computing has, but the framework for its widespread deployment as a commodity emerged with style and unmistakable promise. For the first time, Hadoop and NoSQL gained currency not only within the developer community, but also amongst bloggers and analysts. More importantly, Big Data garnered for itself a certain status and meaning in the technology community even though few people asked about the meaning of big in “Big Data” in a landscape where the circle around the meaning of “big” with respect to “data” is constantly being redrawn. Even though yesterday’s “big” in Big Data morphed into today’s “small” as consumer personal storage transitions from gigabytes to terabytes, the term “Big Data” emerged as a term that everyone almost instantly understood. It was as if consumers and enterprises alike had been searching for years for a long lost term to describe the explosion of data as evinced by web searches, web content, Facebook and Twitter feeds, photographs, log files and miscellaneous structured and unstructured content. Having been speechless, lacking the vocabulary to find the term for the data explosion, the world suddenly embraced the term Big Data with passion.

Below are some of the highlights of 2011 with respect to big data:

March
•Teradata finalized a deal to acquire Big Data player Aster Data Systems for $263 million.

July
•Yahoo revealed plans to create Hortonworks, a spin-off dedicated to the commercialization of Apache Hadoop.

September
Teradata announced the Teradata Aster MapReduce Platform that combines SQL with MapReduce. The Teradata Aster MapReduce Platform empowers business analysts who know SQL to leverage the power of MapReduce without having to write scripted queries in Java, Python, Perl or C.

October
Oracle announced plans to launch a Big Data appliance featuring Apache Hadoop, Oracle NoSQL Database Enterprise Edition and an open source distribution of R. The company’s announcement of its plans to leverage a NoSQL database represented an abrupt about face of an earlier Oracle position that discredited the significance of NoSQL.
Microsoft revealed plans for a Big Data appliance featuring Hadoop for Windows Server and Azure, and Hadoop connectors for SQL Server and SQL Parallel Data Warehouse. Microsoft revealed a strategic partnership with Yahoo spinoff Hortonworks to integrate Hadoop with Windows Server and Windows Azure. Microsoft’s decision not to leverage NoSQL and use instead a Windows based version of Hadoop for SQL Server 2012 constituted the key difference between Microsoft and Oracle’s Big Data platforms.
IBM announced the release of IBM Infosphere BigInsights application for analyzing “Big Data.” The SmartCloud release of IBM’s BigInsights application means that IBM beat competitors Oracle and Microsoft in the race to deploy an enterprise grade, cloud based Big Data analytics platform.

November
•Christophe Bisciglia, founder of Cloudera, the commercial distributor of Apache Hadoop, launched a startup called Odiago that features a Big Data product named WibiData. WibiData manages investigative and operational analytics on “consumer internet data” such as website traffic on traditional and mobile computing devices.
Cloudera announced a partnership with NetApp, the storage and data management vendor. The partnership revealed the release of the NetApp Open Solution for Hadoop, a preconfigured Hadoop cluster that combines Cloudera’s Apache Hadoop (CDH) and Cloudera Enterprise with NetApp’s RAID architecture.
•Big Data player Karmasphere announced plans to join the Hortonworks Technology Partner Program today. The partnership enables Karmasphere to offer its Big Data intelligence product Karmasphere Analytics on the Apache Hadoop software infrastructure that undergirds the Hortonworks Data Platform.
Informatica released the world’s first Hadoop parser. Informatica HParser operates on virtually all versions of Apache Hadoop and specializes in transforming unstructured data into a structured format within a Hadoop installation.
MarkLogic announced support for Hadoop, the Apache open source software framework for analyzing Big Data with the release of MarkLogic 5.
HP provided details of Autonomy IDOL (Integrated Data Operating Layer) 10, a Next Generation Information Platform that integrates two of its 2011 acquisitions, Vertica and Autonomy. Autonomy IDOL 10 features Autonomy’s capabilities for processing unstructured data, Vertica’s ability to rapidly process large-scale structured data sets, a NoSQL interface for loading and analyzing structured and unstructured data and solutions dedicated to the Data, Social Media, Risk Management, Cloud and Mobility verticals.

December
EMC announced the release of its Greenplum Unified Analytics Platform (UAP). The EMC Greenplum UAP contains the The EMC Greenplum platform for the analysis of structured data, enterprise-grade Hadoop for analyzing structured and unstructured data and EMC Greenplum Chorus, a collaboration and productivity software tool that enables social networking amongst constituents in an organization that are leveraging Big Data.

The widespread adoption of Hadoop punctuated the Big Data story of the year so far. Hadoop featured in almost every Big Data story of the year, from Oracle to Microsoft to HP and EMC, while NoSQL came in a close second. Going into 2012, one of the key questions for the Big Data space concerns the ability of OpenStack to support Hadoop, NoSQL, MapReduce and other Big Data technologies. The other key question for Big Data hinges on the user friendliness of Big Data applications for business analysts in addition to programmers. EMC’s Greenplum Chorus, for example, democratizes access to its platform via a user interface that promotes collaboration amongst multiple constituents in an organization by transforming questions into structured queries. Similarly, the Teradata Aster MapReduce Platform allows business analysts to make use of its MapReduce technology by using SQL. That said, as Hadoop becomes more and more mainstream, the tech startup and data intensive spaces are likely to witness a greater number of data analysts trained in Apache Hadoop in conjunction with efforts by vendors to render Hadoop more accessible to programmers and non-programmers alike.

Big Data Goes Social With EMC’s Greenplum Unified Analytics Platform

EMC announced the release of its Greenplum Unified Analytics Platform (UAP) on Thursday. The Greenplum Unified Analytics Platform, a unified platform for processing structured and unstructured data, represents EMC’s latest move to consolidate its positioning in the Big Data space and compete squarely with Big Data offerings recently elaborated by Oracle, Microsoft and HP. EMC’s announcement comes scarcely two weeks after HP’s disclosure of the integration of its Autonomy and Vertica offerings within a unified Next Generation Information Platform called Autonomy IDOL 10 that specializes in the processing of structured and unstructured data. EMC’s Unified Analytics Platform features integration with Hadoop, the software framework for analyzing massive amounts of structured and unstructured data.

The EMC Greenplum UAP contains the following three components:

• The EMC Greenplum platform for the analysis of structured data.
• Enterprise-grade Hadoop for analyzing structured and unstructured data.
• EMC Greenplum Chorus, a collaboration and productivity software tool that enables social networking amongst constituents in an organization that are leveraging Big Data.

EMC Greenplum Chorus recognizes the way in which Big Data scientists and analysts may be geographically dispersed across different enterprise locations, even as they need to collaborate to deliver enterprise-wide analysis that integrates structured and unstructured data from different data sets. GigaOM reports data exploration represents one of the most significant features of Chorus because it provides users with a Facebook-like user interface which enables data scientists to “launch a sandbox environment and start analyzing the data with just a few clicks.” According to EMC’s press release, Chorus facilitates collaboration amongst Big Data teams as follows:

EMC Greenplum Chorus opens data science teams up to an entirely new way to collaborate across dispersed geographies and with very large data sets. Through the Chorus interface, users get ready access to tools, data and supporting resources that enable enterprise-wide Big Data productivity. Frictionless and rapid collaboration across data science teams helps to ensure useful insights get back to the business in time to take the right actions, thus increasing agility and innovation.

Like IBM’s artificial intelligence supercomputer Watson, Chorus provides an interface for translating human questions into queries that run against petabytes of data. Chorus also allows users to share results from and refine approaches to data analysis. The social networking component of EMC’s Unified Analytics Platform ensures that diverse constituents can examine Big Data and iteratively refine their approach to data analysis as a collective. Chorus, the collaborative platform of UAP, profoundly differentiates EMC’s Big Data offering from competing products from HP, Oracle, Microsoft, Cloudera and Odiago.

EMC’s Unified Analytic Platform represents the convergence of the hottest trends in technology today: cloud computing, Big Data, virtualization and social networking. The question now is whether social networking and Big Data represents a fad that will pass, or an innovation that forever changes the landscape of products in the Big Data space.