As reported by Arik Hesseldahl in Recode, Apache Hadoop vendor Hortonworks has filed for an IPO. The decision by Hortonworks to offer public shares represents the first IPO from a major Hadoop vendor. In fiscal year 2013, Hortonworks reported a loss of $36.6M relative to $11M in revenue. Meanwhile, for the first 9 months of 2014, Hortonworks increased its revenue to $33.3M but posted a loss of $86.7M. The decision by Hortonworks to go public comes after two major capital raises in 2014. In July, HP invested $50M in Hortonworks, following upon the $100M raised by Hortonworks in March. Given the gargantuan capital raises specific to Hortonworks competitors Cloudera and MapR as well, the Big Data landscape should also expect IPOs from Cloudera and MapR in the near future. Meanwhile, more detailed analysis regarding the prospects of Hortonworks executing a successful IPO will emerge in coming weeks in anticipation of the launch of the IPO either in late 2014 or early 2015. In 2011, Hortonworks was spun out of Yahoo, its principal investor. Hortonworks plans to raise up to $100M by means of its IPO.
On Thursday, HP announced an agreement to invest $50M in Hortonworks. HP’s investment builds on the $100M Hortonworks raised in March in a financing red led by funds managed by Blackrock and Passport Capital as well as existing investors. The investment illustrates HP’s commitment to its reseller relationship with Hortonworks that allows it to resell the Hortonworks Data Platform. Moreover, HP plans to continue refining the engineering of its products such that they integrate with YARN, the resource management component of version 2.x of Hadoop. In addition to preparing its products to operate in conjunction with YARN, HP will be integrating its product architecture to optimally perform in conjunction with the Hortonworks Data Platform more generally. Key HP products targeted for integration with the Hortonworks Data Platform include the HP HAVEn platform, one component of which is HP Vertica. As a result of the $50M equity investment, HP’s Executive Vice President and Chief Technology Officer Martin Fink will join the board of directors of Hortonworks. HP’s investment in Hortonworks underscores how the Big Data revolution lies poised to accelerate as technology companies deepen their relationships with Hadoop vendors in anticipation of delivering turnkey big data analytics solutions that simplify and streamline the operationalization of Big Data.
On Thursday, Hortonworks announced that Apache Spark is “YARN Ready” and compatible with the multiple workloads and additional CPU processing-demands specific to Spark applications. As a result of the compatibility of Apache Spark with YARN, Hadoop users can now use one Hadoop cluster with a single repository of data for a variety of purposes rather than having to segment workloads such that some data is dedicated to Apache Spark. More specifically, Hadoop users can now rest assured that YARN-based applications work collaboratively with applications that leverage Spark’s capabilities to facilitate real-time analytics, interactive analytics, machine learning and stream processing. Hortonworks introduced Apache Spark to the Hortonworks Data Platform as a technology preview download in May but today announces the integration of Spark with YARN, its recent acquisition, XA Secure, for authentication and data security purposes, as well as Ambari toward the larger goal of delivering an integrated, turnkey, enterprise-grade Hadoop platform. Thursday’s announcement by Hortonworks responds to similar statements by competitors MapR regarding the integration of Spark into its Hadoop distribution, and Cloudera’s announcement of its enterprise-grade support for Apache Spark.
The following graphic illustrating the integration of Spark into YARN originated from the Hortonworks blog post Making Apache Spark YARN Ready.
Trifacta Partners With Hortonworks To Certify Trifacta Data Transformation Platform On Hortonworks Data Platform
Trifacta today announced that its Trifacta Data Transformation Platform has been certified for use with Hortonworks Data Platform 2.1 (HDP) by means of the Hortonworks Certified Technology Program. The certification ensures the compatibility of the Trifacta Data Transformation Platform with the latest Hortonworks Data Platform and thereby positions Trifacta’s technology to integrate with enterprise-grade deployments of the Hortonworks Hadoop distribution. Today’s announcement further validates the value of the Trifacta Data Transformation Platform as a technology platform that facilitates the derivation of actionable business intelligence from Hadoop by rendering it easier for analysts to visualize and engage with Hadoop-based data in conjunction with machine learning-based suggestions regarding data transformations and analytics. Trifacta’s partnership with Hortonworks builds upon recent news of its $25M Series C raise and the finalization of an analogous collaboration with Hadoop vendor Cloudera. In March, Trifacta announced a partnership with Cloudera that ensures the compatibility of Trifacta’s Data Transformation Platform with the Cloudera Hadoop ecosystem.
Now that Trifacta has inked deals to certify its Data Transformation Platform with the two Hadoop market share leaders, Cloudera and Hortonworks, the Big Data space should expect enterprise deployments of its platform to accelerate as Trifacta solidifies its branding as the de facto platform for the transformation, cleansing and guided exploration of Hadoop-based data. The platform’s value proposition consists in the reduction of time to insight with respect to actionable business intelligence derived from Hadoop-based data, its ability to enhance analyst productivity and to iteratively deliver more nuanced guidance regarding data transformations of interest by means of its machine learning-based technology. Expect Trifacta to continue expanding its range of strategic partnerships in the forthcoming months as it leverages its recent funding to position itself at the forefront of enterprise technologies regarding the effective operationalization of Big Data.
On Thursday, Hortonworks announced the acquisition of XA Secure, a company that delivers security solutions for Hadoop. XA Secure provides an integrated suite of Hadoop security solutions that addresses security concerns involving Administration, Authentication, Authorization, Audit and Data Protection as illustrated below:
To date, Hortonworks has worked on security issues related to access control in HDFS, DBA-compatible grant or revoke functionality for Apache Hive and a single point of secure access to Hadoop clusters by means of Apache Knox. XA Secure provides an integrated security solution for Hadoop that centralizes security management, authenticates users, integrates access control policies, provides a comprehensive audit trail and protects data in motion and at rest. The XA Secure solution works with batch Hadoop solutions in addition to real-time Hadoop solutions and interactive SQL. True to its open-source roots, Hortonworks plans to enhance XA Secure and then return the results to the open source community by incubating a new project within the Apache Software Foundation in the latter half of 2014. Hortonworks will make XA Secure available to Hortonworks customers under the branding HDP Security in late June 2014. The decision by Hortonworks to purchase XA Secure with the intent of open-sourcing the technology once it has matured represents an astute play to carve out a leadership position in the rapidly evolving Hadoop security space by delivering one of the first turnkey Hadoop security solutions available today, Cloudera’s Apache Sentry admitted and notwithstanding. In the meantime, however, Hortonworks customers stand to enjoy enhanced security functionality as the XA Secure acquisition becomes progressively integrated into the Hortonworks Data Platform and experiences a slew of enhancements slotted into an aggressive product development timeline.
Today, Concurrent, Inc. announces the release of Cascading 3.0, the latest version of the popular open source framework for developing and managing Big Data applications. Widely recognized as the de facto framework for the development of Big Data applications on platforms such as Apache Hadoop, Cascading simplifies application development by means of an abstraction framework that facilitates the execution and orchestration of jobs and processes. Compatible with all major Hadoop distributions, Cascading sits squarely at the heart of the Big Data revolution by streamlining the operationalization of Big Data applications in conjunction with Driven, a commercial product from Concurrent that provides visibility regarding application performance within a Hadoop cluster.
Today’s announcement extends Cascading to platforms and computational frameworks such as local in-memory, Apache MapReduce and Apache Tez. Going forward, Concurrent plans for Cascading 3.0 to ship with support for Apache Spark, Apache Storm and other computational frameworks by means of its customizable query planner, which allows customers to extend the operation of Cascading to compatible computational fabrics as illustrated below:
The breakthrough represented by today’s announcement is that it renders Cascading extensible to a variety of computational frameworks and data fabrics and thereby expands the range of use cases and environments in which Cascading can be optimally used. Moreover, the customizable query planner featured in today’s release allows customers to configure their Cascading deployment to operate in conjunction with emerging technologies and data fabrics that can now be integrated into a Cascading deployment by means of the functionality represented in Cascading 3.0.
Used by companies such as Twitter, eBay, FourSquare, Etsy and The Climate Corporation, Cascading boasts over 150,000 applications a month, more than 7,000 deployments and 10% month over month growth in downloads. The release of Cascading 3.0 builds on Concurrent’s recent partnership with Hortonworks whereby Cascading will be integrated into the Hortonworks Data Platform and Hortonworks will certify and support the delivery of Cascading in conjunction with its Hadoop distribution. Concurrent, Inc. also recently revealed details of a strategic partnership with Databricks, the principal steward behind the Apache Spark project, that allows it to “operate over Spark…[the] next generation Big Data processing engine that supports batch, interactive and streaming workloads at scale.” In an interview with Cloud Computing Today, Concurrent CEO Gary Nakamura confirmed that Concurrent plans to negotiate partnerships analogous to the agreement with Hortonworks with other Hadoop distribution vendors in order to ensure that Cascading consolidates its positioning as the framework of choice for the development of Big Data applications. Overall, the release of Cascading 3.0 represents a critical product enhancement that positions Cascading to operate over a broader pasture of computational frameworks and consequently assert its relevance for Big Data application development in a variety of data and computational frameworks. More importantly, however, the product enhancement in Cascading 3.0, in conjunction with the partnership with Databricks regarding Apache Spark, suggests that Cascading is well on its way to becoming the universal framework of choice for developing and managing applications in a Big Data environment, particularly given its compatibility with a wide range of Hadoop distributions and data and computational frameworks.
Concurrent and Hortonworks recently revealed a deepening of their strategic relationship whereby Cascading SDK will now be integrated into the Hortonworks Data Platform. Moreover, Hortonworks will certify, deliver and support Cascading, the application framework for developing Hadoop-based applications. A Java-based, open source alternative to MapReduce, Cascading provides developers with a framework for constructing complex, repeatable data processing tasks within a Hadoop cluster. Cascading features an abstraction platform which uses plumbing metaphors such as taps, pipes, data flows, cascades and sinks to allow developers to design, visualize and execute jobs and processes on Hadoop-based data without having to master the intricacies of MapReduce. Forthcoming releases of Cascading will support Apache Tez, an initiative that represents the next step after the addition of YARN to Hadoop that allows for Hadoop-based data to “meet demands for fast response times and extreme throughput at petabyte scale.” The partnership between Concurrent, the developer of Cascading, and Hortonworks, represents a huge coup for Concurrent given that the collaboration stands to rapidly accelerate Cascading’s adoption in enterprise environments. Hortonworks, meanwhile, benefits from packaging its Hadoop distribution with Cascading, one of the industry’s most well respected frameworks for Big data management and application development that boasts enterprise users such as Twitter, LinkedIn, eBay and Nokia. The obvious question now is whether Concurrent will finalize similar partnerships with other Hadoop vendors such as Cloudera and MapR or whether Concurrent’s partnership with Hortonworks enables the latter to improve its positioning in the battle for Hadoop market share, particularly in light of Cloudera’s remarkable $900 capital raise and partnership with Intel.