Hortonworks Leads the Industry in Performance and Customer Choice with HDP 2.6

Real-Time, Operational Analytics Delivered Directly from the Data Lake

Hortonworks, Inc. (NASDAQ: HDP), a leading innovator of open and connected data platforms, today announced the general availability of Hortonworks Data Platform (HDP®) version 2.6, which leverages open source innovations to deliver enhanced enterprise features. HDP is the industry's only true secure, enterprise-ready open source Apache™ Hadoop® distribution that addresses the complete needs of data-at-rest, powers real-time customer applications and delivers robust analytics that accelerate decision making and innovation. With HDP 2.6, customers benefit from interactive query in seconds, enhanced data science, enterprise-grade security and streamlined operations—in the cloud and on-premises—to harvest value from their data faster than previously possible.

“HDP 2.6 showcases the advantages of the open source community. Significant innovation is coming out of the Apache community, and because of our commitment to delivering an open platform, we are uniquely able to bring these value-creating capabilities to customers,” said Scott Gnau, chief technology officer at Hortonworks. “HDP 2.6 introduces key new enterprise features and performance improvements that will benefit our customers immediately—no application re-write required.”

Hortonworks is the first in the industry to fully support Apache Hive 2.0 with LLAP functionality for intelligent in-memory caching. With the general availability of Hive with LLAP, customers will benefit from a dramatic performance boost for SQL interactive queries. In addition, HDP 2.6 introduces ACID merge functionality to enable additional use cases for optimizing existing Enterprise Data Warehouse investments without requiring all data to be re-loaded.

In addition, HDP 2.6 includes the following new capabilities:
  • Data Science at Scale: Improved user experience for data scientists with Spark 2.1 and the latest version of Zeppelin.
  • Enterprise-Grade Security: Enhancements to Ranger and Atlas include reduced sync time for customers with large user bases and enhanced bulk addition of policies from one environment to another via expanded tag-based policy support for Spark, Zeppelin, HDFS, Kafka and Hbase.
  • Streamlined and Proactive Operations: Easy configuration of services and components when a cluster node restarts with the latest version of Ambari, as well as immediate understanding of the most frequent operations being performed. In addition, SmartSense has been enhanced to automate the application of the recommendations for cluster improvement.
Hortonworks Data Platform 2.6 Offers Customers Complete Flexibility for Deployment

Hortonworks has delivered the industry’s first connected data architecture, allowing customers the flexibility to easily manage and analyze data in the data center and in the cloud. With rapidly growing cloud adoption, Hortonworks’ “cloud-first” strategy delivered HDP 2.6 first on Microsoft Azure HDInsight and Hortonworks Data Cloud for AWS.

Additionally, HDP 2.6 is also available on IBM Power Systems. The work Hortonworks and IBM are doing together, including their support for ODPi, gives customers increased choice when selecting a top-tier distribution for Hadoop and Spark and enables them to fully exploit the performance, scalability and acceleration capabilities of the POWER8 platform. With multiple deployment options in any environment, customers benefit from increased choice for HDP.

Supporting Quotes
  • “There is a need to improve SQL performance and support, along with Spark adoption in Hadoop related workloads. A key enhancement is the addition of Upsert support, which is essential for building confidence in data currency and making Hadoop BI-ready. Backing Hive with LLAP and Spark 2.1 should produce the kinds of service levels that BI users expect. With the ‘cloud-first’ strategy, Hortonworks is going where more and more new Hadoop workloads are heading.” – Tony Baer, Principal Analyst, Ovum
  • “At Geisinger Health System, we’re using HDP to optimize our enterprise data warehouse, with the immediate goal of enriching clinical data with additional data such as billing and claims records. Apache Hive is a key part of that optimization program. Over the past several months, we’ve been running a preview version of Hive with LLAP, and we’ve seen dramatic improvement to query performance. Traditionally, Hive has performed well on more complex SQL but has been challenged with quicker response time queries. LLAP has shown significant improvement in this area. Benchmark queries have run in half or in a quarter of the amount of time as compared to production Hive. Our team looks forward to making that speed and efficiency available throughout our environment as we tackle other use cases such as precision medicine and genomic marker analysis.” – Mark Mossel, Director of Enterprise Data Management, Geisinger Health System
  • “At The Hanover, our strategy is focused around modern, business-led analytics and driven by and for the business. Hortonworks Data Platform is an important part of that strategy, and we’re looking forward to the new HDP 2.6 functionality. We’re particularly excited to see the enhancements in Spark 2.1, which is included in HDP 2.6.” – Srinivasan Sankar, Data Office Lead, The Hanover Insurance Group
  • “Enterprise customers are looking to run high-performance analytics and cognitive applications on a data platform that provides exceptional memory and I/O capabilities for fast data access and data movement. By providing HDP 2.6 on IBM Power Systems, we offer customers more choice for building a data platform that will deliver superior throughput and acceleration capabilities for demanding use cases.” – Terri Virnig, Vice President, Power Systems Ecosystem and Strategy, IBM
Further Reading

HDP 2.6 for the data center https://hortonworks.com/products/data-center/hdp/

HDP 2.6 for the cloud https://hortonworks.com/products/cloud/

Hive 2.0 with LLAP performance benchmarks https://hortonworks.com/blog/apache-hive-vs-apache-impala-query-performance-comparison/
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