HP has unveiled a number of new products, services, and programs designed to help organizations leverage data and analytics to build new products and experiences.
These include a new release of HP Vertica, which will feature data streaming and advanced log file text search to power high-speed analytics on Internet of Things (IoT) data. Also announced is new support for and contributions to open source technologies, including optimized Hadoop performance, integration with the Apache Kafka distributed messaging system, and advancements in Distributed Rpredictive analytics.
Also announced is the HP Haven Startup Accelerator program, which will provide early-stage companies with special access to HP Big Data and Application Delivery Management software and services.
HP Vertica Updates
HP’s newest version of its HP Vertica SQL database, dubbed “Excavator,” offers the following:
- Data streaming analytics: Excavator will offer native support for the Apache Kafka open source distributed messaging system to enable organizations to ingest and analyze high-speed streaming data, including Internet of Things, in near real-time. This capability delivers insight for a wide range of use cases, including manufacturing process control, supply chain optimization, healthcare monitoring, financial risk management, and fraud detection.
- Advanced Machine Log Text Search: Excavator will enable organizations to collect and index large log file data sets generated by systems and business applications, helping IT organizations to quickly identify and predict application failures and cyber-attacks, and investigate authorized and unauthorized access.
Big Data Open Source
HP also released a series of offerings that enable organizations to combines open source with HP’s Big Data platform. These include:
- HP Vertica for SQL on Hadoop native file support: The new version of Vertica brings an increase in performance on Hadoop formats like ORC and Parquet. Specifically, HP has worked collaboratively with Hortonworks to develop a new high-performance access layer that enables SQL queries to run directly on ORC files, resulting in a 5x increase in execution times.
- HP Vertica Flex Zone Table Library: HP has open sourced its Flex Table “schema on-need” technology to to allow companies to harness semi-structured data.
- Native Integration with Apache Spark: HP announced its future commitment to integrate Vertica with Apache Spark. This will enable accelerated data transfer between Vertica and Spark, allowing organizations to take full advantage of their Spark-based deployments. This future capability will enable the developer community to build their models in Spark and run them in Vertica for analytics.
HP Haven Startup Accelerator
The HP Haven Startup Accelerator is a new program designed to support and expand HP’s ecosystem of developers and innovators by making HP Big Data and Application Delivery Management software products accessible to early-stage companies. Qualified participants will benefit from the following program components:
- Free usage of the community versions of HP IDOL and HP Vertica with expanded capacity.
- Premium version of HP IDOL and HP Vertica with special pricing
- HP Application Delivery Management tools including HP Agile Manager, HP LeanFT, and HP LoadRunner.
HP Haven Apps Framework Accelerates Solution Development
HP also announced a new framework of technology and best practices to accelerate the development of next-generation analytical applications. This framework extends the HP Haven Big Data Platform with quick-start visualization, syndicated data feeds, open on-premise and cloud-based APIs. This enables HP Professional Services and HP partners to quickly offer a range of solutions, such as voice of customer, smart cities, and fraud detection.
HP’s Hardware Platforms Optimized for Big Data
HP’s hardware platforms for big data include traditional HP ProLiant DL380 clusters, Apollo 4510, 4530 and 4200 compute and storage servers, and HP’s asymmetric Big Data Reference Architecture. These systems enable customers to optimize their big data workloads.