Micro Focus (LSE: MCRO; NYSE: MFGP) today announced Vertica 9.1, the latest major release of its Vertica Analytics Platform. This release features the general availability of Vertica in Eon Mode, which separates compute resources from data storage to enable organizations to optimize cloud infrastructure costs and simplify operations for their Vertica cloud deployments on Amazon Web Services (AWS). As a result, Vertica customers in the AWS ecosystem can now load and store high volumes of data into AWS S3 to leverage lower storage costs, dynamically spin up new compute resources in minutes and turn off compute resources when no longer needed using Vertica’s query-optimized ANSI SQL advanced analytics engine. This helps organizations with variable workloads to reduce infrastructure costs, while simplifying database administration. In addition, organizations now have the broadest choice of deployment options in the industry for on-premise, natively on Apache Hadoop, hybrid, and cloud workloads—all delivered via the unified Vertica analytics database.
“It is critical for us to provide our clients, the games sector’s leading publishers and developers, with deep and rich player data to maximize engagement and revenues,” said Chris Wright, Founder and CTO at DeltaDNA. “With Vertica 9.1, we can analyze in-game clickstream data to identify trends quickly and provide exceptional query performance to meet our customers’ expectations every day.”
Modern data-driven organizations manage a wide variety of variable workloads that produce different usage demands. When coupled with exploding data volumes across every industry, organizations require a unique approach to cost-effectively store their data, while delivering fast and comprehensive data analytics with optimized compute resources as needed. Moving data in and out of data stores is time consuming, and provisioning for storage and compute resources for peak demand scenarios is costly. Given these volatile and rapidly changing scenarios, organizations need to ensure that they can handle dynamic workloads and evolving data analytics requirements, while safe guarding their data lake investments.
“The cloud has incredible potential for BI and data warehousing through truly fast and flexible data analytical platforms that feature separation of compute and storage architectures,” says Rohit Amarnath, CTO and Founder of Full 360. “Through our benchmark testing, Vertica in Eon Mode has proven to deliver incredibly fast performance and rapid elasticity for dynamic analytical workloads. We anticipate meeting the growing demand for on-premise to cloud migrations as well as new analytical workload projects that will be best served through this new, cloud-optimized architecture.”
Vertica in Eon Mode: Delivering on the Promise of Cloud Economics with the Separation of Compute and Storage.
After a rigorous beta program, Vertica in Eon Mode is now generally available to ensure organizations can handle the most dynamic workloads and evolving data analytics requirements. In fact, one large beta customer achieved 30 percent faster load rates without any tuning, six times faster query performance, and eight times faster node recovery than their previous Vertica AWS deployment—all with lower AWS infrastructure consumption that resulted in dramatic cost savings.
Vertica in Eon Mode includes an intelligent, new caching mechanism on the nodes that enables the separation of compute and storage without any compromise on the speed and breadth of all Vertica analytical functions, including time series, geospatial, pattern matching, and the full suite of in-database machine learning.
“Vertica’s pure-software, hardware-agnostic approach has enabled our customers to deploy in a wide variety of configurations—from embedded solutions to the clouds and beyond,” said Ben Vandiver, Chief Technology Officer and architect of Vertica in Eon Mode, Micro Focus. “Vertica in Eon Mode on AWS represents a transformational step from integrating with cloud services to a core architecture that natively capitalizes on cloud economics—with the full suite of advanced analytics functions and in-database machine learning—while supporting efficient elasticity that aligns consumption with need.”
Additional highlights and enhancements to Vertica 9.1 include:
• Vertica by the Hour on AWS Marketplace—Available on the AWS Marketplace as an hourly paid subscription for organizations that prefer usage-based consumption with financial structures favoring OPEX over CAPEX. Vertica’s hourly pricing on the AWS Marketplace includes software and support, and can be deployed in both Enterprise Mode and the new Eon Mode. This paid subscription offer adds to the choices currently available in the AWS Marketplace, including the Vertica Community Edition and the Bring Your Own License (BYOL) model.
• In-database Machine Learning Advancements—Features new in-database Machine Learning capabilities—including Principal Component Analysis (PCA), a common choice for feature reduction and model efficiency and accuracy. Vertica in Eon Mode enables lower cost and more efficient model training with the ability to easily deploy new models in production.
• Integrated Data Protection—Includes high-performance integration between Vertica and Voltage SecureData to protect Vertica data at rest, in motion, and in use, utilizing Voltage Format-Preserving Encryption (FPE) and Tokenization. Organizations can now protect sensitive data at the source and ensure that the data is always stored, transferred and used in protected form—maintaining usability of big data for analytics and applications across hybrid IT.
• Performance and Management Enhancements—Includes core architectural enhancements that will improve performance of subqueries with data joins. The Vertica Management Console also includes new management advancements that enable administrators to visually monitor how their users are accessing data in HDFS data lakes by specific table, formats, files, and measures overall data consumption capacity. This dashboard enables administrators to target frequent and business-variable queries that could benefit from faster performance by importing HDFS data into Vertica.