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  • Vertica Announces Vertica 12 for Future-Proof Analytics

    Published on June 14, 2022

    Latest version of analytics database enables more deployment flexibility, advanced analytics, and enhanced machine learning

    Vertica, a Micro Focus (LSE: MCRO; NYSE: MFGP) line of business, today announced the release of version 12 of the Vertica analytical database. Vertica 12 includes new major features and enhancements for analytics and machine learning across multi-cloud, hybrid on-premises and cloud, and multi-regional deployments.

    The announcement was made during Vertica Unify 2022, the organization’s annual user conference, where attendees learned that Vertica 12 users can now choose from the broadest range of deployment options on the market, with improved automation capabilities as well, to future-proof analytics against constantly changing technology requirements.

    “While many companies are being forced to choose their analytics deployment strategy, to commit to one thing –public cloud, on-premises, or hybrid –no one knows exactly what the future may hold,” said Scott Richards, Senior Vice President and General Manager, Vertica at Micro Focus. “With Vertica 12, we have developed a completely flexible platform that is seamlessly hybrid. It is as capable of deploying in a SaaS model as it is on-premises. The continuous advancement of our analytical capabilities means that no matter what your future data strategies may hold, Vertica brings powerful analytics to your data.”

    In addition to supporting more on-premises object stores, Vertica 12 expands its Kubernetes support beyond AWS S3 to Google Cloud Storage (GCS), Azure Blob Storage and Hadoop Distributed Filesystem Storage (HDFS), making it fully cloud-native in any environment. Vertica’s cloud-optimized architecture also has been enhanced with intelligent subclustering to better manage variable workloads and data sharing, helping to assign costs to owners in a logical way.

    On the integration front, Vertica 12 increases the interaction with the data analytics ecosystem. Customers will benefit because key proprietary and open-source technologies work seamlessly, including a new version of VerticaPy, the Vertica Python and Jupyter Notebook interface, as well as an enhanced Spark connector and broadened PMML support.

    Highlights and enhancements to Vertica 12 also include:

    Broad Deployment Framework Support and Higher Performance

    • Expanded list of compatible on-prem object stores with VAST Data and H3C: Allows the use of a broad selection of object stores as the main data repository for private cloud implementations with the addition of VAST Data and H3C to the list of compatible on-prem object stores, along with Dell, MinIO, NetApp, Pure and Scality
    • Cloud-native, elastic and containerized architecture anywhere: Cloud-optimized architecture improvements for subcluster use for ELT and data ingestion
    • Enhanced data sharing on AWS S3 and settings with “Requester Pays” for usage
    • Fast Automated Table Segmentation for large, unsegmented tables
    • Faster analysis of Parquet files, and complete support for complex data types
    • Full ISO-2001 Certification of Vertica Accelerator, Vertica’s unified analytics platform delivered as SaaS
    • FIPS support and more authentication: Broadens support for single sign-on (SSO) capabilities with OAuth2 token authentication support for JDBC and ODBC clients

    Advanced Analytics and Machine Learning

    • New VerticaPy features for Python Pickle and an enhanced Graphviz package
    • In-Database Analytics Advancements, such as library expansion and even more geospatial capabilities
    • End-to-End Machine Learning with the ability to manage and operationalize in Vertica tree models that were created elsewhere
    • Stored Procedures Advancements with new extensions for geometry, geography and altering existing procedures.
    • Analyze data in place quickly by enabling users within multiple formats in object storage or on HDFS
    • Spark Connector supports exchange of data through Parquet or other data on HDFS and S3
    • Analytics application developer productivity with support for NODE.JS.

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