Vertica Announces Vertica 11, Delivering on Vision of Unified Analytics
"Unified Analytics is a critical movement in our industry. But truly unified analytics requires proven and mature security, true deployment choice, end-to-end machine learning in production, and no-compromise analytical performance for organizations to capitalize on this mega trend," said
Vertica 11 includes more in-database machine learning capabilities with the latest release of VerticaPy, an open source Python library for Vertica that supports Python projects on data stored in Vertica. VerticaPy now includes expanded machine learning functionality, connection, and data exploration capabilities as well as graphical capabilities. In addition, this release offers a new, open sourced Apache Spark Connector that supports Spark 3.0 and Scala 2.12 with S3, SSO, and parallel read/write support, significantly improving performance. Additional key capabilities include an XG Boost algorithm, increased PMML integrations, and customized time series algorithms.
"Running in-database analytics and machine learning will be a real game changer for
With Vertica 11, Vertica in Eon Mode – Vertica's cloud-optimized architecture that separates compute from storage for rapid elasticity and better cost control -- now is in full production on Microsoft Azure cloud, in addition to AWS and Google Cloud. Vertica also welcomes Dell EMC ECS as a certified object storage partner for Eon Mode deployments for on-premises private data centers. Vertica now supports the Eon Mode database in a Kubernetes StatefulSet, further delivering on the commitment to deployment flexibility across multi-clouds and on-premises data centers.
Highlights and enhancements to Vertica 11 also include:
Broadest Deployment Support
- Eon Mode support for Microsoft Azure – Vertica in Eon Mode now officially supports Microsoft Azure Block Blob Storage, giving organizations complete freedom to choose proven, cloud-optimized architecture on all three major clouds.
- Docker Container and Kubernetes support – The Vertica-tested Docker image is now available on Dockerhub. With this support, organizations now have even more options for cloud-native deployments, with container and Kubernetes orchestration. Support for Vertica Kubernetes Operator, StatefulSets and Helm Charts is included.
- FIPS 140.2 compliance – Vertica complies with FIPS 140-2, which is used by Federal Agencies when organizations specify cryptographic-based security systems for protection of sensitive or valuable data.
- Improved Voltage integration – Improvements to the tight and seamless integration with Voltage SecureData ensure even more secure Format Preserving Encryption (FPE) of data, including data masking and format preserving hash.
- Simplified security configurations – Vertica has considerably simplified security processes to make it easier and more streamlined to restrict and grant user and group privileges
End-to-End Machine Learning
- Expanded Time Series support – Time series forecasting is expanded greatly to include support for autoregression, moving average, stationarity tests, and automatic generation of correlation plots in-database using SQL and VerticaPy.
- Spark 2.0 Connector – Vertica has contributed an improved connection with Spark that now supports Spark 3.0 and 3.1 with more efficient bi-directional data flows, Projections and filters, SQL push-down, and enterprise SSO support, including Kerberos.
- VerticaPy - VerticaPy is a new open source Python library that exposes Pandas and Scikit-like functionality to conduct data science projects on data stored in Vertica, combining the scalability of Vertica with the flexibility of Python.
- AutoML - VerticaPy Delphi, our most advanced auto-ML capability yet, can auto-prepare data, train and evaluate several algorithms, and display a graph by accuracy and efficiency within minutes, using full Vertica data sets to vastly shorten development time for machine learning and AI projects.
- XGBoost - XGBoost is the latest algorithm added to the long list of in-database algorithms supported by Vertica. The Auto-Retrain meta function ensures that models consistently produce accurate predictions.
Deep Learningand PMML support – Vertica now supports import of the latest version of TensorFlow, version 2.5, deep learning and custom models. Generalized linear model import from PMML is also supported.
Increased Analytical Performance and Much More
- Stored Procedures – Vertica 11 introduces stored procedures for the purpose of automating the information lifecycle from ELT, through data preparation, to the ML pipeline. Vertica has a rich set of functions at each step in the lifecycle and usage pipeline. Stored procedures will enable users to automate their execution and facilitate metadata collection for auditing and forensics.
- Expanded Complex Data Type support – Analysis of complex data types such as Maps, Arrays, and Structs is now expanded with the improved ability to export data in these forms directly to ORC as well as Parquet, and a complete capability to query files with complex types in place, without modification or import.
- Management Console (MC) enhancements – The MC has several improvements, including quick launch templates to get you working faster, simplified workflows to help you work more efficiently, and customizable alerts and monitoring metrics to keep your Vertica cluster running smoothly.
- Query Optimization improvements – The speed boost in any query containing a WITH clause will show the clear difference in the ongoing query optimization engine improvements.
For more information about Vertica 11, please visit www.vertica.com.
The core analytical platform within the
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