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In-database machine learning coming in Vertica 9

In-database Machine Learning functionality to be available in an analytics platform update for Vertica.

Big Data 16,979 VIEWS
9/21/2017 2:56:48 PM
In-database machine learning coming in Vertica 9
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Posted Thursday, September 21, 2017 by Richard Harris, Executive Editor


In-database machine learning coming in Vertica 9
Micro Focus has announced a major release of its Vertica Analytics Platform. Vertica 9 introduces an extended list of in-database Machine Learning capabilities - including new algorithms, model replication, data preparation functions, and continuous end-to-end workflow - to simplify the production and deployment of machine learning models. In addition, Vertica 9 will be available for deployment in the Google Marketplace and has further integration with Microsoft Azure including Power BI certification. With Vertica 9, organizations can now analyze their data not only in place, but now in the right place - without data movement - while supporting any major cloud deployment for fast and reliable read and write for multiple data formats.

Micro Focus also announced the beta release of Vertica in Eon Mode, which enables organizations to evaluate the separation of compute and storage for Amazon Web Services (AWS) deployments. Companies in the AWS ecosystem will be able to leverage AWS S3 for storage and Vertica’s query-optimized analytics engine for processing speed to capitalize on cloud economics.

Legacy data warehouse solutions have forced many enterprises into rigid and high-cost proprietary hardware and analytics solutions supporting only limited data formats. As data formats and storage locations continuously evolve, organizations require a powerful and unified solution to analyze data in the right place at the right time, with the performance and economics that the business requires.

“Data is a one of the most valuable assets for companies, and a company's ability to monetize their data while optimizing for both cost and performance at scale is already a fundamental differentiator in every industry,” said Colin Mahony, Senior Vice President and General Manager, Vertica, Micro Focus. “Vertica’s ability to analyze an extensive set of data formats in the right place, at the right time, enables our customers to optimize for both cloud economics and user demands. Vertica is the only platform in the industry that can provide high-performance advanced analytics and in-database machine learning with true freedom from underlying infrastructure across the full data pipeline, at the scale demanded by the world’s most data-driven organizations.”

Key Features:


In-Database Machine Learning: Provides a comprehensive set of new Machine Learning algorithms for categorization, overfitting and prediction to enhance processing speed by eliminating the need for down-sampling and data movement.

- Support for new data-preparation functions for deriving greater meaning from the data, while improving the quality of analysis.

- Streamlined end-to-end workflow simplifies production deployment of Machine models - particularly for customers that embed Vertica and require the ability to replicate models across clusters.

Improved Core Data Management and High Analytical Performance:
Reflects continual investments in the core underlying database architecture, including greater management of massive amounts of historical data with hierarchical partition management and more consistently high performance under the most demanding workloads with the most sophisticated analytical queries.

Parquet Writer: Introduces a new HDFS Parquet writer - built on Vertica’s ability to not only read, but now write data and results on HDFS - to derive and contribute immediate insights on growing data lakes in an organizations’ Hadoop data pipeline.

Flattened Tables: Facilitates the task of performing complex JOINs across multiple tables much less cumbersome and much more performant. Analysts can quickly write straight-forward, fast-running queries as if the data resided in one big flat table without the need to alter their existing schemas, simplifying and speeding the process and management of big data analytics in databases with complex schemas. 





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