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Vertica Blog

8.1

What’s New in Vertica 8.1: Security Updates

Vertica 8.1 includes the following enhancements to Vertica security. Function to Verify Kerberos Configuration The function KERBEROS_CONFIG_CHECK allows you to test your Kerberos configuration of the Vertica cluster. Running this function checks: • Whether or not Kerberos services are available. • If a keytab file exists • If the Kerberos configuration parameters are set in […]

What’s New in Vertica 8.1: Machine Learning

This blog post was authored by Soniya Shah. Overall, you will notice that Machine Learning for Predictive Analytics, introduced in Vertica 7.2.2, is more accessible to use in Vertica 8.1, with the addition of several important functions. There are improvements to model management with access control ability to save and re-apply normalization parameters, missing value […]

What’s New in Vertica 8.1: the Connector for Apache Spark

The Vertica 8.1 release includes updates for the Vertica Connector for Apache Spark. The biggest new feature is Apache Spark 2.0 compatibility. This makes the connector compatible with the version of Spark included with most recent Hadoop distributions. In addition, the connector supports multiple versions of the Scala programming language. Most Hadoop distributions that support […]

What’s New in Vertica 8.1: Supported Platforms

With Vertica Release 8.1, we continue to enhance and broaden our platform support. New Operating Systems for Vertica Server Vertica continues to perform extensive testing as we qualify major Linux distributions for use with the Vertica Analytic Database. Our testing ensures both stability and performance when you use Vertica with a supported operating system. For […]

What’s New in Vertica 8.1?

Watch this video to learn what’s new in Vertica version 8.1. New features include: – flattened tables – supported platforms update – Management Console features – Kafka connectivity update – machine learning functions – rack locality – Geohash conversions – security upgrades – wide column data query improvement