Vertica Analytics Platform Version 9.2.x Documentation

Vertica Concepts

This guide introduces the basic concepts to help you to effectively design, build, operate, and maintain a Vertica database. This document assumes that you are familiar with the basic concepts and terminology of relational database management systems and SQL.

The Vertica Approach

The Vertica Analytic Database consists of the following key features:

Columnar Storage and Execution - column stores offer significant gains in performance, I/O, storage footprint, and efficiency when it comes to analytic workloads. With columnar storage the query only reads the columns needed to answer the query.

Real-time loading and querying - with high query concurrency and the ability to simultaneously load new data into the system, Vertica can load data up to 10X faster than traditional row-store databases.

Advanced Database Analytics - a set of Advanced In-Database Analytics allows you to conduct the analytics computations closer to the data. This provides immediate results from a single place without having to extract data from a separate environment.

Database Designer and Administration Tools - these features allow you to tune and control Vertica with minimal administration effort. For more information see About Database Designer and Using the Administration Tools.

Advanced Compression - aggressive encoding and compression allows Vertica to dramatically improve analytic performance by reducing CPU, memory, and disk I/O at processing time. Vertica can reduce the original data size by up to 90%, to as little as 1/10th of its original size, without loss of information or precision.

Structured and Semi-Structured Data - in addition to traditional structured database tables, Vertica provides flex tables that let you load and analyze semi-structured data such as data in JSON format.

Massively Parallel Processing - a robust and scalable parallel processing solution provides active redundancy, automatic replication, failover, and recovery.

Deploy Anywhere - run on physical hardware located in your own (or a co-located) data center. Or run on virtual hardware on your own virtual hosts or in the major cloud platforms (AWS, Azure, and Google Cloud).

Data Lake Connections - analyze data from Apache Hadoop and Kafka plus many other systems using standard client libraries such as JDBC and ODBC.

Management and Monitoring - the browser-based Management Console lets you create, import, and manage your Vertica databases through a user-friendly GUI.

Dynamically Scale Your Cluster to Meet Your Workload - use Eon Mode to scale your database cluster up to meet increased workloads, or scale it down to save money.

Use Case

A mobile gaming company used to rely on a patchwork of technologies for data warehousing and business intelligence reporting. It took two to four hours to run a query on each game server. The search for a solution led the company to evaluate different companies for expanding its analytic capabilities. The Vertica implementation accomplished the following for the company:

  • Queries were reduced from two to four hours to minutes or seconds
  • Solution successfully met cloud deployment requirement
  • Expanded data capacity from a few months to a whole lifetime of data

This has led to better customer support by shortening response time to customer issues, as well as providing the ability to answer many more questions than before the Vertica implementation.

Getting Started

See the Getting Started for information on starting your Vertica Analytic Database implementation.

In This Section