Vertica Analytics Platform Version 10.0.x Documentation
This section introduces the basics of the Vertica architecture. Understanding how Vertica works helps you effectively design, build, operate, and maintain a Vertica database. This section assumes that you are familiar with the basic concepts and terminology of relational database management systems and SQL.
An Overview of Vertica Features
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 and querying it. Vertica can load data up to 10X faster than traditional row-store databases.
Advanced Database Analytics - a set of advanced in-database analytics including machine learning, geospatial, and time series analytics allows you to conduct the analytics computations closer to your data. These built-in features provide immediate results without having to resort to additional analytic tools.
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 using built-in connectors. For other systems, Vertica supplies a suite of 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.
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.
For more uses cases, see the Customers page on the Vertica web site.
In This Section
Was this topic helpful?