From the user perspective, Vertica is a database. It acts exactly like other databases in that it supports standard ANSI SQL and it works with common ETL and visualization tools. Users will only know the difference when their queries run faster.
From an administrative perspective, there are also many similarities to other common databases in the field. However, there are some differences in optimization of queries. Commonly in other solutions, you’re dealing with indexes and materials views for optimization. Vertica uses a feature called projections. To understand projections and other administrative tasks, it’s good to have a solid understanding of columnar databases and how Vertica works. Differences in columnar and tools like our management console and query optimizer may require some study. We have some great documentation online (https://my.vertica.com/docs/8.0.x/HTML/index.htm), free online training (https://my.vertica.com/resources/web-based-training/), feature videos (https://www.youtube.com/channel/UC7Ca4IZjxee3NFC78-PMMEg) and in-person training programs that can simplify learning.
The Vertica Analytics Platform is purpose built from the very first line of code for Big Data use cases and is trusted by thousands of leading data-driven enterprises around the world, including AT&T, Etsy, Twitter, Intuit, Uber and more. Vertica delivers speed, scale and reliability on mission-critical analytics at a lower total cost of ownership than legacy systems. All based on the same powerful, unified architecture, the Vertica Analytics Platform provides you with the broadest range of deployment models, so that you have complete choice as your analytical needs evolve. Deploy Vertica on-premise, in the clouds (AWS, Azure and GCP), on Apache Hadoop, or as a hybrid model.
Vertica features include:
• Column-oriented storage organization, which increases performance of queries.
• Standard SQL interface with advanced analytics capabilities built-in, such as time series, pattern matching, event series joins, machine learning and geospatial.
• Compression, which reduces storage costs and I/O bandwidth. High compression is possible because columns of homogeneous datatype are stored together and because updates to the main store are batched.
• Shared nothing architecture, which reduces system contention for shared resources and allows gradual degradation of performance in the face of hardware failure.
• Easy to use and maintain through automated data replication, server recovery, query optimization, and storage optimization.
• Support for standard programming interfaces ODBC, JDBC, ADO.NET, and OLEDB.
• Integration to Hadoop with the capability to perform analytics on ORC and Parquet files directly.