A New Data Storage Architecture to Support Data Warehousing

Tuesday Mar 30th 2010 by DatabaseJournal.com Staff
Share:

We have grown accustom to storing more and more data in our Data Warehouse. We are also looking at faster and faster hardware as a way to speed up these mammoth size Data Warehouses. New hardware is not the only solution for speeding up your warehouse. Now you can look at storing your data in a new database architecture that has been designed from the ground up to support Data Warehouses and Business Intelligence applications.

Data Warehouses store tons of data, and are highly optimize to support Business Intelligence applications.  Traditionally Data Warehouses are built using a relational database, with row-level data stores.  Now you can put your Data Warehouse in a database that has been designed from the bottom up to support Business Intelligence applications.

Vertica has introduced its Vertica Analytic Database to support Data Warehousing.  This new database architecture supports terabytes of data and returns queries 50 to 200 times faster than traditional databases.  It does this by storing data by columns instead of rows.  When you need to read only a couple of different columns, the Vertica Analytic Database does not have to wade through all the row level data, just to find the few columns you want to read.  Instead, it is able to go directly to those column level stores and read only the columns you need.  This drastically reduces the I/O required to resolve queries.  Vertica also does aggressive data compression.  By compressing data up to 90% large amounts of disk space savings can be achieved. 

Technology is forging ahead making faster disk drives and CPUs to support our need for storing and retrieving massive amounts of data.  Vertica has taken the leap forward by designing a new data storage architecture to support the future of Data Warehousing and Business Intelligence. Read this white paper to learn more about the Vertica Analytic Database.

Share:
Home
Mobile Site | Full Site
Copyright 2017 © QuinStreet Inc. All Rights Reserved