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Column-Store Databases and DW Appliances: How to Make the Right Choice

With data volumes exploding, conventional enterprise data warehouses are fast running out of headroom. Data warehouse appliances are starting to fill the gap, but the emerging category of column-oriented databases may offer a better option. The key to success is matching your application to the right product.

Consider Columns Over Rows

What exactly is a column-store database? Unlike a conventional database that stores data in rows, with say, one complete customer record per row, the column-store looks at data vertically. In the case of a customer database, for example, it stores down the columns: all the first names, all the last names, all the cities, all the zips and so on. While the row approach is well suited to OLTP (online transaction processing) with lots of writes for each new customer transaction, the column-store approach is ideal for OLAP (online analytical processing) with lots of reads against particular attributes.

Column-Store Databases
Vendor Product Differentiators
Calpont CNX Data Warehouse Platform Compatible with existing Oracle database environments. Modular architecure for incremental scaling.
Exasol EXASolution A leader in 100 GB and 1000 GB TCP-H benchmark tests
InfoBright Brighthouse Data pack storage approach for data-optimized compression for smaller footprint. Queries focus on relevant packs for faster performance.
ParAccel ParAccel Analytic Database Combines column-store architecture with in-memory anaysis capabilities for fastperformance. Offers "drop-in accelerator" configurations on top of popular relational databases.
Sand Technology Sand/DNA Software Extensions available for Oracle, IBM DB2, SAP NetWeaver BI
Sybase Sybase IQ Category leader and early pioneer with 1,000-plus customers
Vertica Vertica Database Supports continuous data loading. Also offers appliance based on HP hardware/Red Hat Linux OS-based database appliance.

The first advantage of a column-store database is that it can focus only on those columns that figure in the query. If you want to explore, say, sales by zip code and product, for example, your query will only interrogate the zip code and product sku columns rather than wading through the names, addresses and all the other irrelevant information in each row. The second advantage of the column-store approach is that it can apply optimized compression (upwards of ten to one compression, and some claim higher) because the data in each column is consistent (all names, all zip codes, all product sku numbers, etc.). For the right type of analytic query, the amount of data coming back from a column store is always going to be less than a conventional database, so it's going to have less I/O and, therefore, better performance.

"If you're bringing back all the columns, a column-store database isn't going to perform any better than a row-store DBMS, but analytic applications are typically looking at all rows and only a few columns" says Gartner analyst Donald Feinberg. "When you put that type of application on a column-store DBMS, it outperforms anything that doesn't take a column-store approach."

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