The rise in Hadoop interest and usage in the past couple years can be directly attributed to the large number of projects that have added an SQL access layer over Hadoop data stores. This growth and investment shows that there’s a real need for products running SQL queries against data that lives inside Hadoop to give fast, easy access to and analysis of that data, rather than relying on Hadoop’s native reporting, or moving Hadoop data into a conventional database. Now that Hadoop data can be accessed quickly and easily, the question is becoming where and when to use Hadoop or a data warehouse?
This paper will first explain the logical data warehouse and how the components, including Hadoop and the data warehouse, will work in concert. It will then examine which workloads will work best on which components and follow up with a total cost of ownership (TCO) analysis of the components.
Included in this white paper:
This asset is sponsored by IBM.