So there you have the two approaches to handling machine-generated-data. If you have vast archives, EMC, IBM Netezza, and Teradata all have purpose-build appliances that scale into the petabytes. You also could use Hadoop, which promises much lower cost, but you’ll have to develop separate processes and applications for that environment. You’ll also have to establish or outsource expertise on Hadoop deployment, management, and data processing.
For fast-query needs, EMC, IBM Netezza, and Teradata all have fast, standard appliances and faster, high-performance appliances (and companies including Kognitio and Oracle have similar configuration choices). Column-oriented database and appliance vendors including HP Vertica, InfoBright, ParAccel, and Sybase have speed advantages inherent in their database architectures.
I’m wondering why Hadoop is mentioned just in passing considering how many large datasets it is already handling.
Original title and link: 2 Ways to Tackle Really Big Data (NoSQL database©myNoSQL)