Cloudscale

For twenty years, analytics has been viewed as just one specific area within the broader relational database industry. So, analytics has meant databases. Today that view is changing. Over the past year or so, a new movement, the "NoSQL" movement has emerged promoting the advantages of doing a variety of kinds of analytics without using any relational database technologies at all.

Whatever one thinks of the capabilities and limitations of distributed key-value stores relative to relational databases, one thing is clear - the stranglehold that SQL has held over all aspects of data analytics since 1990 is now coming to an end. Other non-SQL approaches to analytics such as MapReduce/Hadoop, a very simple dataflow architecture for batch computing, are now gaining ground. As the need for realtime analytics grows we will continue to see a migration away from databases and towards more scalable parallel dataflow architectures for analytics.

The main differences between databases and dataflow can be summarized as follows:

The shift from databases to dataflow for enterprise cloud analytics mirrors what we have recently seen in another area, the "realtime web". The old demand-driven web model of polling/querying/pulling RSS feeds has proved unable to deliver the kinds of low latency required for the numerous new realtime web services being created by Twitter and others. New data-driven, realtime, push models such as PubSubHubbub and RSSCloud are now replacing the old approaches.

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