October 2015
Intermediate to advanced
50 pages
1h 2m
English
Content preview from Fast Data: Smart and at Scale
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Fast Data Application Value
Looking Beyond Streaming
Fast data application deployments are exploding, driven by the Internet of Things (IoT), a surge in data from machine-to-machine communications (M2M), mobile device proliferation, and the revenue potential of acting on fast streams of data to personalize offers, interact with customers, and automate reactions and responses.
Fast data applications are characterized by the need to ingest vast amounts of streaming data; application and business requirements to perform analytics in real time; and the need to combine the output of real-time analytics results with transactions on live data. Fast data applications are used to solve three broad sets of challenges: streaming analytics, fast data pipeline applications, and request/response applications that focus on interactions.
While there’s recognition that fast data applications produce significant value—fundamentally different value from big data applications—it’s not yet clear which technologies and approaches should be used to best extract value from fast streams of data.
Legacy relational databases are overwhelmed by fast data’s requirements, and existing tooling makes building fast data applications challenging. NoSQL solutions offer speed and scale but lack transactionality and query/analytics capability. Developers sometimes stitch together a collection of open source projects to manage the data stream; however, this approach has a steep learning curve, adds complexity, forces ...
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