Database Reference
In-Depth Information
also provides an easy-to-use, drag-and-drop tooling environment to help
you design and build your streaming applications (covered in the “Program-
ming Streams Made Easy” section in this chapter). Another nice feature is
that Streams shares the same Text Analytics Toolkit with BigInsights, enabling
you to reuse skills and code snippets across your entire Big Data platform
(we talk about that in Chapter 8).
You can build your applications for a single server or a cluster, and Streams
automatically fuses sets of operators into highly optimized processing elements
(PEs) that transmit streaming data point-to-point in the cluster for optimal
throughput. When you're ready to deploy your streaming application,
Streams autonomically decides, at run time, where to run the PEs based on
cluster-based load balancing and availability metrics, enabling it to reconfig-
ure operators to run on other servers to ensure the continuity of the stream in
the event of server or software failures. If you want more placement control,
you can also have fine-grained control over the placement of one or more
operators by programmatically specifying which operators run on which
servers and which operators should run together or separately.
This autonomic streaming and customizable platform enables you to increase
the number of servers performing analysis on the stream simply by adding
additional servers and assigning operators to run on those servers. The Streams
infrastructure ensures that the data flows successfully from one operator to
another, whether the operators are running on distinct servers or on the same
server. Not only can you add or remove servers, but you can also dynami-
cally add applications that will automatically connect to running
applications and that can be reconfigured programmatically. Of course, you
can also remove applications on the fly, which enables you to change priori-
ties and improve analysis over time. These features provide a high degree of
agility and flexibility to let you start small and grow the platform as needed.
Much like BigInsights, Streams is ideally suited for both structured data and
for the nontraditional semistructured or unstructured data coming from sensors,
voice, text, video, and financial sources, and many other high-volume sources.
Because Streams and BigInsights are part of the IBM Big Data platform, you'll
find enormous efficiencies with being able to apply the same Big Data analytics
you build to both in-motion or at-rest data. For example, the extractors that are
built from the Text Analytics Toolkit can be deployed in both Streams and
BigInsights.
