Database Reference
In-Depth Information
applications requiring very high performance, it is appropriate to deploy
two or more parallel instances of the streaming application on different
hardware—that way, if a PE in one instance of a graph fails, the other
parallel instance is already actively processing all of the data and can
continue while the failed graph is being restored. There are other strategies
that can be used, depending on the needs of the application. Streams is often
deployed in production environments in which the processing of every
single bit of data with high performance is essential, and these high-
availability strategies and mechanisms have been critical to successful
Streams deployments. The fact that Streams is hardened for enterprise
deployments helped one customer recover from a serious business emergency
that was caused by an electrical outage. (If only everything were as
reliable as Streams!)
Integration Is the Apex
of Enterprise Class Analysis
Another aspect of an enterprise class solution is how well it integrates into
your existing enterprise architecture. As we've discussed previously, Big
Data is not a replacement for your traditional systems; it's there to augment
them. Coordinating your traditional and new age Big Data processes takes a
vendor who understands both sides of the equation. Streams already has
extensive high-speed connection capabilities into enterprise assets, such as
relational databases, in-memory databases, application server queues (such
as IBM WebSphere), and more. Although we delve into the details and nu-
ances of integration in a Big Data world in Chapter 11, let's briefly touch on
some Streams-specific integration points in this section.
Streams has sink adapters that enable the high-speed delivery of streaming
data into BigInsights (through the BigInsights Toolkit for Streams) or directly
into your data warehouse for data-at-rest analysis. A new enhancement is the
ability to include Streams applications in ETL flows with IBM InfoSphere
DataStage (DataStage), and for Streams applications to move data to and
from DataStage. This integration enables DataStage to embed analytics in
flows, and Streams to access the specialized data sources and sinks that are
available through DataStage. Finally, Streams has the ability to incorporate
virtually any analytic flow from SPSS Modeler Solution Publisher into a
