Streaming Analytics with IBM Streams: Analyze More, Act Faster, and Get Continuous Insights

4Streams

Up to this point, we’ve talked about the differences between data-at-rest and data-in-motion, provided examples of where data-in-motion can be used, and described why we should use a platform instead of a framework, let alone writing something from scratch. This chapter describes Streams. The goal is not to describe how to use Streams. For that, you can look at the many resources mentioned in Chapter 8 and at the end of the book. Instead, we want to find out the capabilities of Streams so we can better understand how to apply this technology to our business problems and get to a solution faster. Understanding what a product can do is essential in any discussion of its use in a solution. It is not an issue of how to do something but what can be done. Any non-programmer interested in Streams will benefit from this chapter.

After a quick background on the origin of the product, this chapter includes six main sections:

• Runtime: The runtime is the execution part of the product. Streams was created with performance and scalability in mind. We discuss these issues in the runtime section.

• Deployment capabilities: In today’s world, we need the flexibility to deploy on premises or in different ways on the cloud.

• Language: Streams includes its own language called SPL (Streams Programming Language). This is needed to provided that higher-level of abstraction mentioned in Chapter 3. This section also covers the capabilities available in the creation of new operators.

• Toolkits: ...