An optimized stream processing system will include four foundational elements:
Event and data capture: Not all data is relevant, so your organization needs to determine which information it needs to track its systems, processes and goals. Making matters more challenging, data emanates from a wide range of sources, so you’ll need to clarify and clean those types of information that provide the optimal form of data for your enterprise purposes.
Key determinations at this stage include:
How to organize and structure the incoming files.
Validation components to ensure the data’s accuracy and relevancy.
Flexible conversion capacities to ensure the maximum possible integration across the enterprise.
Data delivery/plumbing: Some information is relevant in many corporate targets, and getting it delivered across the organization as instantaneously as possible maximizes its impact. Latency can reduce or even kill its value. Stream processing must be able to pluck the information from the moving data stream and relate it to warehoused data for it to deliver optimal relevance in each instance.
In anticipation of a migration to stream processing, your business must first parse out which sources of incoming data are relevant to which corporate sectors. You can then strategize an API-based architecture that will capture relevant data based on sector-specific queries, moving that information as quickly into the offices that need it most.
Data collection (data warehouses and data lakes): Even while delivering instant information, stream processing will also feed the aggregate corporate data store. Event-driven data can trigger instant responses and, in a more global sense, also reveal insights into overarching organizational realities such as industry trends, whole-system capacities and production system actions.
If you’re a newcomer to stream processing, consider adopting parallel processing engines to process and ingest single data streams into multiple repositories, including enterprise data stores. Information triggering an instant response appears immediately on appropriate dashboards while also informing the larger database of current events affecting other corporate concerns.
Data engineering (data analytics and data science): Immediate analysis of arriving information keeps leaders informed about minute-to-minute corporate functioning so they can react quickly to emerging challenges or arising opportunities. Longer-term analysis of steam-processed data provides context to larger data stories, revealing insights into the more granular aspects of company and industry activities.
You can access those deeper insights by organizing the data coming from the disparate sources into APIs for individual corporate departments to use as processing building blocks. Rendering source data to be both pluggable and reusable provides flexibility for its use without diminishing the quality of its information. It also offers visibility into how those sources interact with other system applications and devices.
These four foundations—appropriate capture, strategized delivery, organized collection and intelligent engineering—form the basis of the stream processing configuration, allowing organizations to use incoming data effectively: immediately, for short-term decision making and for long-term corporate strategizing.