Stream data processing has become a critical component of business operations. The exponential growth of available data and the pressure to act on information in real time have made traditional computing approaches obsolete. It is no longer sufficient to gather data and post process it to determine what actions to take. Now businesses need to operate on the data in flight to filter, format, validate, measure, and respond to events in a timely manner.
And for simple operations this works. Many operations, like filtering data based on fixed rules or converting from one format to another, can be performed at speed. However, the Achilles heel of stream data processing is the fact that many operations still require access to up-to-date and entrusted information such as customer accounts, inventory levels, and resource availability. These stateful operations, if performed against a traditional SQL database, incur the same latency to which previous centralized operations were susceptible. Which is where Active(SP) comes in.
By integrating stream data processing with Volt Active Data — an ACID database designed to maximize throughput without sacrificing consistency, durability, or availability — Active(SP) makes it possible to combine both stateless and stateful processing in flight and at speed.
The Active(SP) architecture consists of three primary parts: sources, sinks, and processors. And the Domain Specific Language you use to define Active(SP) pipelines mirror the exact same structure, letting you define the source, one or more processors, and a sink:
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sinkWhere the processors can be any combination of stateless or stateful operations, with Volt Active Data providing real time access to reference data that can be used to verify, authenticate, authorize, or in other ways validate and enhance the data as it passes.
The advantages the Active(SP) architecture offers are:
Cloud Native — Active(SP) pipelines are designed from the ground up to run in the cloud. It is also self contained and does not require any additional infrastructure (such as resource managers, schedulers, or the like). This allows for easy setup, scaling, and management.
Apache Kafka and Volt Active Data integration — Kafka is supported out of the box as a data source and both Kafka and Volt Active Data are supported as sinks for the pipeline, so that setting up the initial pipeline template is trivial.
Complex business logic — Partitioned procedures in Volt Active Data can be used to incorporate complex, stateful operations on the data without sacrificing latency.
Flexibility — The pipelines are designed as templates, using placeholders for key resources such as server addresses and topic names, so that different pipelines can be created from the same template by identifying different resources in the properties at runtime.
Scalability — The pipelines themselves can be scaled at runtime completely separately from the resources, such as Kafka servers or Volt Active Data cluster nodes allowing you to optimize computing resources to match actual needs.