Empowering Real-Time Action with Stream Processing

The pace of business never slows down—especially in uncertain economic times. Just a few years ago, even those working for the most data-enabled organizations were probably reasonably content with deploying technical solutions for collecting and storing real-time data streams for deeper batch analysis days or weeks after the actual events.

Since establishing those real-time collection and storage systems, these organizations have watched their market conditions transform daily. Their competitors are innovating and responding faster than ever. Suddenly, enterprise architects and other senior technical decision makers are becoming keenly aware that by relying on batch analysis, they’re preventing their talented employees—business users, data analysts, and beyond—from creating new value from real-time data streams because they can’t process events into insights and reactions immediately.

Enter real-time action

Real-time action is the objective of enabling an immediate and active response to specific events, such as customer interactions. The outcome of this real-time action that customers and other stakeholders experience is real-time responsiveness.

The response allows an organization to act on new information, separating its techniques from competitors who only have real-time analytics or data availability. While they might collect and store real-time data, they can only perform analysis long after the data-generating events have passed.

There are many valuable opportunities for analyzing real-time data after the fact, such as optimizing throughput, exploring different methods of reporting metrics, or researching net-new options, but acting on fresh information gives any organization a distinct competitive advantage.

See also: The Antidote for Congested Data and Analytics Pipelines

Using stream processing to enact real-time action

One answer to unlocking the use cases behind near-instantaneous reactions is stream processing, the practice of taking one or more actions on a series of data when said data is created at its source. Either serially or in parallel, a stream processing pipeline takes a variety of actions, like aggregating data from multiple sources, transforming data into different structures, and more. This pipeline improves analytics—how an organization predicts its future based on patterns—but also enables applications to respond to new data when they occur.

Think of it like an intelligent feedback loop. The data pipeline ingests data, enriches it, transfers it to other systems for processing, and then returns an informed response to whoever created the transactions. Stream processing transforms stale interactions between customers and businesses into dynamic ones or creates new and valuable touchpoints where there had once been silence.

For example, credit card providers and card issuing banks already use stream processing for instantaneous fraud detection. The moment a card is run, the provider analyzes usage and buying patterns to determine whether their customer has become a victim of credit card theft—and if so, it denies the transaction and informs the customer of remediation options. The response is valuable because it’s immediate, saving everyone time and money and reinforcing the provider/bank as a trusted partner.

Instead of collecting and storing real-time data for analysts to peruse later, stream processing enables the paradigm shift of real-time action and responsiveness. By automating how organizations process real-time data in just a few milliseconds, stream processing removes human bottlenecks like batch processing to help architects and leaders deliver better customer experiences, move faster against their competition, and free their talented people to dream up new valuable use cases for the data they already collect.

Challenges to overcome

Any organization’s road to real-time isn’t perfectly logical—especially as its people start looking for ways to process streams in real time. The challenges are varied and complex:

• The volume of data collected when switching to real time quickly balloons into concerns over read latency and cost.
• Data diversity complicates how organizations define data models/schemas around the most valuable analytics use cases, making life harder for those trying to query and analyze the data.
• Most real-time data pipelines are designed with batch analytics in mind, and adding real-time responsiveness capabilities is far from plug-and-play.
• Stream processing platforms, which often work with data in a non-persistent way, require additional fault tolerance to preserve data through network interruptions or node failures.
• Because inputs and outputs are continuous, stream processing must utilize windows to understand how value changes over time and gracefully handle event data that arrives “late.”

Unlocking the benefits of real-time action requires a platform based on an ultra-fast data source, which can operate as a hub for integrating multiple streaming sources. With intelligent responses in just a few milliseconds, a stream processing platform becomes the core of all new competitive advantages, use cases, and innovations.

To process and derive insights at the speed of real-time data, enterprise architects need to establish a data pipeline that enables a unified processing and responsiveness experience. Elements of such a pipeline typically include:

Data sources: Where data is generated, such as Apache Kafka, big data repositories, TCP sockets, and database events, among many others.

Streaming platform: The engine that transforms, combines, streams, and even runs machine learning (ML) inference on data using in-memory stores and multiple stream processing applications.

Sinks: Destinations for data that’s been acted upon, such as marketing applications, CRMs, analytical databases, and more.

Bottom line: It is time to explore the world beyond batch processing.