A data streaming platform (DSP) is a system that continuously captures, processes, analyzes and stores data in real time or near-real time. These platforms enable organizations to work with continuously generated data streams to support real-time analytics, power artificial intelligence (AI) applications and enhance operational efficiency.
Modern data streaming platforms are designed to support the growing need among enterprises to unlock value from data as quickly as possible—often within milliseconds—while ensuring its accuracy and quality.
Also known as streaming data platforms, they allow organizations to manage continuous data flows, ensuring that critical downstream systems and applications consume up-to-date, consistent information. This agile data processing approach stands in stark contrast to slower batch processing methods historically deployed to manage and analyze large data volumes.
Organizations often adopt open source or managed commercial data streaming platforms instead of building custom infrastructure from scratch. Managed offerings typically provide additional capabilities such as governance tools and prebuilt connectors, accelerating enterprises’ ability to stand up data streaming applications.
To understand the importance of data streaming platforms in data management today, it helps to consider the evolution of both data processing and business needs.
For years, enterprise business intelligence was fueled by data stored and analyzed in data warehouses and then delivered to internal dashboards or used for reports. This warehouse-centric approach relied on batch processing and extract, transform and load (ETL) data integration.
Batch processing has substantial benefits. Enterprises can schedule and run batch jobs during less busy periods, such as overnight, to optimize resource use for greater efficiency. It’s a go-to approach for processing large datasets requiring complex transformations—as long as timeliness wasn’t an issue.
The grouping and periodic execution of tasks through batch processing means that results aren’t ready until well after data ingestion. However, as the pace of business accelerated, so has the need for faster data processing.
Technologies ranging from Internet of Things (IoT) devices to cybersecurity intelligence platforms deliver continuous flows of data. Much of this data is big data—large-scale, complex datasets that traditional data architecture cannot handle.
Staying competitive means executing real-time analytics and acting swiftly on such data. Increasingly, it means adopting the event-driven architectures (EDAs)—software models built around the publication, capture, processing and storage of events that demand a timely response.
The right, timely actions can make a big difference on business bottom lines, whether they entail elevating customer experiences or streamlining operations.
Data streaming and data streaming platforms have become integral to modern AI initiatives.
Predictive AI models are trained on up-to-date data to produce more accurate forecasts. Retrieval augmented generation (RAG) applications connect to streaming data to power more relevant, higher-quality outputs by large language models (LLMs). And real-time data pipelines provide the timely context AI agents need to make autonomous decisions.
The fusion of data streaming and AI, in particular, removes latency for even faster and more timely outputs and decisions. In streaming AI and machine learning systems, data processing and feature computation occur in a streaming pipeline before data is fed to the model. Such systems can also perform online inferencing, in which data is processed immediately for real-time decision-making.
As an enterprise seeks to build and scale AI applications, a data streaming platform can serve as a data orchestration layer—ensuring up-to-date, accurate data is routed throughout an organization’s tech stack for use cases such as:
Data streaming platforms are designed to handle feeds from streaming and real-time data sources to support real-time data pipelines and streaming pipelines.
They’re characterized by four key qualities:
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While the capabilities offered by different data streaming platform providers can vary, most platforms include four core architectural layers or components:
Data streaming platforms are designed to continuously ingest data points generated by various sources, including databases, applications, social media feeds, logs and IoT devices. This incoming data is often unbounded, meaning it is generated and continues flowing without a fixed endpoint.
In a DSP, data ingestion tools and streaming connectors capture events from these sources and deliver them to a streaming system for processing. Common ingestion methods include application programming interfaces (APIs), message queues and change data capture (CDC) connectors that link to SQL and nonrelational databases.
In Apache Kafka—a leading open source streaming platform—applications publish to event streams through APIs, while event brokers host the streams.
Stream processing (sometimes referred to as real-time data processing) is a core capability of data streaming platforms: It’s how data in different formats is filtered, enriched, transformed, aggregated or analyzed as it arrives.
Prominent open source stream processing frameworks include Apache Flink and Apache Spark Structured Streaming.
Stream processing frameworks can support capabilities such as:
In addition, many platforms support AI and machine learning workloads that can be deployed to power data analysis and discern key insights.
Some DSPs also offer support for batch workloads. This allows organizations to unite historical data (which typically undergoes batch processing) with streaming data, providing more context for analytics and AI use cases.
In the destination or serving layer (sometimes also called the storage and output layer), the processed streaming data is delivered to a destination for either immediate use—in an app or dashboard, for instance—or to a storage repository.
Organizations often use data lakes and data lakehouses to store streaming data because they can accommodate high volumes of data at relatively low costs. Streaming data can also be stored in data warehouses, which use ETL processes for data transformation, organization and visualization.
In addition to the ingestion, processing and destination layers, many modern data streaming platforms also feature governance components and management capabilities that operate across all other layers.
These governance solutions can include:
Using data streaming platforms can help enterprises achieve better business outcomes in myriad ways.
DSPs empower organizations to act quickly on customer interactions and market signals, using that information for revenue-generating steps such as optimizing pricing or making time-sensitive investment decisions.
Faster data access through DSPs speeds innovation, productivity and the deployment of new use cases and services, helping enterprises succeed in competitive landscapes.
A variety of organizations, ranging from retail businesses to healthcare providers, can use the real-time data streaming through DSPs to offer services and experiences tailored to a customer’s preferences or a patient’s needs.
The consistent propagation of accurate, up-to-date data through an enterprise ecosystem reduces version conflicts and ensures that collaborators from different teams and departments are working off the same set of information.
Data streaming platforms can offer replayability—every record can be re-read, making it easier to comply with regulatory requirements on auditing. Real-time processing and data validation through DSPs also helps ensure the data shared with regulatory agencies is current and accurate, reducing the risk of government penalties.
While DSPs can help prevent expensive non-compliance mistakes, they also support risk mitigation in other ways. For example, data streaming platforms support constant monitoring of assets and processes, allowing businesses to detect and address small problems (a supply chain bottleneck, for instance) before they turn into bigger ones (a major shipment delay).
DSPs enable AI systems and applications to access the right data at the right time to achieve more accurate outputs and better performance. (AI agents, in particular, power context-driven workflows which require timely data.) DSPs also support the creation of data products—reusable packages of data, metadata, semantics and templates—for AI use cases.
By moving critical data quickly in automated fashion, data streaming platforms reduce the need for manual data processing, saving time and bandwidth for workers in a variety of industries. In addition, some DSPs can enable more agile pipeline development and automatic scaling, reducing workloads for data engineers.
Enterprises increasingly rely on open source or commercial technologies for data streaming platforms.
Key open source technologies for data streaming include Apache Kafka, Apache Flink and Apache Spark.
While open source platforms and technologies are popular, many enterprises prefer to invest in commercial solutions to access additional capabilities and support.
IBM’s Confluent is a cloud-native enterprise-grade solution that is built on Kafka and Flink but also offers governance tools such as a schema registry and stream lineage; more than 120 pre-built connectors for streaming data from and to a variety of systems; and options for fully managed and self-managed deployment.
Major cloud providers offer their own managed streaming data solutions including:
These services, as well as Confluent, can also integrate with on-premises streaming tools to create hybrid architectures to help meet data privacy requirements.
Data streaming platforms can support a variety of use cases that improve business outcomes.
Data streaming systems can form the cornerstone of successful AI development and deployment. For instance, streaming data pipelines can support online feature stores that are reliably up to date and consistent for better AI model training and inferencing, while stateful processing engines can help AI agents maintain context for data and take autonomous actions.
DSPs manage the enormous streams of data generated by Internet of Things devices and sensors in both consumer and industrial settings. Through DSPs, IoT data is continuously aggregated and processed, providing real-time insights and enabling actions like adjusting settings on smart home appliances and ordering repairs on factory floor machinery.
As enterprises endeavor to replace legacy systems with modern technologies, data streaming platforms enable the synchronization of data between the old and new systems. As such, organizations can ensure they continue working with accurate, consistent data during the transition.
A microservices architecture is a cloud-native architectural approach in which a single application is composed of many loosely coupled and independently deployable smaller components or services. DSPs can enable data sharing among the diverse technologies that often make up different microservices.
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