Stream Processing

Stream Processing Definition

Stream processing refers to a computer programming architecture in which data is computed directly as it is produced or received.

Diagram depicts the computer programming architecture of stream processing.
Image from Ivan Mushketyk via TowardsDataScience


What is Stream Processing?

Stream processing is a big data technology that focuses on the real-time processing of continuous streams of data in motion. A stream processing framework simplifies parallel hardware and software by restricting the performance of parallel computation. Pipelined kernel functions are applied to each element in a data stream, employing on-chip memory reuse to minimize loss in bandwidth. Stream processing tools and technologies are available in a variety of formats: distributed publish-subscribe messaging systems such as Kafka, distributed real-time computation systems such as Storm, and streaming data flow engines such as Flink.

Distributed stream processing systems involve the use of geographically distributed architectures for processing large data streams in real time to increase efficiency and reliability of the data ingestion, data processing, and the display of data for analysis. Distributed stream processing can also refer to an organization’s ability to centrally process distributed streams of data originating from various geographically dispersed sources, such as IoT connected devices and cellular data towers. Distributing stream processing hardware and software is a reliable method for developing a more scalable disaster recovery strategy.

Microservices, an architectural design for building distributed applications, allows for each service to scale or update without disrupting other services in the application. Microservices stream processing manages these individual data streams through one central area so systems and IT engineers can monitor health and security of these microservices to ensure an uninterrupted end user experience.

How Does Stream Processing Work?

In order to incorporate event stream processing capabilities into an application, programmers either code the process from scratch or use an event stream processor. In certain first generation data stream processing engines, such as Apache Spark and Apache Storm, users are required to write code, which involves the following processes: events are placed in a message broker topic, events from topics in the broker are programmed to become the data stream and be received, and then publish the results back to the broker.

A streaming data processing architecture will automatically collect the data, deliver it to each actor, ensure they run in the correct order, collect the results, scale for higher volumes, and handle failures. The user is able to write the logic for each actor, wire the actors up, and hook up the edges to the data source(s).

You can either send events directly to the stream processing system or send them via a broker. Then the streaming part of the app can be written using “Streaming SQL,” which provides operators such as windows, patterns, and joins directly in the language, enabling users to query data without needing to write code. Finally the stream processor is configured to act on the results by publishing events to a broker topic and listening to the topic or by invoking a service when the stream processor triggers.

Batch vs Stream Processing

Batch processing, a more traditional stream processing architecture, refers to the processing of transactions in a batch or group without end user interaction. All input data is preselected through command-line parameters or scripts. The batch processing technique enables the automation and processing of multiple transactions as a single group, which can be implemented at any time, but is best suited for end-of-cycle processing cases, such as monthly payrolls and daily bank reports.

The extract, transform, load (ETL) step used in data warehousing is typically a batch process. Major benefits of batch processing include: the ability to distribute job processing to other resources that have greater capacity, the ability to share computer resources among programs and users, and decreasing idling computer resources with manual intervention.

While batch processing involves batches of data that have already been stored over a period of time, and is run on regularly scheduled times or on an as-needed basis, stream processing enables the user to feed data into analytics tools as soon as they get generated, facilitating real-time data processing and instant analytics results. Benefits include immediate detection of conditions and anomalies within a very short space of time, which is useful for tasks such as early fraud detection. Batch processing is beneficial in situations where it is more important to process large volumes of data than it is to gather real-time analytics results.

When to Use Steam Processing

Stream processing is ideal if you want analytics results in real time. Streaming data processing systems in big data are effective solutions for scenarios that require: minimal latency, built-in features for handling imperfect data, SQL queries on data streams to build extensive operators, guaranteed ability to generate predictable and repeatable results, stored and streaming data integration capabilities, fault tolerance features, guaranteed data safety and availability, the ability to achieve real-time response with minimal overhead for high-volume data streams, and the ability to scale applications automatically among multiple processors and nodes.

Applications in which stream processing is most effective include: algorithmic trading and stock market surveillance, computer system and network monitoring, geofencing and wildlife tracking, geospatial data processing, predictive maintenance, production line monitoring, smart device applications, smart patient care, sports analytics, supply chain optimization, surveillance and fraud detection, and traffic monitoring.

Value Stream Mapping for Process Improvement

Value stream mapping is a lean-management method that provides a visual depiction of all critical steps and data related to optimizing and improving specific, multi-step processes, and quantifies the volume and time taken at each stage. The ultimate goal is to easily identify and remove waste in value streams, creating leaner operations and increasing the productivity and efficiency of the value stream.

The value stream mapping process shows the flow of both information and materials as they progress through the process, facilitating clear analysis of the current state, improving designs of the future state, and helping teams communicate and collaborate more effectively. While value stream mapping is often associated with manufacturing, value stream processing for business processes is also popular and used in administrative processes, healthcare, logistics, product development, service related industries, software development, and supply chain.

Value Stream Mapping vs Process Mapping

Process mapping refers to the use of management and planning software that visually describes the flow of a series of events that produce an end result. A process map, or process flowchart, can be used in a variety of businesses and organizations to gain greater insight into a process and detect areas that should be updated to improve efficiency, and can improve team decision-making and problem-solving by providing clear, visual indicators of process characteristics.

While value stream mapping takes a high-level look at a company’s flow of services or goods to identify waste between and within processes, process mapping provides more granular analysis of the process.

How Does HEAVY.AI Accelerate Data Processing?

HEAVY.AI, the pioneer in accelerated analytics, harnesses the power of the GPU database to provide unparalleled processing power. The power of accelerated databases makes it easier to work with extremely large data sets or extremely fast data streams from sources such as business transactions, clickstreams, and the Internet of Things.