Best Event Stream Processing Software
Event stream processing software allows for the processing of data on the fly, enabling users to properly store, manage, and analyze their streaming data. In contrast to batch processing which focuses on historical data, stream processing allows for the processing of data in real time. Event stream processing software gives users the ability to examine how their data has changed over time. It also helps users by providing insight into anomalies and trends in the data.
Event stream processing software, with processing at its core, provides users with the capabilities they need to integrate their data, for purposes such as analytics and application development. If the user is focused on data analysis, above and beyond processing, stream analytics software is a good solution to consider.
To qualify for inclusion in the Event Stream Processing category, a product must:
Featured Event Stream Processing Software At A Glance
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Aiven for Apache Kafka® is a fully managed distributed event streaming service, that can be deployed in the cloud of your choice. Aiven for Apache Kafka is ideal for event-driven applications, near-re
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Amazon Kinesis Data Streams is a massively scalable, durable, and low-cost streaming data service. Kinesis Data Streams can continuously capture gigabytes of data per second from hundreds of thousands
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Cloud-native service for data in motion built by the original creators of Apache Kafka® Today’s consumers have the world at their fingertips and hold an unforgiving expectation for end-to-end real-ti
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Cloud Dataflow is a fully-managed service for transforming and enriching data in stream (real time) and batch (historical) modes with equal reliability and expressiveness -- no more complex workaround
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IBM StreamSets is a robust streaming data integration tool for hybrid, multi-cloud environments that enables real-time decision making. It allows ingestion and in-flight transformation of structured,
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IBM Event Streams is a high-throughput, fault-tolerant, event streaming solution. Powered by Apache Kafka, it provides access to enterprise data through event streams, enabling businesses to unlock in
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RudderStack is the leading warehouse native CDP designed to help organizations efficiently collect, unify, and activate customer data across various channels. This end-to-end solution empowers data te
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Amazon Managed Streaming for Apache Kafka (Amazon MSK) is an AWS streaming data service that manages Apache Kafka infrastructure and operations, making it easy for developers and DevOps managers to ru
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Svix is the enterprise ready webhooks sending service. With Svix, you can build a secure, reliable, and scalable webhook platform in minutes.
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Apache Kafka is an open-source stream processing platform developed by the Apache Software Foundation written in Scala and Java.
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Axual presents a suite of Kafka solutions tailored for enterprises, offering both cloud and on-premise deployment options. Axual Governance simplifies the management of existing Kafka clusters with a
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Learn More About Event Stream Processing Software
What is Event Stream Processing Software?
Data is stored and subsequently processed with traditional data processing tools. This method is not effective when data is constantly changing, as by the time the data has been stored and analyzed, it has likely already changed and become obsolete.
Event stream processing, also known as stream processing, helps ease these concerns by processing the data when it is on the move. As opposed to batch processing, which focuses on data at rest, stream processing allows for the processing of an uninterrupted flow of records. With event stream processing, the data is constantly arriving, with the focus being on identifying how the data has changed over time or detecting anomalies in the historical data, or both.
Key Benefits of Event Stream Processing Software
- Allow for extremely low latency
- Analyze data in real time
- Scale data processing, giving the user the ability to handle any amount of streaming data and process data from numerous sources
Why Use Event Stream Processing Software?
Event stream processing software is incomplete without the ability to manipulate data as it arrives. This software assists with on-the-fly processing, letting users aggregate, perform joins of data within a stream, and more. Users leverage stream processing tools to process data transferred among a whole range of internet of things (IoT) endpoints and devices, including smart cars, machinery, or home appliances. Real-time data processing is key when companies want deeper insight into their data; it is also helpful when time is of the essence—for example, in the case of retail companies looking to keep a constant and consistent record of their inventory across multiple channels.
Gain insights from data — Users leverage event stream processing software as a buffer to connect a company’s many data sources to a data storage solution, such as a data lake. From movie watching on a streaming service to taxi rides on a ride-hailing app, this data can be used for pattern identification and to inform business decisions.
Real time integration— Through the continuous collection of data from data sources, such as databases, sensors, messaging systems, and logs, users are able to ensure their applications which rely on this data are up to date.
Control data flows — Event stream processing software makes it easier to create, visualize, monitor, and maintain data flows.
Who Uses Event Stream Processing Software?
Business users working with data use event stream processing software which gives them access to data in real time.
Developers — Developers looking to build event streaming applications that rely on the flow of big data benefit from event stream processing software. For example, batch processing does not serve an application well that is aimed at providing recommendations based on real-time data. Therefore, developers rely on event stream processing software to best handle this data and process it effectively and efficiently.
Analysts — To analyze big data as it comes, analysts need to utilize a tool that processes the data. With event stream processing software, they are equipped with the proper tools to integrate the data into their analytics platforms.
Machine learning engineers — Data is a key component of the training and development of machine learning models. Having the right data processing software in place is an important part of this process.
Kinds of Event Stream Processing software
There are different methods or manners in which the stream processing takes place.
At-rest analytics — Like log analysis, at rest-analytics looks back on historical data to find trends.
In-stream analytics — A more complex form of analysis occurs with in-stream analytics in which data streams between or across devices are analyzed.
Edge analytics — This method has the added benefit of potentially lowering the latency for data that is processed on device (for example an IoT device), as the data does not necessarily need to be sent to the cloud.
Event Stream Processing Software Features
Event stream processing software, with processing at its core, provides users with the capabilities they need to integrate their data for purposes such as analytics and application development. The following features help to facilitate these tasks:
Connectors — With connectors to a wide range of core systems (e.g., via an API), users extend the reach of existing enterprise assets.
Metrics — Metrics help users analyze the processing to ascertain its performance.
Change data capture (CDC) — CDC turns databases into a streaming data source where each new transaction is delivered to event stream processing software instantaneously.
Data validation— Data validation allows users to visualize the data flow and ensure their data and data delivery is validated.
Pre-built data pipelines — Some tools provide pre-built data pipelines to enable operational workloads in the cloud.
Potential Issues with Event Stream Processing Software
Data organization — It may be challenging to organize data in a way that is easily accessible and harness big data sets that contain historical and real-time data. Companies often need to build a data warehouse or a data lake that combines all the disparate data sources for easy access. This requires highly skilled employees.
Deployment issues — Search software requires lots of work by a skilled development team or vendor support staff to properly deploy the solution, especially if the data is particularly messy. Some data may lack compatibility with different products while some solutions may be geared for different types of data. For example, some solutions may not be optimized for unstructured data, whilst others may be the best fit for numerical data.
