Event Stream Processing reviews by real, verified users. Find unbiased ratings on user satisfaction, features, and price based on the most reviews available anywhere.
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:
Backed by a 99.99% SLA, our managed Kafka solution represents the most advanced event streaming platform available. Click a button and launch a fully-operational, cloud-native, full-featured cluster in AWS, GCP, and Microsoft Azure with replicated and optimally rebalanced data across availability zones. No matter the size of your plan, you'll have access to premium features: • Kafka Rest • Schema Registry • Kafka Connect with 25+ connectors • Kafka MirrorMaker • ACLs • SASL/PLAIN and SASL/SCRAM
Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data such as video, audio, application logs, website clickstreams, and IoT telemetry, so you can get timely insights and react quickly to new information.
Spark Streaming brings Apache Spark's language-integrated API to stream processing, letting you write streaming jobs the same way you write batch jobs. It supports Java, Scala and Python. Spark Streaming recovers both lost work and operator state (e.g. sliding windows) out of the box, without any extra code on your part.
Amazon Managed Streaming for Kafka (Amazon MSK) is a fully managed service that makes it easy for you to build and run applications that use Apache Kafka to process streaming data. Apache Kafka is an open-source platform for building real-time streaming data pipelines and applications.
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 workarounds or compromises needed. And with its serverless approach to resource provisioning and management, you have access to virtually limitless capacity to solve your biggest data processing challenges, while paying only for what you use.
Set up the appropriate organizational models and governance practices to support agile integration, simplify the management of your integration architecture and reduce cost with the IBM Cloud Pak™ for Integration. Running on Red Hat® OpenShift®, the IBM Cloud Pak for Integration gives businesses complete choice and agility to deploy workloads on premises and on private and public clouds.
Lenses.io delivers a developer workspace for building & operating real-time applications on any Apache Kafka. By enabling teams to monitor, investigate, secure and deploy on their data platform, organizations can shift their focus to data-driven business outcomes and help engineers get their weekends back.
Instaclustr’s Hosted Managed Service for Apache Kafka® is the best way to run Kafka in the cloud, providing you with a production ready and fully supported Apache Kafka cluster in minutes. Instaclustr Apache Kafka® is SOC 2 and PCI certified and hosted on AWS, Azure, GCP, or on-prem. We support running in your cloud provider account or ours. We customize and optimize the configuration of your cluster so you can focus on your applications. Features ◆ 100% open source Kafka and Kafka Connect ◆
SAS Event Stream Processing continuously ingests, processes and delivers insights on live data stream detecting patterns of interest, predictive results, and pattern detection. No stream of data is too big or too fast. SAS Event Stream Processing provides design-time programming interfaces for data scientists and graphical drag and drop interfaces for business analysts, a low-latency, high throughput run-time engine for exceptional processing speeds, as well as deployment and monitoring compone
Hazelcast Jet is a high performance stream processing engine designed for building applications with extremely high throughput and low latency requirements. It supports both stream and batch processing through a pipeline API that models each job as directed acyclic graph (DAG), which lay out the tasks of a job and how they interact. It then optimizes the DAG to leverage parallelism for performance and efficiency of jobs based on the available resources in the cluster. Hazelcast Jet can be used i
At Operatr.IO, we make tools for Apache Kafka® The kPow engineering toolkit is a web-based management and monitoring console that provides simple, secure, self-contained support for Kafka. kPow allows engineers to take their Kafka observability to the next level by giving users the ability to monitor, search for, inspect, replay, and export data in real-time. Product Highlights: - Search tens of thousands of messages a second with kPow’s unique, custom implementation of JQ-like queries for K
Pandio is a software management company offering Apache Pulsar as a Service, we provide companies around the world with a durable and scalable distributed messaging service that is secure, future-proof, and fully managed by a highly experienced team. Pandio combines Queues, Streams, and PubSub into one powerful service build on Apache Pulsar.
PubSub+ is a complete event streaming and management platform for the real-time enterprise. PubSub+ helps enterprises design, deploy and manage event-driven architectures across hybrid cloud, multi-cloud and IoT environments, so they can be more integrated and event-driven. The "+" in PubSub+ means it supports a wide range of message exchange patterns beyond publish/subscribe, including request/reply, streaming and replay, as well as different qualities of service, such as best effort and guar
Some data naturally occurs as an ongoing stream of events – a continuous feed of data from remote sensors and devices in the fast-growing Internet of Things (IoT). With SAS Event Stream Processing for Edge Computing, you can: • Make faster, more intelligent decisions on the edge to understand events while they’re happening. • Analyze data continually as it's received, without having to send it to a traditional data center. • Update situational intelligence and respond with agility as new even
PubSub+ Event Portal is a tool for architects and developers who implement event-driven architectures. Event Portal provides a single place to design, create, discover, share, secure, manage and visualize all events within your system. With Event Portal you can define and model event-driven systems, manage and audit changes to events, schemas and apps, discover and share events of interest, visualize existing event-driven relationships, and quickly generate consistent code with AsyncAPI code
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
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.
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.
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, 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.
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.