Alternatives to SAS Event Stream Processing
Compare SAS Event Stream Processing alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to SAS Event Stream Processing in 2026. Compare features, ratings, user reviews, pricing, and more from SAS Event Stream Processing competitors and alternatives in order to make an informed decision for your business.
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Striim
Striim
Data integration for your hybrid cloud. Modern, reliable data integration across your private and public cloud. All in real-time with change data capture and data streams. Built by the executive & technical team from GoldenGate Software, Striim brings decades of experience in mission-critical enterprise workloads. Striim scales out as a distributed platform in your environment or in the cloud. Scalability is fully configurable by your team. Striim is fully secure with HIPAA and GDPR compliance. Built ground up for modern enterprise workloads in the cloud or on-premise. Drag and drop to create data flows between your sources and targets. Process, enrich, and analyze your streaming data with real-time SQL queries. -
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Azure Stream Analytics
Microsoft
Discover Azure Stream Analytics, the easy-to-use, real-time analytics service that is designed for mission-critical workloads. Build an end-to-end serverless streaming pipeline with just a few clicks. Go from zero to production in minutes using SQL—easily extensible with custom code and built-in machine learning capabilities for more advanced scenarios. Run your most demanding workloads with the confidence of a financially backed SLA. -
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DeltaStream
DeltaStream
DeltaStream is a unified serverless stream processing platform that integrates with streaming storage services. Think about it as the compute layer on top of your streaming storage. It provides functionalities of streaming analytics(Stream processing) and streaming databases along with additional features to provide a complete platform to manage, process, secure and share streaming data. DeltaStream provides a SQL based interface where you can easily create stream processing applications such as streaming pipelines, materialized views, microservices and many more. It has a pluggable processing engine and currently uses Apache Flink as its primary stream processing engine. DeltaStream is more than just a query processing layer on top of Kafka or Kinesis. It brings relational database concepts to the data streaming world, including namespacing and role based access control enabling you to securely access, process and share your streaming data regardless of where they are stored. -
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Informatica Data Engineering Streaming
Informatica
AI-powered Informatica Data Engineering Streaming enables data engineers to ingest, process, and analyze real-time streaming data for actionable insights. Advanced serverless deployment option with integrated metering dashboard cuts admin overhead. Rapidly build intelligent data pipelines with CLAIRE®-powered automation, including automatic change data capture (CDC). Ingest thousands of databases and millions of files, and streaming events. Efficiently ingest databases, files, and streaming data for real-time data replication and streaming analytics. Find and inventory all data assets throughout your organization. Intelligently discover and prepare trusted data for advanced analytics and AI/ML projects. -
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Amazon MSK
Amazon
Amazon Managed Streaming for Apache 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. With Amazon MSK, you can use native Apache Kafka APIs to populate data lakes, stream changes to and from databases, and power machine learning and analytics applications. Apache Kafka clusters are challenging to setup, scale, and manage in production. When you run Apache Kafka on your own, you need to provision servers, configure Apache Kafka manually, replace servers when they fail, orchestrate server patches and upgrades, architect the cluster for high availability, ensure data is durably stored and secured, setup monitoring and alarms, and carefully plan scaling events to support load changes.Starting Price: $0.0543 per hour -
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Axual
Axual
Axual is Kafka-as-a-Service for DevOps teams. Empower your team to unlock insights and drive decisions with our intuitive Kafka platform. Axual offers the ultimate solution for enterprises looking to seamlessly integrate data streaming into their core IT infrastructure. Our all-in-one Kafka platform is designed to eliminate the need for extensive technical knowledge or skills, and provides a ready-made solution that delivers all the benefits of event streaming without the hassle. The Axual Platform is a all-in-one solution, designed to help you simplify and enhance the deployment, management, and utilization of real-time data streaming with Apache Kafka. By providing an array of features that cater to the diverse needs of modern enterprises, the Axual Platform enables organizations to harness the full potential of data streaming while minimizing complexity and operational overhead. -
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IBM StreamSets
IBM
IBM® StreamSets enables users to create and manage smart streaming data pipelines through an intuitive graphical interface, facilitating seamless data integration across hybrid and multicloud environments. This is why leading global companies rely on IBM StreamSets to support millions of data pipelines for modern analytics, intelligent applications and hybrid integration. Decrease data staleness and enable real-time data at scale—handling millions of records of data, across thousands of pipelines within seconds. Insulate data pipelines from change and unexpected shifts with drag-and-drop, prebuilt processors designed to automatically identify and adapt to data drift. Create streaming pipelines to ingest structured, semistructured or unstructured data and deliver it to a wide range of destinations.Starting Price: $1000 per month -
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Lenses
Lenses.io
Enable everyone to discover and observe streaming data. Sharing, documenting and cataloging your data can increase productivity by up to 95%. Then from data, build apps for production use cases. Apply a data-centric security model to cover all the gaps of open source technology, and address data privacy. Provide secure and low-code data pipeline capabilities. Eliminate all darkness and offer unparalleled observability in data and apps. Unify your data mesh and data technologies and be confident with open source in production. Lenses is the highest rated product for real-time stream analytics according to independent third party reviews. With feedback from our community and thousands of engineering hours invested, we've built features that ensure you can focus on what drives value from your real time data. Deploy and run SQL-based real time applications over any Kafka Connect or Kubernetes infrastructure including AWS EKS.Starting Price: $49 per month -
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Azure Event Hubs
Microsoft
Event Hubs is a fully managed, real-time data ingestion service that’s simple, trusted, and scalable. Stream millions of events per second from any source to build dynamic data pipelines and immediately respond to business challenges. Keep processing data during emergencies using the geo-disaster recovery and geo-replication features. Integrate seamlessly with other Azure services to unlock valuable insights. Allow existing Apache Kafka clients and applications to talk to Event Hubs without any code changes—you get a managed Kafka experience without having to manage your own clusters. Experience real-time data ingestion and microbatching on the same stream. Focus on drawing insights from your data instead of managing infrastructure. Build real-time big data pipelines and respond to business challenges right away.Starting Price: $0.03 per hour -
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Google Cloud Dataflow
Google
Unified stream and batch data processing that's serverless, fast, and cost-effective. Fully managed data processing service. Automated provisioning and management of processing resources. Horizontal autoscaling of worker resources to maximize resource utilization. OSS community-driven innovation with Apache Beam SDK. Reliable and consistent exactly-once processing. Streaming data analytics with speed. Dataflow enables fast, simplified streaming data pipeline development with lower data latency. Allow teams to focus on programming instead of managing server clusters as Dataflow’s serverless approach removes operational overhead from data engineering workloads. Allow teams to focus on programming instead of managing server clusters as Dataflow’s serverless approach removes operational overhead from data engineering workloads. Dataflow automates provisioning and management of processing resources to minimize latency and maximize utilization. -
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Amazon Kinesis
Amazon
Easily collect, process, and analyze video and data streams in real time. Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. Amazon Kinesis offers key capabilities to cost-effectively process streaming data at any scale, along with the flexibility to choose the tools that best suit the requirements of your application. With Amazon Kinesis, you can ingest real-time data such as video, audio, application logs, website clickstreams, and IoT telemetry data for machine learning, analytics, and other applications. Amazon Kinesis enables you to process and analyze data as it arrives and respond instantly instead of having to wait until all your data is collected before the processing can begin. Amazon Kinesis enables you to ingest, buffer, and process streaming data in real-time, so you can derive insights in seconds or minutes instead of hours or days. -
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WarpStream
WarpStream
WarpStream is an Apache Kafka-compatible data streaming platform built directly on top of object storage, with no inter-AZ networking costs, no disks to manage, and infinitely scalable, all within your VPC. WarpStream is deployed as a stateless and auto-scaling agent binary in your VPC with no local disks to manage. Agents stream data directly to and from object storage with no buffering on local disks and no data tiering. Create new “virtual clusters” in our control plane instantly. Support different environments, teams, or projects without managing any dedicated infrastructure. WarpStream is protocol compatible with Apache Kafka, so you can keep using all your favorite tools and software. No need to rewrite your application or use a proprietary SDK. Just change the URL in your favorite Kafka client library and start streaming. Never again have to choose between reliability and your budget.Starting Price: $2,987 per month -
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Streaming service is a real-time, serverless, Apache Kafka-compatible event streaming platform for developers and data scientists. Streaming is tightly integrated with Oracle Cloud Infrastructure (OCI), Database, GoldenGate, and Integration Cloud. The service also provides out-of-the-box integrations for hundreds of third-party products across categories such as DevOps, databases, big data, and SaaS applications. Data engineers can easily set up and operate big data pipelines. Oracle handles all infrastructure and platform management for event streaming, including provisioning, scaling, and security patching. With the help of consumer groups, Streaming can provide state management for thousands of consumers. This helps developers easily build applications at scale.
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Confluent
Confluent
Infinite retention for Apache Kafka® with Confluent. Be infrastructure-enabled, not infrastructure-restricted Legacy technologies require you to choose between being real-time or highly-scalable. Event streaming enables you to innovate and win - by being both real-time and highly-scalable. Ever wonder how your rideshare app analyzes massive amounts of data from multiple sources to calculate real-time ETA? Ever wonder how your credit card company analyzes millions of credit card transactions across the globe and sends fraud notifications in real-time? The answer is event streaming. Move to microservices. Enable your hybrid strategy through a persistent bridge to cloud. Break down silos to demonstrate compliance. Gain real-time, persistent event transport. The list is endless. -
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KX Streaming Analytics provides the ability to ingest, store, process, and analyze historic and time series data to make analytics, insights, and visualizations instantly available. To help ensure your applications and users are productive quickly, the platform provides the full lifecycle of data services, including query processing, tiering, migration, archiving, data protection, and scaling. Our advanced analytics and visualization tools, used widely across finance and industry, enable you to define and perform queries, calculations, aggregations, machine learning and AI on any streaming and historical data. Deployable across multiple hardware environments, data can come from real-time business events and high-volume sources including sensors, clickstreams, radio-frequency identification, GPS systems, social networking sites, and mobile devices.
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Oracle Stream Analytics
Oracle
Oracle Stream Analytics allows users to process and analyze large scale real-time information by using sophisticated correlation patterns, enrichment, and machine learning. It offers real-time actionable business insight on streaming data and automates action to drive today’s agile businesses. Visual GEOProcessing with GEOFence relationship spatial analytics. New Expressive Patterns Library, including Spatial, Statistical, General industry and Anomaly detection, streaming machine learning. Abstracted visual façade to interrogate live real time streaming data and perform intuitive in-memory real time business analytics. -
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Cloudera DataFlow
Cloudera
Cloudera DataFlow for the Public Cloud (CDF-PC) is a cloud-native universal data distribution service powered by Apache NiFi that lets developers connect to any data source anywhere with any structure, process it, and deliver to any destination. CDF-PC offers a flow-based low-code development paradigm that aligns best with how developers design, develop, and test data distribution pipelines. With over 400+ connectors and processors across the ecosystem of hybrid cloud services—including data lakes, lakehouses, cloud warehouses, and on-premises sources—CDF-PC provides indiscriminate data distribution. These data distribution flows can then be version-controlled into a catalog where operators can self-serve deployments to different runtimes. -
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Apache Kafka
The Apache Software Foundation
Apache Kafka® is an open-source, distributed streaming platform. Scale production clusters up to a thousand brokers, trillions of messages per day, petabytes of data, hundreds of thousands of partitions. Elastically expand and contract storage and processing. Stretch clusters efficiently over availability zones or connect separate clusters across geographic regions. Process streams of events with joins, aggregations, filters, transformations, and more, using event-time and exactly-once processing. Kafka’s out-of-the-box Connect interface integrates with hundreds of event sources and event sinks including Postgres, JMS, Elasticsearch, AWS S3, and more. Read, write, and process streams of events in a vast array of programming languages. -
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IBM Streams
IBM
IBM Streams evaluates a broad range of streaming data — unstructured text, video, audio, geospatial and sensor — helping organizations spot opportunities and risks and make decisions in real-time. Make sense of your data, turning fast-moving volumes and varieties into insight with IBM® Streams. Streams evaluate a broad range of streaming data — unstructured text, video, audio, geospatial and sensor — helping organizations spot opportunities and risks as they happen. Combine Streams with other IBM Cloud Pak® for Data capabilities, built on an open, extensible architecture. Help enable data scientists to collaboratively build models to apply to stream flows, plus, analyze massive amounts of data in real-time. Acting upon your data and deriving true value is easier than ever. -
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Spring Cloud Data Flow
Spring
Microservice-based streaming and batch data processing for Cloud Foundry and Kubernetes. Spring Cloud Data Flow provides tools to create complex topologies for streaming and batch data pipelines. The data pipelines consist of Spring Boot apps, built using the Spring Cloud Stream or Spring Cloud Task microservice frameworks. Spring Cloud Data Flow supports a range of data processing use cases, from ETL to import/export, event streaming, and predictive analytics. The Spring Cloud Data Flow server uses Spring Cloud Deployer, to deploy data pipelines made of Spring Cloud Stream or Spring Cloud Task applications onto modern platforms such as Cloud Foundry and Kubernetes. A selection of pre-built stream and task/batch starter apps for various data integration and processing scenarios facilitate learning and experimentation. Custom stream and task applications, targeting different middleware or data services, can be built using the familiar Spring Boot style programming model. -
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Embiot
Telchemy
Embiot® is a compact, high performance IoT analytics software agent for IoT gateway and smart sensor applications. This edge computing application is small enough to integrate directly into devices, smart sensors and gateways, but powerful enough to calculate complex analytics from large amounts of raw data at high speed. Internally, Embiot uses a stream processing model to enable it to handle sensor data that arrives at different rates and out of order. It has a simple intuitive configuration language and a rich set of math, stats and AI functions making it fast and easy to solve your analytics problems. Embiot supports a range of input methods including MODBUS, MQTT, REST/XML, REST/JSON, Name/Value and CSV. Embiot is able to send output reports to multiple destinations concurrently in REST, MQTT and custom text formats. For security, Embiot supports TLS selectively on any input or output stream, HTTP and MQTT authentication. -
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PubSub+ Platform
Solace
Solace PubSub+ Platform helps enterprises design, deploy and manage event-driven systems across hybrid and multi-cloud and IoT environments so they can be more event-driven and operate in real-time. The PubSub+ Platform includes the powerful PubSub+ Event Brokers, event management capabilities with PubSub+ Event Portal, as well as monitoring and integration capabilities all available via a single cloud console. PubSub+ allows easy creation of an event mesh, an interconnected network of event brokers, allowing for seamless and dynamic data movement across highly distributed network environments. PubSub+ Event Brokers can be deployed as fully managed cloud services, self-managed software in private cloud or on-premises environments, or as turnkey hardware appliances for unparalleled performance and low TCO. PubSub+ Event Portal is a complimentary toolset for design and governance of event-driven systems including both Solace and Kafka-based event broker environments. -
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SQLstream
Guavus, a Thales company
SQLstream ranks #1 for IoT stream processing & analytics (ABI Research). Used by Verizon, Walmart, Cisco, & Amazon, our technology powers applications across data centers, the cloud, & the edge. Thanks to sub-ms latency, SQLstream enables live dashboards, time-critical alerts, & real-time action. Smart cities can optimize traffic light timing or reroute ambulances & fire trucks. Security systems can shut down hackers & fraudsters right away. AI / ML models, trained by streaming sensor data, can predict equipment failures. With lightning performance, up to 13M rows / sec / CPU core, companies have drastically reduced their footprint & cost. Our efficient, in-memory processing permits operations at the edge that are otherwise impossible. Acquire, prepare, analyze, & act on data in any format from any source. Create pipelines in minutes not months with StreamLab, our interactive, low-code GUI dev environment. Export SQL scripts & deploy with the flexibility of Kubernetes. -
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Azure Data Explorer
Microsoft
Azure Data Explorer is a fast, fully managed data analytics service for real-time analysis on large volumes of data streaming from applications, websites, IoT devices, and more. Ask questions and iteratively explore data on the fly to improve products, enhance customer experiences, monitor devices, and boost operations. Quickly identify patterns, anomalies, and trends in your data. Explore new questions and get answers in minutes. Run as many queries as you need, thanks to the optimized cost structure. Explore new possibilities with your data cost-effectively. Focus on insights, not infrastructure, with the easy-to-use, fully managed data analytics service. Respond quickly to fast-flowing and rapidly changing data. Azure Data Explorer simplifies analytics from all forms of streaming data.Starting Price: $0.11 per hour -
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Digital Twin Streaming Service
ScaleOut Software
ScaleOut Digital Twin Streaming Service™ Easily build and deploy real-time digital twins for streaming analytics Connect to many data sources with Azure & AWS IoT hubs, Kafka, and more Maximize situational awareness with live, aggregate analytics. Introducing a breakthrough cloud service that simultaneously tracks telemetry from millions of data sources with “real-time” digital twins — enabling immediate, deep introspection with state-tracking and highly targeted, real-time feedback for thousands of devices. A powerful UI simplifies deployment and displays aggregate analytics in real time to maximize situational awareness. Ideal for a wide range of applications, including the Internet of Things (IoT), real-time intelligent monitoring, logistics, and financial services. Simplified pricing makes getting started fast and easy. Combined with the ScaleOut Digital Twin Builder software toolkit, the ScaleOut Digital Twin Streaming Service enables the next generation in stream processing. -
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Apache Flink
Apache Software Foundation
Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Flink has been designed to run in all common cluster environments, perform computations at in-memory speed and at any scale. Any kind of data is produced as a stream of events. Credit card transactions, sensor measurements, machine logs, or user interactions on a website or mobile application, all of these data are generated as a stream. Apache Flink excels at processing unbounded and bounded data sets. Precise control of time and state enable Flink’s runtime to run any kind of application on unbounded streams. Bounded streams are internally processed by algorithms and data structures that are specifically designed for fixed sized data sets, yielding excellent performance. Flink is designed to work well each of the previously listed resource managers. -
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Apama
Apama
Apama Streaming Analytics allows organizations to analyze and act on IoT and fast-moving data in real-time, responding to events intelligently the moment they happen. Apama Community Edition is a freemium version of Apama by Software AG that can be used to learn about, develop and put streaming analytics applications into production. The Software AG Data & Analytics Platform is an end-toend, modular and integrated set of world-class capabilities optimized for high-speed data management and analytics on real-time data and offering out-of-the-box integration and connectivity to all key enterprise data sources. Choose the capabilities you need: streaming, predictive and visual analytics along with messaging for easy integration with other enterprise apps and an in-memory data store for extremely fast access. Integrate historical and other data for comparison—ideal when building models or enriching customer and other vital data. -
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Astra Streaming
DataStax
Responsive applications keep users engaged and developers inspired. Rise to meet these ever-increasing expectations with the DataStax Astra Streaming service platform. DataStax Astra Streaming is a cloud-native messaging and event streaming platform powered by Apache Pulsar. Astra Streaming allows you to build streaming applications on top of an elastically scalable, multi-cloud messaging and event streaming platform. Astra Streaming is powered by Apache Pulsar, the next-generation event streaming platform which provides a unified solution for streaming, queuing, pub/sub, and stream processing. Astra Streaming is a natural complement to Astra DB. Using Astra Streaming, existing Astra DB users can easily build real-time data pipelines into and out of their Astra DB instances. With Astra Streaming, avoid vendor lock-in and deploy on any of the major public clouds (AWS, GCP, Azure) compatible with open-source Apache Pulsar. -
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Materialize
Materialize
Materialize is a reactive database that delivers incremental view updates. We help developers easily build with streaming data using standard SQL. Materialize can connect to many different external sources of data without pre-processing. Connect directly to streaming sources like Kafka, Postgres databases, CDC, or historical sources of data like files or S3. Materialize allows you to query, join, and transform data sources in standard SQL - and presents the results as incrementally-updated Materialized views. Queries are maintained and continually updated as new data streams in. With incrementally-updated views, developers can easily build data visualizations or real-time applications. Building with streaming data can be as simple as writing a few lines of SQL.Starting Price: $0.98 per hour -
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TIBCO Streaming
TIBCO
TIBCO Streaming is a real-time analytics platform designed to process and analyze high-velocity data streams, enabling organizations to make immediate, data-driven decisions. It offers a low-code development environment through StreamBase Studio, allowing users to build complex event processing applications with minimal coding. It supports over 150 connectors, including APIs, Apache Kafka, MQTT, RabbitMQ, and databases like MySQL and JDBC, facilitating seamless integration with various data sources. TIBCO Streaming incorporates dynamic learning operators, enabling adaptive machine learning models that provide contextual insights and automate decision-making processes. It also features real-time business intelligence capabilities, allowing users to visualize live data alongside historical information for comprehensive analysis. It is cloud-ready, supporting deployments on AWS, Azure, GCP, and on-premises environments. -
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Cogility Cogynt
Cogility Software
Deliver Continuous Intelligence solutions easier, faster, and cost-effectively - with less engineering effort. The Cogility Cogynt platform delivers cloud-scalable event stream processing software powered by advanced, Expert AI-based analytics. A complete, integrated toolset enables organizations to quickly, easily, and more efficiently deliver continuous intelligence solutions. The end-to-end platform streamlines deployment, constructing model logic, customizing data source intake, processing data streams, examining, visualizing and sharing intelligence findings, auditing and improving results, and integrating with other applications. Cogynt’s Authoring Tool provides a convenient, zero-code design environment for creating, updating, and deploying data models. Cogynt’s Data Management Tool makes it easy to publish your model to immediately apply to stream data processing while abstracting Flink job coding. -
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Spark Streaming
Apache Software Foundation
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. By running on Spark, Spark Streaming lets you reuse the same code for batch processing, join streams against historical data, or run ad-hoc queries on stream state. Build powerful interactive applications, not just analytics. Spark Streaming is developed as part of Apache Spark. It thus gets tested and updated with each Spark release. You can run Spark Streaming on Spark's standalone cluster mode or other supported cluster resource managers. It also includes a local run mode for development. In production, Spark Streaming uses ZooKeeper and HDFS for high availability. -
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Apache Spark
Apache Software Foundation
Apache Spark™ is a unified analytics engine for large-scale data processing. Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine. Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells. Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application. Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources. You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, on Mesos, or on Kubernetes. Access data in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and hundreds of other data sources. -
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Red Hat OpenShift Streams
Red Hat
Red Hat® OpenShift® Streams for Apache Kafka is a managed cloud service that provides a streamlined developer experience for building, deploying, and scaling new cloud-native applications or modernizing existing systems. Red Hat OpenShift Streams for Apache Kafka makes it easy to create, discover, and connect to real-time data streams no matter where they are deployed. Streams are a key component for delivering event-driven and data analytics applications. The combination of seamless operations across distributed microservices, large data transfer volumes, and managed operations allows teams to focus on team strengths, speed up time to value, and lower operational costs. OpenShift Streams for Apache Kafka includes a Kafka ecosystem and is part of a family of cloud services—and the Red Hat OpenShift product family—which helps you build a wide range of data-driven solutions. -
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Eclipse Streamsheets
Cedalo
Build professional applications to automate workflows, continuously monitor operations, and control processes in real-time. Your solutions run 24/7 on servers in the cloud and on the edge. Thanks to the spreadsheet user interface, you do not have to be a software developer. Instead of writing program code, you drag-and-drop data, fill cells with formulas, and design charts in a way you already know. Find all necessary protocols on board that you need to connect to sensors, and machines like MQTT, REST, and OPC UA. Streamsheets is native to stream data processing like MQTT and kafka. Pick up a topic stream, transform it and blast it back out into the endless streaming world. REST opens you the world, Streamsheets let you connect to any web service or let them connect to you. Streamsheets run in the cloud, on your servers, but also on edge devices like a Raspberry Pi. -
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Pathway
Pathway
Pathway is a Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG. Pathway comes with an easy-to-use Python API, allowing you to seamlessly integrate your favorite Python ML libraries. Pathway code is versatile and robust: you can use it in both development and production environments, handling both batch and streaming data effectively. The same code can be used for local development, CI/CD tests, running batch jobs, handling stream replays, and processing data streams. Pathway is powered by a scalable Rust engine based on Differential Dataflow and performs incremental computation. Your Pathway code, despite being written in Python, is run by the Rust engine, enabling multithreading, multiprocessing, and distributed computations. All the pipeline is kept in memory and can be easily deployed with Docker and Kubernetes. -
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Xeotek
Xeotek
Xeotek helps companies develop and explore their data applications and streams faster with Xeotek's powerful desktop and web application. Xeotek KaDeck was designed to be used by developers, operations, and business users alike. Because business users, developers, and operations jointly gain insight into data and processes via KaDeck, the whole team benefits: fewer misunderstandings, less rework, more transparency. Xeotek KaDeck puts you in control of your data streams. Save hours of work by gaining insights at the data and application level in projects or day-to-day operations. Export, filter, transform and manage data streams in KaDeck with ease. Run JavaScript (NodeV4) code, transform & generate test data, view & change consumer offsets, manage your streams or topics, Kafka Connect instances, schema registry, and ACLs – all from one convenient user interface. -
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Arroyo
Arroyo
Scale from zero to millions of events per second. Arroyo ships as a single, compact binary. Run locally on MacOS or Linux for development, and deploy to production with Docker or Kubernetes. Arroyo is a new kind of stream processing engine, built from the ground up to make real-time easier than batch. Arroyo was designed from the start so that anyone with SQL experience can build reliable, efficient, and correct streaming pipelines. Data scientists and engineers can build end-to-end real-time applications, models, and dashboards, without a separate team of streaming experts. Transform, filter, aggregate, and join data streams by writing SQL, with sub-second results. Your streaming pipelines shouldn't page someone just because Kubernetes decided to reschedule your pods. Arroyo is built to run in modern, elastic cloud environments, from simple container runtimes like Fargate to large, distributed deployments on the Kubernetes logo Kubernetes. -
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Akka
Akka
Akka is a toolkit for building highly concurrent, distributed, and resilient message-driven applications for Java and Scala. Akka Insights is intelligent monitoring and observability purpose-built for Akka. Actors and Streams let you build systems that scale up, using the resources of a server more efficiently, and out, using multiple servers. Building on the principles of The Reactive Manifesto Akka allows you to write systems that self-heal and stay responsive in the face of failures. Distributed systems without single points of failure. Load balancing and adaptive routing across nodes. Event Sourcing and CQRS with Cluster Sharding. Distributed Data for eventual consistency using CRDTs. Asynchronous non-blocking stream processing with backpressure. Fully async and streaming HTTP server and client provides a great platform for building microservices. Streaming integrations with Alpakka. -
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Aiven for Apache Kafka
Aiven
Apache Kafka as a fully managed service, with zero vendor lock-in and a full set of capabilities to build your streaming pipeline. Set up fully managed Kafka in less than 10 minutes — directly from our web console or programmatically via our API, CLI, Terraform provider or Kubernetes operator. Easily connect it to your existing tech stack with over 30 connectors, and feel confident in your setup with logs and metrics available out of the box via the service integrations. A fully managed distributed data streaming platform, deployable in the cloud of your choice. Ideal for event-driven applications, near-real-time data transfer and pipelines, stream analytics, and any other case where you need to move a lot of data between applications — and quickly. With Aiven’s hosted and managed-for-you Apache Kafka, you can set up clusters, deploy new nodes, migrate clouds, and upgrade existing versions — in a single mouse click — and monitor them through a simple dashboard.Starting Price: $200 per month -
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IBM Event Streams is a fully managed event streaming platform built on Apache Kafka, designed to help enterprises process and respond to real-time data streams. With capabilities for machine learning integration, high availability, and secure cloud deployment, it enables organizations to create intelligent applications that react to events as they happen. The platform supports multi-cloud environments, disaster recovery, and geo-replication, making it ideal for mission-critical workloads. IBM Event Streams simplifies building and scaling real-time, event-driven solutions, ensuring data is processed quickly and efficiently.
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Leo
Leo
Turn your data into a realtime stream, making it immediately available and ready to use. Leo reduces the complexity of event sourcing by making it easy to create, visualize, monitor, and maintain your data flows. Once you unlock your data, you are no longer limited by the constraints of your legacy systems. Dramatically reduced dev time keeps your developers and stakeholders happy. Adopt microservice architectures to continuously innovate and improve agility. In reality, success with microservices is all about data. An organization must invest in a reliable and repeatable data backbone to make microservices a reality. Implement full-fledged search in your custom app. With data flowing, adding and maintaining a search database will not be a burden.Starting Price: $251 per month -
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Macrometa
Macrometa
We deliver a geo-distributed real-time database, stream processing and compute runtime for event-driven applications across up to 175 worldwide edge data centers. App & API builders love our platform because we solve the hardest problems of sharing mutable state across 100s of global locations, with strong consistency & low latency. Macrometa enables you to surgically extend your existing infrastructure to bring part of or your entire application closer to your end users. This allows you to improve performance, user experience, and comply with global data governance laws. Macrometa is a serverless, streaming NoSQL database, with integrated pub/sub and stream data processing and compute engine. Create stateful data infrastructure, stateful functions & containers for long running workloads, and process data streams in real time. You do the code, we do all the ops and orchestration. -
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Upsolver
Upsolver
Upsolver makes it incredibly simple to build a governed data lake and to manage, integrate and prepare streaming data for analysis. Define pipelines using only SQL on auto-generated schema-on-read. Easy visual IDE to accelerate building pipelines. Add Upserts and Deletes to data lake tables. Blend streaming and large-scale batch data. Automated schema evolution and reprocessing from previous state. Automatic orchestration of pipelines (no DAGs). Fully-managed execution at scale. Strong consistency guarantee over object storage. Near-zero maintenance overhead for analytics-ready data. Built-in hygiene for data lake tables including columnar formats, partitioning, compaction and vacuuming. 100,000 events per second (billions daily) at low cost. Continuous lock-free compaction to avoid “small files” problem. Parquet-based tables for fast queries. -
45
Crosser
Crosser Technologies
Analyze and act on your data in the Edge. Make Big Data small and relevant. Collect sensor data from all your assets. Connect any sensor, PLC, DCS, MES or Historian. Condition monitoring of remote assets. Industry 4.0 data collection & integration. Combine streaming and enterprise data in data flows. Use your favorite Cloud Provider or your own data center for storage of data. Bring, manage and deploy your own ML models with Crosser Edge MLOps functionality. The Crosser Edge Node is open to run any ML framework. Central resource library for your trained models in crosser cloud. Drag-and-drop for all other steps in the data pipeline. One operation to deploy ML models to any number of Edge Nodes. Self-Service Innovation powered by Crosser Flow Studio. Use a rich library of pre-built modules. Enables collaboration across teams and sites. No more dependencies on single team members. -
46
Flowcore
Flowcore
The Flowcore platform provides you with event streaming and event sourcing in a single, easy-to-use service. Data flow and replayable storage, designed for developers at data-driven startups and enterprises that aim to stay at the forefront of innovation and growth. All your data operations are efficiently persisted, ensuring no valuable data is ever lost. Immediate transformations and reclassifications of your data, loading it seamlessly to any required destination. Break free from rigid data structures. Flowcore's scalable architecture adapts to your growth, handling increasing volumes of data with ease. By simplifying and streamlining backend data processes, your engineering teams can focus on what they do best, creating innovative products. Integrate AI technologies more effectively, enriching your products with smart, data-driven solutions. Flowcore is built with developers in mind, but its benefits extend beyond the dev team.Starting Price: $10/month -
47
Nussknacker
Nussknacker
Nussknacker is a low-code visual tool for domain experts to define and run real-time decisioning algorithms instead of implementing them in the code. It serves where real-time actions on data have to be made: real-time marketing, fraud detection, Internet of Things, Customer 360, and Machine Learning inferring. An essential part of Nussknacker is a visual design tool for decision algorithms. It allows not-so-technical users – analysts or business people – to define decision logic in an imperative, easy-to-follow, and understandable way. Once authored, with a click of a button, scenarios are deployed for execution. And can be changed and redeployed anytime there’s a need. Nussknacker supports two processing modes: streaming and request-response. In streaming mode, it uses Kafka as its primary interface. It supports both stateful and stateless processing.Starting Price: 0 -
48
Radicalbit
Radicalbit
Radicalbit Natural Analytics (RNA) is a DataOps platform for Streaming Data Integration and Real-time Advanced Analytics. Choose the easiest way to deliver data at the right time in the right hands. RNA provides users with the latest technologies – in self service mode – for real-time data processing, taking advantage of Artificial Intelligence solutions for extracting value from data. It automates the labor-intensive process of data analysis and helps convey important findings and insights in understandable formats. Have a Real-time situational awareness and timely insights to respond quickly and appropriately. Achieve new levels of efficiency and optimization and guarantee collaboration between siloed teams. Manage and monitor the models from a centralized view, and deploy your evolving models in seconds. No downtime. -
49
Esper Enterprise Edition
EsperTech Inc.
Esper Enterprise Edition is a distributable platform for linear and elastic horizontal scalability and fault-tolerant event processing. EPL editor and debugger; Hot deployment; Detailed metric and memory use reporting with break-down and summary per EPL. Data Push for multi-tier CEP-to-Browser delivery; Management of Logical and Physical Subscribers and Subscriptions. Web-based user interface for managing all aspects of multiple distributed engine instances with JavaScript and HTML 5. Composable, configurable and interactive displays of distributed event streams or series; Charts, Gauges, Timelines, Grids. JDBC-compliant client and server endpoints for interoperability. Esper Enterprise Edition is a closed-source commercial product by EsperTech. The source code is made available to support customers only. Esper Enterprise Edition is a distributable platform for linear and elastic horizontal scalability and fault-tolerant event processing. -
50
Kinetica
Kinetica
A scalable cloud database for real-time analysis on large and streaming datasets. Kinetica is designed to harness modern vectorized processors to be orders of magnitude faster and more efficient for real-time spatial and temporal workloads. Track and gain intelligence from billions of moving objects in real-time. Vectorization unlocks new levels of performance for analytics on spatial and time series data at scale. Ingest and query at the same time to act on real-time events. Kinetica's lockless architecture and distributed ingestion ensures data is available to query as soon as it lands. Vectorized processing enables you to do more with less. More power allows for simpler data structures, which lead to lower storage costs, more flexibility and less time engineering your data. Vectorized processing opens the door to amazingly fast analytics and detailed visualization of moving objects at scale.
