Streaming Analytics Software

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Tealium Customer Data Hub

The Tealium Customer Data Hub powers capabilities across the data supply chain. Tealium universally collects customer data from any source including; websites, mobile applications, devices, kiosks, servers, and files. Data collected is then standardized in the data layer, which drives…

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Tealium Customer Data Hub

The Tealium Customer Data Hub powers capabilities across the data supply chain. Tealium universally collects customer data from any source including; websites, mobile applications, devices, kiosks, servers, and files. Data collected is then standardized in the data layer, which drives…

IBM Event Streams

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 insights from historical data, and identify and take action on situations in real…

Spotfire Streaming

The Spotfire Streaming (formerly TIBCO Streaming or StreamBase) platform is a high-performance system for rapidly building applications that analyze and act on real-time streaming data. Using Spotfire Streaming, users can rapidly build real-time systems and deploy them at a fraction…

IBM Streams

A real-time analytics solution that turns fast-moving volumes and varieties into insights. Streams evaluates a broad range of streaming data — unstructured text, video, audio, geospatial and sensor — helping organizations spot opportunities and risks as they happen. Its Eclipse-…

Amazon Kinesis

Amazon Kinesis is a streaming analytics suite for data intake from video or other disparate sources and applying analytics for machine learning (ML) and business intelligence.


Striim is an enterprise-grade platform that offers continuous real-time data ingestion, high-speed in-flight stream processing, and sub-second delivery of data to cloud and on-premises endpoints.


Confluent Cloud is a cloud-native service for Apache Kafka used to connect and process data in real time with a fully managed data streaming platform. Confluent Platform is the self-managed version.

Azure Stream Analytics

Microsoft offers Azure Stream Analytics for IoT and connected devices, supporting real-time analytics and reporting.


jKool is a streaming analytics platform from the company of the same name in Melville, that analyzes fast data such as logs, metrics, transactions in real-time so users can focus on finding insight and opportunities in their data.

Elecard StreamEye Studio

Elecard StreamEye - a software tool for professionals in the video compression field. It enables in-depth bitstream analysis to macroblock level, codec parameters inspection. MPEG-1, MPEG-2, AVC/H.264, HEVC/H.265, AV1, VP9, VVC (preview version). Finding an issue in the elementary…

Apache Spark Streaming

Apache Spark Streaming is a scalable fault-tolerant streaming processing system that natively supports both batch and streaming workloads.

Astra Streaming

DataStax Astra Streaming is a fully-managed event streaming service powered by Apache Pulsar that was built to scale. Astra Streaming has been built to run in the cloud of your choice, including (GCP, AWS, Microsoft Azure) without sacrificing open-source compatibility.

Enterprise Fluentd

Used by Microsoft, Amazon, Google, and many more, Fluentd was invented by Treasure Data to easily collect, parse, and deliver massive amounts of data from applications, infrastructure, network devices, and log files. Enterprise Fluentd expands on that original vision and brings enterprise-…

Apache Flink

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. And FlinkCEP is the Complex…

Google Cloud Dataflow

Google offers Cloud Dataflow, a managed streaming analytics platform for real-time data insights, fraud detection, and other purposes.

Cloudera Data Platform

Cloudera Data Platform (CDP), launched September 2019, is designed to combine the best of Hortonworks and Cloudera technologies to deliver an enterprise data cloud. CDP includes the Cloudera Data Warehouse and machine learning services as well as a Data Hub service for building custom…

StreamSets DataOps Platform

StreamSets in San Francisco offers their DataOps Platform, a subscription based streaming analytics platform including StreamSets Data Collector data source management, Control Hub for data movement architecture management, StreamSets Data Collector Edge IoT manager, DataFlow Performance…


Rockset is a serverless search and analytics engine that does fast SQL on NoSQL data from Kafka, DynamoDB, S3 and more. According to the vendor, it delivers millisecond-latency SQL over TBs of raw data, without any ETL. Rockset integrates with the user's database, data stream or…

M3 Open Source Metrics Engine (M3DB)

M3 is a Prometheus compatible, metrics engine that provides visibility, consisting of 3 simple components for ingestion and streaming aggregation, a timeseries database, and a real-time query engine. It is available free and open source under the Apache 2.0 license, and was originally…

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Eventador headquartered in Austin aims to eliminate the need for intricate programming and streamlines writing, deploying, joining, and managing fault-tolerant data streams with standards-compliant SQL via SQLStreamBuilder. Additionally, Eventador simplifies managing, scaling, and…

Esper Enterprise Edition

Esper Enterprise Edition is an enterprise ready, complex event and streaming analytics processing platform.

Altimetrik A3P

Altimetrik headquartered in Southfield offers the A3P analytics tool.

Apache Samza
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Apache Samza is an open-source near-realtime, asynchronous computational framework for stream processing developed by the Apache Software.

Hitachi Streaming Data Platform (HSDP)

Hitachi Vantara offers the Hitachi Streaming Data Platform (HSDP) supporting IoT operations with data-in-motion streaming analytics.

BlackLynx Accelerated Analytics

BlackLynx (formerly Ryft) offers their Accelerated Analytics service.

Learn More About Streaming Analytics Software

What is Streaming Analytics?

Streaming analytics software processes and analyzes fast-moving live and historical data to raise alerts, make decisions, and report findings in real-time without human intervention. Streaming analytics can gather insights from multiple sources of data, such as applications, mobile devices, and machines. Because of this, they can determine threats or opportunities, address them quickly, and create protocols to address similar events in the future. Since streaming analytics can almost instantly acknowledge and address patterns in large volumes of information from many sources, it is useful for the rapid analysis of real-time data. This can include data from Internet of Things (IoT) sensors, medical monitoring equipment, and internal financial transactions.

For example, if you have a network that you need to monitor, there is a list of things that need to be managed - temperature of important hardware, connection to the internet, ongoing security programs, and so forth. Streaming analytics will capture data from all of these sources, recognize patterns, and address irregularities. If there is an issue with the network - a security protocol is suddenly disabled, for instance - streaming analytics will instantly and autonomously determine the source of the issue, create a course of action based on the most appropriate response, and use this information to detect possible threats in the future.

Streaming analytics solutions are similar to complex event processing (CEP) software, but they provide more general support for such as storage, monitoring, analysis, visualization, modeling, message queuing, and processing for batch and aggregate data without the need for correlation calculation with CEP. This enables a simpler user experience by removing the need to consider event correlation. Additionally, compared to CEP, streaming analytics tend to have better support for parallel processing, which sees data broken up into ‘chunks” that are processed and analyzed simultaneously.

Streaming Analytics Features

These are the most common features among streaming analytics solutions:

  • Proactive monitoring
  • Security monitoring
  • Parallel processing
  • Fault-tolerant processing
  • Integrated machine learning capabilities
  • Batch processing
  • Big Data streaming.
  • Asynchronous data messaging
  • Compatibility with multiple data sources
  • Data archiving and retention
  • Data migration and integration
  • Data masking
  • Data aggregation
  • Data virtualization
  • Data analysis
  • Data reporting and visualization
  • Disaster recovery
  • Audit trails
  • Hierarchical modeling
  • Query framework
  • Datastream customization and blending
  • Integrated dashboard
  • Cloud, browser, or on-premise hosting

Streaming Analytics Comparison

When comparing streaming analytics solutions, consider the following:

Open-source vs. monitored platforms. Open-source streaming analytics solutions like Apama Community Edition have a wide range of benefits for small businesses, including personalization, flexibility, compatibility with other solutions, and low cost. However, their installation, integration, management, and troubleshooting are handled by the end-user, meaning they may require a bit more time and effort from your IT department. Monitored platforms like Amazon Kinesis and Google Cloud DataFlow manage the major responsibilities of maintaining streaming analytics in exchange for increased cost, more limited personalization, and removal of some control (namely server uptime) from the end-user.

Structured vs. unstructured data processing. Structured data refers to data that is specific and stored in predefined formatting, whereas unstructured data is varied and stored in its native formatting. The type of data you expect to handle will determine the best solution for you, as some streaming analytics may not perform as well with unstructured data, which in turn may impact overall performance. IBM Streaming Analytics and SAS Event Stream Processing boast robust support for handling both structured and unstructured data streams.

Telemetry analysis. If you intend to use streaming analytics to monitor data from the Internet of Things, you’ll need a solution that can process the telemetry data coming from sensors, cameras, and other real-world measurement devices. Oracle Stream Analytics and IBM Streaming Analytics offer geospatial analysis solutions.

Fault-tolerance processing. The degree to which streaming analytics software handles faults within datastreams - fragmented data, read failure, low latency, and so forth - will determine which solution works best for you. This is especially true if your business handles time-sensitive data, as fault-tolerance can increase overall processing time. Flink and Kafka have robust auto-restart analysis features, making them efficient in their fault tolerance.

Programming language. Finally, streaming analytics solutions may have limited ranges of programming languages that they can use to create or develop real-time analytics. Java is universally supported, but if you require support for more specific languages, you’ll need to ensure a solution can work with it. For example, Azure Stream Analytics leverage C# and SQL, whereas IBM Stream Analytics can support Python and Scala.

Pricing Information

There are many free, open-source streaming analytics solutions. For paid solutions, prices can range between $15 to $200 a month at the lowest subscription cost, with possible variation based on the amount of data processed. These vendors also offer free trials or low-cost price plans with limited features. Vendors should be contacted directly for price points.

More Resources

If you need more information about structured and unstructured data, this resource will be helpful for you:

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Frequently Asked Questions

What does streaming analytics software do?

Streaming analytics products analyze and report on large quantities of streamed data in real-time. They monitor internal transactions, detect threats, and provide solutions, as well as visualize data analysis.

What are the benefits of using streaming analytics software?

They provide cost-efficient ways to automize many data science tasks, including detecting system or equipment interruptions, gathering and analyzing large amounts of data, and providing proactive monitoring for future events.

How much does streaming analytics software cost?

There are many free and low-cost open source solutions. Paid services range between $15 and $200 per month at their lowest subscription tiers, with many vendors offering free plans and trials.