Streaming Analytics Software
The TIBCO Streaming (StreamBase®) platform is a high-performance system for rapidly building applications that analyze and act on real-time streaming data. Using TIBCO Streaming, users can rapidly build real-time systems and deploy them at a fraction of the cost and risk of other…
IBM Streaming Analytics is a fully managed service that frees you from time-consuming installation, administration, and management tasks, giving you more time to develop streaming applications. It is powered by IBM Streams, an advanced analytic platform that you can use to ingest,…
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…
Dataminr Pulse is presented as Real-Time Event and Risk Detection solution for businesses, public sector, and news organization, that leverages AI to give users early indication of business-critical information about risks to people, a brand, and physical and virtual assets – so…
Apama Streaming Analytics is a platform for streaming analytics and intelligent automated action on fast-moving big data. Combining event processing, messaging, in-memory data management and visualization, this platform is presented as a complete solution to turn relentless data…
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-…
EVAM is a real-time Continuous Intelligence Platform for customer journey orchestration having real-time data integration and streaming advanced analytics with ML/AI.Evam Continuous Intelligence Platform is deployed in different industries including Telco, Banking, Retail & Loyalty,…
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 (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 , 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 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 and 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. and 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 , you’ll need a solution that can process the telemetry data coming from sensors, cameras, and other real-world measurement devices. and 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. and 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, leverage C# and SQL, whereas can support Python and Scala.
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.
If you need more information about structured and unstructured data, this resource will be helpful for you: