Great tool if you're looking for proof-of-concept real-time applications
I use IBM Streaming Analytics as a data analytics tool for a graduate class for business students in my university's college of business. …
Overview
What is 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-based, visual…
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Popular Features
- Visualization Dashboards (5)10.0100%
- Data Ingestion from Multiple Data Sources (5)9.090%
- Machine Learning Automation (5)9.090%
- Real-Time Data Analysis (5)8.080%
Pricing
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What is 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-based, visual…
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- Free/Freemium Version
- Premium Consulting/Integration Services
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Alternatives Pricing
What is Striim?
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.
What is Azure Stream Analytics?
Microsoft offers Azure Stream Analytics for IoT and connected devices, supporting real-time analytics and reporting.
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Acquire, Analyze and Act in Real Time with IBM Streams
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- FAQs
What is 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-based, visual IDE lets solution architects visually build applications or use familiar programming languages like Java™, Scala or Python. Data engineers can connect with virtually any data source — whether structured, unstructured or streaming — and integrate with Hadoop, Spark and other data infrastructures.
Its Eclipse-based, visual IDE lets solution architects visually build applications or use familiar programming languages like Java™, Scala or Python. Data engineers can connect with virtually any data source — whether structured, unstructured or streaming — and integrate with Hadoop, Spark and other data infrastructures.
Built-in domain analytics — like machine learning, natural language, spatial-temporal, text, and acoustics — create adaptive stream applications.
IBM Streams Integrations
- Watson IoT Platform
- IBM Cloudant
- IBM Event Streams (formerly Message Hub)
IBM Streams Competitors
- Azure Stream Analytics
- Amazon Kinesis
- Spark Streaming, Storm
IBM Streams Technical Details
| Operating Systems | Unspecified |
|---|---|
| Mobile Application | No |
| Supported Countries | United States, United Kingdom, Australia, Germany |
| Supported Languages | English, French, German, Italian, Japanese, Korean, Portugese/Brazil, Spanish, Chinese simplified, Chinese traditional |
Frequently Asked Questions
Azure Stream Analytics and Amazon Kinesis are common alternatives for IBM Streams.
Reviewers rate Visualization Dashboards highest, with a score of 10.
The most common users of IBM Streams are from Enterprises (1,001+ employees).
Comparisons
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Reviews and Ratings
(62)Reviews
(1-9 of 9)Companies can't remove reviews or game the system. Here's why
- Intuitive Visual Programming Interface
- Integration with other analytics via Watson Studio
- Very vibrant user communities for sharing ideas for applications
- Can use more default settings for some of the parameters
- Include more tutorials for cross-data analytic services applications
February 15, 2019
Nice streaming analytics tool
- It does really well with running a job, provides flexible ways of increasing parallelism, fusion
- It provides a good number of operators and toolkits
- It provides being able to use the Streams runtime using Java and Python
- Exception handling needs improvement, not able to catch exceptions and save data or send to a different flow
- Would like to see some alerting operators
- Would like to see some healthcare HL7 related operators
February 14, 2019
Unique advantages for real time use cases
- Query analysis of real time streaming data
- Filter out events based on time windows
- Scalability for large scale data, production tested
- Integration with big data
- Ability to write complex business rules
- Interface and packaging
February 14, 2019
IBM: Its Future is Growing
- User friendly
- Works pretty quick
- Shows how data are transported
- User interface. Takes time to get used to.
February 14, 2019
Streaming Live analysis
- Streaming is able to perform analytics operations and pattern detection in real time on real-time data from multiple systems or sources.
- IBM Streams allows for the ability to program and link to IDE for collaboration with other applications
- Integration with Business Process Automation
- Cost and platform availability
- Integration with cognitive decision making and execution
- Improvement in Graphical user interface
April 05, 2018
IBM Streams in the classroom
- Easy to integrate with other IBM cloud services.
- Fairly simple API with essential functions, not overly complicated.
- The system seems reliable and has high performance.
- Python API available.
- Documentation could be more extensive, with more examples, although overall this is not too bad compared to some of the alternative solutions.
- Seems expensive to use in production.
March 30, 2018
Streaming hot IBM review.
- Analyzing geospatial data
- Online application support
- Application hosting and management
- Documentation can be improved
- Integration of the foreign languages could be handier.
March 21, 2018
Need tutorials for IBM Stream
- Connection to other functions
- Visualized interface
- Accessible anywhere via browser
- Lack of supporting material
- Frequent update
March 19, 2018
Stream Processing - Better Knowledge Faster
- IBM Streams is well suited for providing wire-speed real-time end-to-end processing with sub-millisecond latency.
- Streams is amazingly computationally efficient. In other words, you can typically do much more processing with a given amount of hardware than other technologies. In a recent linear-road benchmark Streams based application was able to provide greater capability than the Hadoop-based implementation using 10x less hardware. So even when latency isn't critical, using Streams might still make sense for reducing operational cost.
- Streams comes out of the box with a large and comprehensive set of tested and optimized toolkits. Leveraging these toolkits not only reduces the development time and cost but also helps reduce project risk by eliminating the need for custom code which likely has not seen as much time in test or production.
- In addition to the out of the box toolkits, there is an active developer community contributing additional specialized packages.
- Although there is support for developing Streams application in Python and Java as well as a visual programming interface. In order to get the absolute most out of the platform IMO it's still best to develop applications using proprietary SPL (Stream Programming Language). Although SPL is a very effective language for stream processing it does present a barrier to entry that will be avoided with the updated visual development tools which being worked on.