#1 most frequent
United States of America
76.2%16 installations of 21
What is IBM Streams (discontinued)?
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. The product was sunsetted in 2024.
Categories & Use Cases
Product Demos
Top Performing Features
Visualization Dashboards
Easy-to-understand pictorial illustration of data with graphs charts and dashboards
Category average: 8.9
Data Ingestion from Multiple Data Sources
Ability to ingest data from many sources including Internet of Things (IoT) endpoint data, stock trading data etc, as well as static data
Category average: 8.7
Machine Learning Automation
Machine learning helps automate predictive scoring on streaming data
Category average: 8.6
Areas for Improvement
Low Latency
How many milli-seconds or seconds it takes to ingest, analyze and respond to an incoming event or data-point
Category average: 8.4
Linear Scale-Out
Easy to scale out or scale down by visually changing the resource allocation. This allows changes in load or traffic to be handled without interruptions
Category average: 7.5
Data Enrichment
Ability to enrich the data stream with static reference data
Category average: 7.7
Who Buys & Uses IBM Streams (discontinued)
#2 most frequent
Canada
9.5%2 installations of 21
#3 most frequent
India
4.8%1 installation of 21
Great tool if you're looking for proof-of-concept real-time applications
Pros
- Intuitive Visual Programming Interface
- Integration with other analytics via Watson Studio
- Very vibrant user communities for sharing ideas for applications
Cons
- Can use more default settings for some of the parameters
- Include more tutorials for cross-data analytic services applications
Return on Investment
- Low learning curve for non-programmers
- Accessible to big data platform
- Accessible to other data analytic services under one roof
IBM: Its Future is Growing
Pros
- User friendly
- Works pretty quick
- Shows how data are transported
Cons
- User interface. Takes time to get used to.
Return on Investment
- Makes it quick
- Saves my company a lot of money
Other Software Used
IBM Watson Analytics, IBM Cognos
Stream Processing - Better Knowledge Faster
Pros
- 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.
Cons
- 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.
Return on Investment
- Historically Streams has allowed me to solve problems for clients that simply could not have been addressed using any other means. So the business benefit was actually being able to provide a solution to very challenging requirements. However, the relatively recent proliferation of stream processing platforms means there are now more options available that might meet the desired requirements.
- IBM Streams was a critical component in a data science processing pipeline allowing us to identify a potential biomarker in EEG recordings indicating whether traumatic brain injury patients are at risk for developing post-traumatic epilepsy. This is important for identifying which patients should or should not be included in drug studies.
- Another successful project employed Streams as part of a pipeline for detecting and classifying sources in underwater acoustic data. Compared to other methods the approach had very high simultaneous levels of both sensitivity and discrimination. It also had the significant benefit of being able to detect signals which had not previously been observed.
Other Software Used
Apache Kafka, Redis, H2O
Need tutorials for IBM Stream
Pros
- Connection to other functions
- Visualized interface
- Accessible anywhere via browser
Cons
- Lack of supporting material
- Frequent update
Return on Investment
- It provides easy internet service via IBM cloud
- But still need more functions, and tutorials
Alternatives Considered
Watson Studio, Notebook and Node-Red
Nice streaming analytics tool
Pros
- 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
Cons
- 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
Return on Investment
- Has been able to help us with streaming analytics easily
- Different toolkits and operators help for easier development
- Would like to see some provenance capability added
Alternatives Considered
Hortonworks Data Platform
Other Software Used
IBM Operational Decision Manager, IBM Db2 Warehouse, Hortonworks Data Platform
