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https://dudodiprj2sv7.cloudfront.net/product-logos/xO/yb/TFUEPOZ6GFJT.PNGStream Processing - Better Knowledge FasterIBM Streams allows me to solve problems for my clients that would be difficult, impossible, or too expensive to do with other technologies. Most of the applications areas for which I have been applying it have been real-time in nature with a requirement for low end-to-end latency. Lately, a common use case has been to use Streams to ingest and transform incoming events into a form more suitable for storing for subsequent long-term analysis such as model training. For example, ingesting complex nested JSON documents and transforming and enriching that data into a flattened columnar format to be persisted in Spark, Event Store, Parquet files, etc. With this pattern, Streams is one element in an overall data processing pipeline where multiple technologies are optimally employed to do what they do best. Some of the features provided by the IBM Streams platform are particularly well suited for implementing dynamic microservices than can quickly be developed and deployed to provide valuable agility for evolving problem spaces.,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.,8,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.,,Apache Kafka, Redis, H2ONeed tutorials for IBM StreamI'm using IBM DSx, which is now known as Watson Studio. Honestly speaking, this platform is updating quite often, which make it hard to learn all functions. IBM Streams is one of them and I haven't figured out all possibilities it got. And tutorials are rather hard to find and some tutorials are out of date. I hope there could be more tutorials for different application examples.,Connection to other functions Visualized interface Accessible anywhere via browser,Lack of supporting material Frequent update,7,It provides easy internet service via IBM cloud But still need more functions, and tutorials,Watson Studio, Notebook and Node-RedIBM Streams in the classroomI used IBM Streams to design a lab at a university used for analyzing streaming data from IoT devices. The students had to implement signal processing algorithms using the IBM Streams API.,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.,8,N/A,Streaming hot IBM review.Clusterization and compaction of customer data; used mainly by the department responsible for data preprocessing before further analysis.,Analyzing geospatial data Online application support Application hosting and management,Documentation can be improved Integration of the foreign languages could be handier.,7,Wasn't able to estimate during the usage time.,Docker, IntelliJ WebStorm, Atlassian Confluence
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IBM Streaming Analytics
18 Ratings
Score 7.2 out of 101
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IBM Streaming Analytics Reviews

IBM Streaming Analytics
18 Ratings
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Score 7.2 out of 101
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Jim Sharpe profile photo
March 19, 2018

IBM Streaming Analytics Review: "Stream Processing - Better Knowledge Faster"

Score 8 out of 10
Vetted Review
Verified User
Review Source
IBM Streams allows me to solve problems for my clients that would be difficult, impossible, or too expensive to do with other technologies. Most of the applications areas for which I have been applying it have been real-time in nature with a requirement for low end-to-end latency. Lately, a common use case has been to use Streams to ingest and transform incoming events into a form more suitable for storing for subsequent long-term analysis such as model training. For example, ingesting complex nested JSON documents and transforming and enriching that data into a flattened columnar format to be persisted in Spark, Event Store, Parquet files, etc. With this pattern, Streams is one element in an overall data processing pipeline where multiple technologies are optimally employed to do what they do best. Some of the features provided by the IBM Streams platform are particularly well suited for implementing dynamic microservices than can quickly be developed and deployed to provide valuable agility for evolving problem spaces.
  • 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.
Streams is a good fit for situations requiring low end-to-end latency, have complex real-time analytical processing needs on large fast data, or where the reduction of operational costs is important. However, it is very much a data-in-motion technology and not well suited for situations such as some forms of machine learning where the entire historical data set needs to be operated on. Note that it's fairly common to use Streams to perform online scoring using models that were trained offline using other technologies.
Read Jim Sharpe's full review
Zhaoyan Lyu profile photo
March 21, 2018

IBM Streaming Analytics Review: "Need tutorials for IBM Stream"

Score 7 out of 10
Vetted Review
Verified User
Review Source
I'm using IBM DSx, which is now known as Watson Studio. Honestly speaking, this platform is updating quite often, which make it hard to learn all functions. IBM Streams is one of them and I haven't figured out all possibilities it got. And tutorials are rather hard to find and some tutorials are out of date. I hope there could be more tutorials for different application examples.
  • Connection to other functions
  • Visualized interface
  • Accessible anywhere via browser
  • Lack of supporting material
  • Frequent update
All kinds of IoT projects which require a runtime environment can take advantage of IBM Streams.
Read Zhaoyan Lyu's full review
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April 05, 2018

IBM Streaming Analytics Review: "IBM Streams in the classroom"

Score 8 out of 10
Vetted Review
Verified User
Review Source
I used IBM Streams to design a lab at a university used for analyzing streaming data from IoT devices. The students had to implement signal processing algorithms using the IBM Streams API.
  • 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.
Well suited to process data streams, especially if collected via other IBM services such as IBM IoT.
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March 30, 2018

IBM Streaming Analytics: "Streaming hot IBM review."

Score 7 out of 10
Vetted Review
Verified User
Review Source
Clusterization and compaction of customer data; used mainly by the department responsible for data preprocessing before further analysis.
  • Analyzing geospatial data
  • Online application support
  • Application hosting and management
  • Documentation can be improved
  • Integration of the foreign languages could be handier.
Well suited for distributed development and streaming data analysis if the tools provided are sufficient for the need of the developers.

Less appropriate - analysis of static data, complex analysis that is not covered by IBM tools, and security-sensitive data analysis.
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IBM Streaming Analytics Scorecard Summary

About IBM Streaming Analytics

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, analyze, and correlate information as it arrives from different types of data sources in real time. When you create an instance of the Streaming Analytics service, you get your own instance of IBM Streams running in IBM Cloud, ready to run your IBM Streams applications.

Visit our Docs pages for pricing and support information.

IBM Streaming Analytics Supported Products

IBM Watson IoT, IBM Cloudant, IBM Event Streams (formerly Message Hub)

IBM Streaming Analytics Competitors

Azure Stream Analytics, Amazon Kinesis, Spark Streaming, Storm

IBM Streaming Analytics Availability

Geography:United States, United Kingdom, Australia, Germany
Supported Languages: English, French, German, Italian, Japanese, Korean, Portugese/Brazil, Spanish, Chinese simplified, Chinese traditional