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https://media.trustradius.com/product-logos/Uv/Xp/77N37PEPH17Z-180x180.PNGIBM: Its Future is GrowingI am currently working as a Data Analyst for ATG travel group. We are currently moving our database to IBM Cloud. I am using Streaming Analytics to pull the data from the cloud and manipulating the data to answer research questions. Streaming Analytics will also be able to help my company to make dashboards from the data that I will be using.,User friendly Works pretty quick Shows how data are transported,User interface. Takes time to get used to.,8,Makes it quick Saves my company a lot of money,,IBM Watson Analytics, IBM CognosNice streaming analytics toolIt is being used by more than one department. Able to look at streaming data and enrich information and store information. It helps with the problem of looking at live data and being able to filter and make calls. Like the ability to be able to read from various sources and be able to write to many as well.,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,8,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,Hortonworks Data Platform,IBM Operational Decision Manager, IBM Db2 Warehouse, Hortonworks Data PlatformStreaming Live analysisIBM Streaming Analytics is being used to analyze real time data. This is limited to the IT Department in analyses of logs and problem determination, analysis and resolution. Error logs are redirected to a stream of text DISK_ERRx (X, can be 1, 2, 3, or 4). Errors are either temporary or permanent. We are able to address the problem of determining and grouping errors into classes.,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,9,Ability to do more with less Admins and data analyst can now focus on more thinking tasks No negative impacts yet,Domino, IBM Spectrum Protect, IBM Spectrum Protect PlusUnique advantages for real time use casesStreams was considered for SENS IoT platform for analytics of real time data from mobile phones, WiFi access points and devices in Sensitel SENS platform. We used the on premise version and evaluated the cloud version as well.,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,8,Awesome product for some really difficult use cases Reduces storage requirements Makes writing rules easy,,Hadoop, Watson Studio (formerly IBM Data Science Experience), JIRA SoftwareStream 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
58 Ratings
Score 7.3 out of 101
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IBM Streaming Analytics Reviews

IBM Streaming Analytics
58 Ratings
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Score 7.3 out of 101
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John Nguyen profile photo
February 14, 2019

IBM Streaming Analytics Review: "IBM: Its Future is Growing"

Score 8 out of 10
Vetted Review
Verified User
Review Source
I am currently working as a Data Analyst for ATG travel group. We are currently moving our database to IBM Cloud. I am using Streaming Analytics to pull the data from the cloud and manipulating the data to answer research questions. Streaming Analytics will also be able to help my company to make dashboards from the data that I will be using.
  • User friendly
  • Works pretty quick
  • Shows how data are transported
  • User interface. Takes time to get used to.
IBM Streaming Analytics is great for big data management.
Read John Nguyen's full review
No photo available
February 15, 2019

IBM Streaming Analytics Review: "Nice streaming analytics tool"

Score 8 out of 10
Vetted Review
Verified User
Review Source
It is being used by more than one department. Able to look at streaming data and enrich information and store information. It helps with the problem of looking at live data and being able to filter and make calls. Like the ability to be able to read from various sources and be able to write to many as well.
  • 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
Like the name says, it is good for streaming data and analyzing. It is great to look at tuples at a fast rate, filtering, calling other sources to enrich data, can call APIs, etc. Could do better for ingest use cases, can do better with guaranteed delivery, etc.
Read this authenticated review
No photo available
February 14, 2019

IBM Streaming Analytics Review: "Streaming Live analysis"

Score 9 out of 10
Vetted Review
Verified User
Review Source
IBM Streaming Analytics is being used to analyze real time data. This is limited to the IT Department in analyses of logs and problem determination, analysis and resolution. Error logs are redirected to a stream of text DISK_ERRx (X, can be 1, 2, 3, or 4). Errors are either temporary or permanent. We are able to address the problem of determining and grouping errors into classes.
  • 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
IBM Streaming Analytics is well suited in cases where you have raw live data, the need to check data and react based on selected or identified metrics in a way to either prevent or take appropriate action in the way you would in a workflow process approver, reviewer scenario. Less appropriate for unrelated, random non patterned data
Read this authenticated review
No photo available
February 14, 2019

IBM Streaming Analytics Review: "Unique advantages for real time use cases"

Score 8 out of 10
Vetted Review
Verified User
Review Source
Streams was considered for SENS IoT platform for analytics of real time data from mobile phones, WiFi access points and devices in Sensitel SENS platform. We used the on premise version and evaluated the cloud version as well.
  • 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
Well suited for:
Healthcare emergency room acute condition onset detection

Machine failure and anomaly detection
Read this authenticated review
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
No photo available
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.
Read this authenticated review
No photo available
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.
Read this authenticated review

IBM Streaming Analytics Scorecard Summary

Feature Scorecard Summary

Real-Time Data Analysis (4)
7.3
Visualization Dashboards (4)
6.6
Data Ingestion from Multiple Data Sources (4)
6.8
Low Latency (3)
7.9
Integrated Development Tools (3)
5.8
Data wrangling and preparation (3)
6.1
Linear Scale-Out (2)
7.7
Machine Learning Automation (4)
4.3
Data Enrichment (3)
6.7

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