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58 Ratings
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Score 7.3 out of 101

Likelihood to Recommend

IBM Streaming Analytics

IBM Streaming Analytics is great for big data management.
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Feature Rating Comparison

Streaming Analytics

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

Pros

IBM Streaming Analytics

  • Query analysis of real time streaming data
  • Filter out events based on time windows
  • Scalability for large scale data, production tested
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Cons

IBM Streaming Analytics

  • 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.
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Alternatives Considered

IBM Streaming Analytics

I have considered Apache Spark Streaming and Apache Flink. Spark Streaming is still changing too often for my taste and does not seem as easy to connect to IoT data especially for students having limited experience with cloud computing. Interesting signal processing functions require stateful computations, which I find is not so well documented in Spark Streaming. Flink does not have a Python API, which would be useful for an introduction to a course.
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Return on Investment

IBM Streaming Analytics

  • Awesome product for some really difficult use cases
  • Reduces storage requirements
  • Makes writing rules easy
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Pricing Details

IBM Streaming Analytics

General

Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Cost:
Additional Pricing Details

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