IBM InfoSphere BigInsights [EOL] Reviews

InfoSphere Streams was the one core product from the platform in which we were using. We were building a real-time response system and we built it on InfoSphere Streams.

The solution we built using InfoSphere was mainly aiming to: 

• Collect telematics data from vehicle engines.
• Do root cause analysis (RCA) on any engine failure. 
• Give recommendations to the customer and the service center, in real time.

This helped us to serve our customers better by giving real-time suggestions and proactive maintenance. Also, our engineering team was able to figure out frequent faults and incorporate design changes to avoid them in future.

The things that I have found of value are those that make database management easier to deal with. I have been using Netezza and Oracle DBA for several years so the big data is a bit of a mind shift for me. The thing that I have found most valuable in this solution is the BIQSQL implementation which is fully SQL ANSI compliant. No retraining is required on the SQL front.

The customer that I am working with has had a portion of the database that they will be shifting from Netezza to BigInsights. This will remove all of the queries and overhead on Netezza around those tables. This will be a big improvement and will allow the organization to deal with backup/recovery better as it decreases the size of the Netezza database.

We evaluated primarily the Text analytics module for social media analysis.

This feature has been looked at for social media analysis esp sentiment analysis by the marketing groups trying to understand product/service feedback on social media

The enterprise-ready technologies and capabilities for text analytic and big data analytic. The easy way to deploy big data solutions into production is a very desirable feature of this product.

I led a project in which my company deployed a big data solution in less than four month with a Hadoop cluster of more than 100 datanodes and 3 namenodes to perform big data analytic from datasets extracted of several data sources such as social networks, RFID sensors and other type of sensors.

Watson is the perfect engine for text analysis for us, but in 2014 it doesn’t support the Russian language.

We didn’t use these products in production environment. For test only.

It integrates with JSqsh, enabling us to submit long-running exports from the shell. Also, the automated user acceptance testing suite is extremely useful as we can follow through the state of UAT process.

It enabled us to process low-level detailed transactional data that previously was not processed.

BigSQL – implementation of DB2 Database Partitioning Feature on HDFS cluster – in conjunction with IBM Fluid Query for Netezza.

It gives us the option of extending our analytics system. Now, a customer can move part of their data from Netezza into a Hadoop cluster. What's more, they can run algorithms directly on HDFS using R.