IBM Analytics Engine vs. Kibana

• Well suited for my big data related project or a static data set analysis especially for uploading huge dataset to the cluster.
• But had some issues with connecting IoT real-time data and feeding to Power BI. It might be my understanding please take it as a mere comment rather than a suggestion.

Incentivized

Kibana is indeed a powerful tool and has many use cases especially in environments that rely heavily on real-time log analysis and visualisation. Kibana’s ability to handle large volumes of log data and present it in an accessible, searchable format is invaluable. We use Kibana to monitor security related issues and it proactively alerts our Slack channels about any anomality or issues.

Incentivized

• Jobs with Spark, Hadoop, or Hive queries are rapidly attained
• Can collect, organize and analyze your data accurately
• You can customize, for example, Spark or Hadoop configuration settings, or Python, R, Scala, or Java libraries.

Incentivized

• Fast searches with powerful index.
• Beautiful data visualizations.
• Real-time observability.

Incentivized

• Easier pricing and plug-and-play like you see with AWS and Azure, it would be nice from a budgeting and billing standpoint, as well as better support for the administration.
• Bundling of the Cloud Object Storage should be included with the Analytics Engine.
• The inability to add your own Hadoop stack components has made some transfers a little more complex.

Incentivized

• Some performance issues with large datasets.
• Linking to dashboards makes extremely long urls.
• Lack of reports.

Incentivized

Its usability is generally good and it provides teams with a basic to intermediate understanding about data visualization. It is very user-friendly when it comes to creating dashboards. The UI is very good and simple. Its integration with other tools for alerting and reporting is amazing. But its advance features have a learning curve and a first timer needs some time to use the advance features.

Incentivized

We did not use the official Kibana support. Documentation was easy enough to follow.

Incentivized

We initially wanted to go with Google BigQuery, mainly for the name recognition. However, the pricing and support structure led us to seek alternatives, which pointed us to IBM. Apache Spark was also in the running, but here IBM's domination in the industry made the choice a no-brainer. As previously stated, the support received was not quite what we expected, but was adequate.

Incentivized

Kibana is free; it was the first and only thing we've tried in this area.

Incentivized

• This product has allowed us to gather analytics data across multiple platforms so we can view and analyze the data from different workflows, all in one place.
• IBM Analytics has allowed us to scale on demand which allows us to capture more and more data, thus increasing our ROI.
• The convenience of the ability to access and administer the product via multiple interfaces has allowed our administrators to ensure that the application is making a positive ROI for our business users and partners.

Incentivized

• Issues that affect checkout experiences for customers are able to be prioritized and solved quickly.
• We are able to more efficiently use resources due to the automation of reporting alerts. Decreasing employee resources needed.
• Visualization allows us to quickly share issues and explain to coworkers in order to escalate issues that can cost our bottom line.

Incentivized