Apache Hive vs. Kibana

Software work execution is on a large scale, it is good to use for new projects or organizational changes, data lineage mapping has always been dubious but this one has had good results. You can store and synchronize data from different departments, the storage process can be manual but it is best automated.

Incentivized

Kibana integrates seamlessly with Elastic Search which gives us access to parse and analyze data generated from our systems in order to make decisions. Also, Kibana helps us create insightful reports and dashboards that give us insights into the end-users usage on the system and helps us find the root cause of issues as well.

Incentivized

• Apache Hive allows use to write expressive solutions to complex problems thanks to its SQL-like syntax.
• Relatively easy to set up and start using.
• Very little ramp-up to start using the actual product, documentation is very thorough, there is an active community, and the code base is constantly being improved.

Incentivized

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

Incentivized

• Some queries, particularly complex joins, are still quite slow and can take hours
• Previous jobs and queries are not stored sometimes
• Switching to Impala can sometimes be time-consuming (i.e. the system hangs, or is slow to respond).
• Sometimes, directories and tables don't load properly which causes confusion

Incentivized

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

Incentivized

Since I do not know the second data warehouse solution that integrate with HDFS as well as Hive.

Hive is a very good big data analysis and ad-hoc query platform, which supports scaling also. The BI processes can be easily integrated with Hadoop via the Hive. It can deal with a much larger data set that traditional RDBMS can not. It is a "must-have" component of the big data domain.

Incentivized

Apache Hive is a FOSS project and its open source. We need not definitely comment on anything about the support of open source and its developer community. But, it has got tremendous developer support, awesome documentation. I would justify the fact that much support can be gathered from the community backup.

Incentivized

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

Incentivized

Besides Hive, I have used Google BigQuery, which is costly but have very high computation speed. Amazon Redshift is the another product, I used in my recent organisation. Both Redshift and BigQuery are managed solution whereas Hive needs to be managed

Incentivized

Kibana has a better usability experience, the core features I was using existed in all of them. I liked more in Kibana how you can easily create dashboards, charts, and reports without the need to be a tech person.

Incentivized

• Apache hive is secured and scalable solution that helps in increasing the overall organization productivity.
• Apache hive can handle and process large amount of data in a sufficient time manner.
• It simplifies writing SQL queries, hence helping the organization as most companies use SQL for all query jobs.

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