Apache Hive vs. Cloudera Manager

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.

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It would be suited for customers who feel more comfortable with using a GUI. It is less appropriate for developers or engineers who are comfortable with command line

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• 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.

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• Graphical user interface
• Management of third party applications start/stop/restart functionality through framework
• Support of Apache Hadoop ecosystem
• Ability to do "rolling restarts"

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• 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

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• Cloudera Manager needs to be more agile with integrating other applications, such as Accumulo 1.7, to their software.
• Cloudera Manager can do a better job at explaining why a node fails to add to a cluster using their assistant.
• Cloudera Manager should show graphs only when there is data, instead of showing just an empty box.

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

It is a well developed product with a good user interface.

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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.

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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.

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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

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I have not used any competitors, such as Hortonworks, because Cloudera Manager just works and meets all my customer's needs. I only have deployed Hadoop using command line, which is not easy to use and manage.

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• 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.

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• Cloudera Manager has allowed our organization to deploy Apache Hadoop to operations quicker and with less training versus using the command line exclusively.
• Increased employee efficiency.
• Increased product adoption.

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