Apache Hive vs. Cloudera Distribution Hadoop (CDH)

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

Cloudera Distribution Hadoop (CDH) does a lot of things really well - especially on the analytical front. That being said the product is quite expensive. There are seemingly numerous applications that do the same thing on the functional level that are much more cost effecient for enterprise teams. If I were recommending this to a colleague I would let them know the product will absolutely be able to get the job done for their use case, but there are more efficient options

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

• Solid and robust set of integrations
• Easy to use and easy to deploy across the enterprise
• Reliability - never lost any info
• Simple and clean interface

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

• The price is quite high competitively speaking
• Hard to learn more robust functions and custom options without experience

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

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

In terms of functionality there's not much difference, both get the job done. Amazon was more cost-efficient for our team, but this could vary depending on the size of the business. One thing I did notice was that Cloudera seemed to management and spit out our deployments faster than AWS.

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

• Saves time by automating typically manual processes (data management, lifecyle AI etc)
• Quick deployments and analytics allow for faster time-to-value

Incentivized