Apache Hive vs. Cloudera Manager

Apache Hive shines for ad-hoc analysis and plugging into BI tools. Its SQL-like syntax allows for ease of use not for only for engineers but also for data analysts. Through our experience, there are probably more desirable tools to use if you are planning on integrating Hive into your processing pipeline.

Incentivized

Cloudera Manager is well suited for environments and deployments where the administrator user base is not well versed in the Apache Hadoop ecosystem or the Linux command line interface.

Incentivized

• Hive syntax is almost like SQL, so for someone already familiar with SQL it takes almost no effort to pick up Hive.
• To be able to run map reduce jobs using json parsing and generate dynamic partitions in parquet file format.
• Simplifies your experience with Hadoop especially for non-technical/coding partners.

Incentivized

• Cloudera Manager has an easy to use web GUI. You can start and stop cluster and services. It will start and stop services in a cluster in the right order. You can monitor the cluster, services, and physical host hardware as well.
• Cloudera Manager has an easy to use API that allows us to create scripts to automate deployment process.
• Cloudera Manager has an option to add additional services that you could manage via the web GUI.

Incentivized

• Use Hive for analytical work loads. Write once and read many scenarios. Do not prefer updates and deletes.
• Behind scenes Hive creates map reduce jobs. Hive performance is slow compared to Apache Spark.
• Map reduce writes the intermediate outputs to dial whereas Spark operates in in-memory and uses DAG.

Incentivized

• Support for third-party Python APIs within Cloudera Manager extension framework
• Providing more reporting/logging functionality as part of the open source distribution
• Support for the latest RHEL versions sooner in the release lifecycle

Incentivized

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

It meets all my customer's needs.

Incentivized

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 have used a simple but necessary function such as merging certain data tables, which although they may be from different areas, complement each other or are necessary, you can use metadata if what you need is to validate the origin of your information and what impact it has, is also feasible.

Incentivized

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.

Incentivized

• Good ROI for being able to access data easily across the network, we have large amounts of data and this is a good system to access it
• Good ROI for being easy to learn how to use for new employees, not much time spent which saves costs
• Good ROI for being able to integrate with Spark and other applications, hence data can be analyzed through programs

Incentivized

• 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.

Incentivized