Apache Hive vs. Kognitio

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

What I like most is the IN Memory capability it is doing, as we all know RAM is faster than disk. The capability to connect different databases. Beginners should also take note of the Data Disk Management because anytime it could go wrong, you should have experience in dealing with this kind of event.

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

• Ultra fast query results.
• IN Memory Database.
• Easy integration to reporting services.

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

• Problems Could Be Encountered is particularly pronounced in more complex analyses.
• Categorical variables are often not precise enough

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 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

The understandable and complete tables and graphs, the cleaning methods and the way of encrypting the data are quite feasible, which does not help to prepare our data, it helps that the data that is thrown as results is separated from each other, the process prior to structuring requires high-level advice and is somewhat time-consuming, there is a risk that they overwrite the data themselves by accident at a later time

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

• The implementation of the formats to integrate the users we have and the program is also good.
• I also improve the control of aspects related to the work environment

Incentivized