Hive Pros and Cons

For all its processing power, Pig requires programmers to learn something on top of SQL. It requires learning and mastering something new. Hive statements are remarkably similar to SQL and despite the limitations of Hive Query Language (HQL) in terms of the commands that it understands, it is still very useful. Hive provides an excellent open source implementation of MapReduce. It works well when it comes to processing data stored in a distributed manner, unlike SQL which requires strict adherence to schemas while storing data. Review collected by and hosted on G2.com.

Despite the working differences, once you enter the Hive world from SQL, similarity in language ensures smooth transition but it is important to note the differences in constructs and syntax, else you’re in for frustrating times. Review collected by and hosted on G2.com.

Hive provides an ease to the user who wants to store bulk data, in a tabular manner.

It works on the same queries like SQL, making it easy for using the traditional database system.

Because of this reason, people need not have to study some new language and can still adapt to the Big Data Culture.

Also it has features like partition, and bucketing, helping in segregation of data.

Data can directly be loaded into hive, by HDFS, using the CSV files of the same format, or from Hbase by making a pointer to the Hbase table, providing a link within Hadoop. Review collected by and hosted on G2.com.

For small amount of data also, it runs map reduce job, which consumes some time, and thus is not efficient for the same.

We do not have a concept of primary key in Hive, so we can have redundant entries.

Also till the older version, update and delete were not possible, and now also in the new version, if we want to use the update and delete commands, the performance of the tool gets degraded. Review collected by and hosted on G2.com.

Stable product; Easy to use; Multiple computation engines - Tez, MR; Almost all SQL capabilities; Review collected by and hosted on G2.com.

Delete support is still not there even though they are nearly there. Review collected by and hosted on G2.com.

Provides quick results based on a hadoop database, easy to use interface with simple set up steps Review collected by and hosted on G2.com.

Some quirks with HiveQL may require referencing the documentation, but there is a lot of similarity with other SQL based languages. Review collected by and hosted on G2.com.

The Hive is intended to simplify your experience with Hadoop and allows developers and business analyst apply their SQL knowledge to query data, build reports, build etl etc. Review collected by and hosted on G2.com.

As the open source software it has common issues with support. Also Hive doesn't support many features that traditional SQL has. Review collected by and hosted on G2.com.

To be able to run map reduce jobs using json parsing and generate dynamic partitions in parquet file format. Review collected by and hosted on G2.com.

It is slow compared to Spark/Impala for most operations. Also, it throws Out of Memory if multiple partitions are updated containing many parquet files. Review collected by and hosted on G2.com.

Hive syntax is almost like sql, so for someone already familiar with sql it takes almost no effort to pick up hive. But there are other tools that can do the same thing faster these days. Hive initially was really good to have; but more and more projects are now available to do SQL like operations on Big Data (like Drill). Review collected by and hosted on G2.com.

Hive is comparatively slower than its competitors. Its easy to use but that comes with the cost of processing, If you are using it just for batch processing then hive is well and fine. It also does not have as rich of a scripting language. Review collected by and hosted on G2.com.

The syntax of hive! Its almost SQL so its easy to use. External tables, partitions, buckets, UDFs all the features I like to use with hive. ORC data format occupying lesser space and retrieving the data much faster.

Learning curve looks easier as it is similar to SQL but hold on! you must learn all the features of hive before writing a big hql to join multiple hundreds GBs tables and fetch results. Otherwise if you write it like a regular SQL it may take hours to process. So hive is always at its best when you set the optimization parameters before you run your scripts. Also its complex datatypes make hive more useful than other RDBMS. Review collected by and hosted on G2.com.

Hive is comparatively slower than its competitors. Its easy to use but that comes with the cost of processing, If you are using it just for batch processing then hive is well and fine. Review collected by and hosted on G2.com.

Hive syntax is almost exactly like sql, so for someone already familiar with sql it takes almost no effort to pick up hive. It can perform a wide variety of analyses over very large sets of data and requires very little tuning if you are willing to wait a while for the results. Review collected by and hosted on G2.com.

Hive can be a bit slow in comparison to other languages like Pig. It also does not have as rich of a scripting language. This is what makes it the second choice language for most data analysis jobs at LinkedIn. Review collected by and hosted on G2.com.

Ease of use as well as ability to scale. It has proven its reliability. They have continued to add more features and increased its speed at the same time. Review collected by and hosted on G2.com.

Speed is still slower compared to newer distributed warehouses. Also, it still uses mapreduce behind the scene which is very slow in the present days. Review collected by and hosted on G2.com.