Hive is one of the Apache projects. it is a data warehouse software which facilitates querying and managing large datasets residing in distributed storage. It provides a way to enable easy data extract/transform/load (ETL)

Some of the nice features include (1) a simple SQL-like query language, called HiveQL, that enables users familiar with SQL to query the data. But it is a bit different from SQL standard. For example, HiveQL can also be extended with custom scalar functions (UDF's), aggregations (UDAF's), and table functions (UDTF's). (2) You can define your own read or written data format called Hive SerDe. (3) Hive can be run on Hadoop and HDFS. It has very good scalability.

Personally speaking, I use hive mainly for ad hoc quires and reports. For BI reports Hive is the best since you can reuse all the SQL that you have done for traditional data warehouses. Also with Hive Server2 you get a real JDBC support so you can plug your BI tools to it. Many more SQL features like cubes, rollups, windowing, lag, lead, etc are being added to Hive through Hortonworks Stinger initiative. Hive also produces very compact code, which is always good for reading and debugging. Review collected by and hosted on G2.com.

Too large code base. It is hard to maintain and support. And, there are too many configurations. If you take a look at the HiveConf.java, you will be confused with so many configurations there. It is easy to get lost when you configure them. And, if you configure some of them in a wrong way, you may suffer from bad query performance. Review collected by and hosted on G2.com.

It is very simple to use because you fill like you use simple SQL language for querying data.

When I just started I didn't have any experience with Hive and in like one week I was able to query big data and do some analysis. In a month I was able to administrate data and create my own databases with the useful data. . . Review collected by and hosted on G2.com.

Not so many implemented functions in the Hive. There are very useful Window functions but it's not enough. . .

It's not that simple to modify data inside a table. . . Review collected by and hosted on G2.com.

- Easy to learn

- Can query complex data including nested structures.

- Flexible (wrt data schema)

- With ORC SerDe, I/O can be reduced drastically - by reading only what is required (columnar formats). Review collected by and hosted on G2.com.

- Needs schema to be defined in prior.

- Not ANSI SQL compliant.

- Not suitable for fast interactive queries, even on moderate size datasets.

- Works only with Hadoop (not an independent query-processing tool)

- Not enterprise grade w.r.t quality of documentation, error messages, support Review collected by and hosted on G2.com.

1) ability to handle PetaByte scale of data

2) ability to work with near SQL query language

3) schema on read capibility Review collected by and hosted on G2.com.

1) optimizer technology is still maturing Review collected by and hosted on G2.com.

If you know how to write sql statements you can write hiveql and it doesn't require you to learn anything new,its pretty straightforward Review collected by and hosted on G2.com.

Performance tuning is difficult and becomes hard for complex queries, it still has a few bugs like all the data going to single reducer, which might lead to slow down the query results. Review collected by and hosted on G2.com.

Easy SQL like syntax for very short and simple queries Review collected by and hosted on G2.com.

No alias for relation. No flow controls as well. Review collected by and hosted on G2.com.

The best part about hive is that it easy to master because of its SQL like interfacing. Also very handy tool for ETL and DW functions. Review collected by and hosted on G2.com.

Not too good with setting up optimization parameters. Need to remember a lot of console settings.

Index doesnt turn out to be very useful. CRUD functions have many pre-requisites like the table must be bucketed, etc. Review collected by and hosted on G2.com.

Its very user friendly. Easy to install and use. I like the interface very much . Review collected by and hosted on G2.com.

Nothing I can think of. I had a great experience working on HIVE and was satisfied with all the features as it met all my requirements. Review collected by and hosted on G2.com.

The ability to view HDFS data in a relational format and easily query it through HiveQL Review collected by and hosted on G2.com.

The fact that it uses MapReduce whether you query a pre existing table or a perform a complex query. Tez helps with this issue. Also the inability to delete/update data is a real issue and forces other services to be used eg HBase. Review collected by and hosted on G2.com.

performing SQL-like queries, Partitioning Tables, De-normalizing data, Compress map/reduce output are best benefits Review collected by and hosted on G2.com.

For some cases you cannot do complicated operations using Hive e.g. when output of one job acts as input to the other job (SequenceFileFormat file) or writing query on an image file, Hive is not useful. Review collected by and hosted on G2.com.