Hive Reviews & Product Details

Leverage sql skills to perform operations on data stored in hadoop.

Works on map reduce algorithm, so the retrieval of data is a little slow.

Allowed business users to query data using sql skills.

The best thing about HIVE is that anyone that is familiar with SQL can take advantage of HIVE's ability to run map reduce jobs. Newer version of HIVE is getting better at supporting windowing functions and fleshing out any inconsistencies. So far the documentation is good enough for getting me through my tasks and there is still on-going support for this product, which is a pretty good sign to me.

Older versions of HIVE sucks. There are lots of limitations that will force you to write HiveQL queries that are not straight forward and, even potentially, inefficient. For example, no support for window functions and no equality comparisons on joins can make your life very difficult so you will need to fall back to using some whacky full joins or self joins to accomplish the same task.

We are using HIVE as a data warehouse. One of the benefits of HIVE is that it can break your SQL queries into a series of map reduce jobs, so its supposed to speed up your queries if given enough compute nodes.

Hive is the best out there for answering ad-hoc queries in parallel paradigm. It works very well with Hadoop Echo system (mainly integrates perfectly with HDFS). - Easy to use as it implements most of SQL functions.

- Needs more optimization for complex queries (like caching, auto-partitioning,etc ...) to speed up the latency of the queries. - Tuning the hive parameters is really challenging for the users. The default settings don't work with the large queries. - Hive is perfect if 90-95% of the queries are read-only. It is not suitable for applications with heavily updates

Get quick insights from big data in case of the customers' data don't fit on one machine. It helps a lot for data preparation (i.e. creating temporary tables), that can be consumed by other machine learning solutions like Spark to build machine learning models that add more business values.

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.

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.

data extracting, processing and analysis. It's fast.

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.

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.

We are using Hive for storing logs, of data, being generated, in our business. Further we will be using these logs for reconciliation purpose, helping in keeping a track of data.

Stable product; Easy to use; Multiple computation engines - Tez, MR; Almost all SQL capabilities;

Delete support is still not there even though they are nearly there.

Primary Querying engine for Data Analytics

Provides quick results based on a hadoop database, easy to use interface with simple set up steps

Some quirks with HiveQL may require referencing the documentation, but there is a lot of similarity with other SQL based languages.

Data analytics, making vast amounts of data available for general BI uses

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.

As the open source software it has common issues with support. Also Hive doesn't support many features that traditional SQL has.

The main purpouse of using Hive is to building reports and do analysis of data that is stored in the Hadoop file system. As for now it is the only one framework that can be used by all most popular BI tools to read the data from the HDFS.

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.

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.

Storing large amount of data that could not fit in to any relational database system. Being able to derive valuable insight into our data by running mapreduce jobs on data stored in Hive.

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.

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.

we are trying to create a standard ETL pipelining tool that would support standard BI/Reporting utilities.