Apache Hive

Apache Hive

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Score 8.2 out of 100
Apache Hive

Overview

Recent Reviews

Help your dev team !

8 out of 10
April 12, 2022
We build our data lake and perform queries on large amounts of data. We group data from multiple sources into a common structure, making …
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Capabilities of Apache Hive

8 out of 10
April 07, 2022
Main purpose for using Apache Hive was to get the insights from data. Analyzing the data and use it to take informed business decisions. …
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very useful for OLTP

10 out of 10
April 06, 2022
We use Apache to process large data and get the output with less process time. The framework is very much useful for data processing and …
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Big Data the SQL way

8 out of 10
September 23, 2020
I am working as a Research Assistant where I have to process tons of data to produce appropriate findings. Our NLP lab used it for all its …
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What is Apache Hive?

Apache Hive is database/data warehouse software that supports data querying and analysis of large datasets stored in the Hadoop distributed file system (HDFS) and other compatible systems, and is distributed under an open source license.

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Product Details

What is Apache Hive?

Apache Hive is database/data warehouse software that supports data querying and analysis of large datasets stored in the Hadoop distributed file system (HDFS) and other compatible systems, and is distributed under an open source license.

Apache Hive Technical Details

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Frequently Asked Questions

What is Apache Hive?

Apache Hive is database/data warehouse software that supports data querying and analysis of large datasets stored in the Hadoop distributed file system (HDFS) and other compatible systems, and is distributed under an open source license.

What is Apache Hive's best feature?

Reviewers rate Usability highest, with a score of 8.7.

Who uses Apache Hive?

The most common users of Apache Hive are from Enterprises (1,001+ employees) and the Computer Software industry.

Reviews and Ratings

 (100)

Ratings

Reviews

(1-25 of 36)
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Score 9 out of 10
Vetted Review
Verified User
Review Source
  • Reduce-based query language with a simple query language.
  • Parallelism across a distributed system is provided.
  • All cloud platforms have access to a tabular format and interfaces.
  • Due to the shuffled data, complex joins may take a long time to complete.
  • Execution is dependent on external storage and memory.
Score 9 out of 10
Vetted Review
Verified User
Review Source
  • Easy-to-use, interactive modern layout
  • Easy to organize data and view tables and views from across the organization
  • Fast speed for most queries
  • Some queries, particularly complex joins, are still quite slow and can take hours
  • Previous jobs and queries are not stored sometimes
  • Switching to Impala can sometimes be time-consuming (i.e. the system hangs, or is slow to respond).
  • Sometimes, directories and tables don't load properly which causes confusion
Camilo Palacios | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source
  • Please provide some detailed examples of things that Apache Hive does particularly well.
  • Migration to the cloud is modern and very secure.
  • The best way to do this is to schedule the extraction at times established by hours and quantities.
  • So that it can be used normally in daily use, it must be taken into account that the maintenance management of the system so that it works effectively.
Pablo Gonzalez | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source
  • The unification of the data will help to establish the commercial criteria.
  • We are sure that the data is protected
  • If you try to extract an excessive amount of data, the system will become slow
  • You may have the danger that the system collapses due to the amount of data
Score 8 out of 10
Vetted Review
Verified User
Review Source
  • It can be used to retrieve data from database like SQL.
  • We can partition the data and distribute amongst the clustered machines
  • Easily scalable, which gives capability of running analytics at a larger level
  • No support for working with Unstructured data.
  • ACID properties are not followed like database which creates confusion many times
  • Support OLAP environment only, OLTP is not supported
Score 9 out of 10
Vetted Review
Verified User
Review Source
  • Apache Hive supports external data tables.
  • Supports data partitioning to improve overall performance.
  • Apache hive is reliable and scalable solution.
  • Apache Hive supports writing ad-hoc queries as well.
  • Apache hive is not best suited for OLTP based jobs.
  • Sometimes we observed high latency rate while querying data.
  • Limitations on providing row-level data update.
  • Training materials needs improvements.
April 06, 2022

very useful for OLTP

Score 10 out of 10
Vetted Review
Verified User
Review Source
  • Used in data warehouse like similar to ETL tools.
  • Interface like SQL give data stored in various db group.
  • Enables analytics at massive scale.
  • Way of framework development can be improved.
  • OLTP is not supported.
  • Does not offer real time queries.
akshay kashyap | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
  • Simple query language built on top of Ma reduce paradigm.
  • Provides parallel execution over distributed system.
  • Tabular format and connectors available for all cloud platforms.
  • Complex joins may take time to execute due to shuffling of data.
  • Static queries mostly.
  • Slower than Apache Spark by almost 100 times.
  • Dependent on external memory and storage to execute.
Manjeet Singh | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
  • It is very easy to set up and start with
  • Apache Hive is a cheaper solution for data warehousing and aggregation compared to other products
  • One of the cons is the speed which is slightly lesser as compare to other enterprise solutions like BigQuery
  • Also, It needs to be maintained by the company itself
Score 9 out of 10
Vetted Review
Verified User
Review Source
  • Gives access to files stored in a variety of data storage systems
  • Facilitates ETL operations, reporting and data analysis
  • Supports queries expressed in a declarative language very similar to SQL
  • Not suitable for for online transaction processing workloads
  • Much more complicated than any typical RDBMS
  • Licensing model based on Apache License 2.0
September 23, 2020

Big Data the SQL way

Score 8 out of 10
Vetted Review
Verified User
Review Source
  • The SQL-like query language is very familiar to all the CS students. Hence, it's easy to use.
  • I used it on a server so I realize it is very scalable and can be used to process small and big datasets.
  • I particularly liked the UDF functionality where the user could define functions to produce particular output.
  • Transactions are not supported
  • Lack of subqueries made some tasks achievable only when completing one query and then the subsequent one
  • It is not as fast as spark.
Score 8 out of 10
Vetted Review
Verified User
Review Source
  • The SQL, like query interface, is the core value and shining core of the Hive.
  • It supports various data formats stored and also allows indexing.
  • It is fast.
  • No transaction support.
  • No sub-query support.
  • Can only deal with the cold data (non-real time).
Ananth Gouri | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
  • The capability to handle large amounts of data and its querying process.
  • A syntax similar to SQL is an added advantage.
  • An active developer support and community always ready to help.
  • Ease of usage.
  • Resource consuming sometimes. May be that I was using a larger object file.
  • Needs to add an update or a modify functionality. This has to be the minimilastic CRUD requirement.
August 29, 2018

My Apache Hive Review

Kartik Chavan | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source
  • Querying in Apache Hive is very simple because it is very similar to SQL.
  • Hive produces good ad hoc queries required for data analysis.
  • Another advantage of Hive is that it is scalable.
  • Apache Hive isn't designed for and doesn't support online processing of data.
  • Sub queries not supported.
  • Updating the data can be a problematic task.
Score 9 out of 10
Vetted Review
Verified User
Review Source
  • It's Fast!
  • You can store a different kind of data structures here other than the standard ones
  • Good scalability
  • Good redundancy too
  • It's not as ACID compliant as an RDBMS. It's a recently added feature and still needs work.
  • This is not the tool to go for online data processing.
  • It does not support sub-queries.
  • It can't process data in real time.
Jordan Moore | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
Review Source
  • One of the standard SQL on Hadoop implementations. Comes installed in both HDP and CDH Hadoop distributions.
  • Hive Live Long and Process has made recent significant improvement on long-running queries.
  • Allows BI tools to run analysis over Hadoop data.
  • Allows various relational databases for its metastore. These include MySQL, Postgres, Derby, or Oracle.
  • Needs to keep up with execution engine improvements. Spark or Tez on Hive, then LLAP are good starts.
  • Overall speed of ad-hoc querying could be improved.
Tejaswar Rao | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
  • Can query on large sets of data and fast when compared to RDBMS
  • Can use SQL for data access and no need to learn new language
  • Can write custom functions (UDF) with python and also Java
  • Security roles for different users should be implemented
  • All the functionalities of SQL should be available