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 …
Continue reading
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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

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.

Reviewers rate Usability highest, with a score of 8.7.

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

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Reviews and Ratings

 (100)

Ratings

Reviews

(1-25 of 36)
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Score 8 out of 10
Vetted Review
Verified User
Review Source
On-premises large data processing is handled by Apache Hive, which is running on Cloud ERA Servers. In order to use Apache Hive, you must have a distributed system that is query efficient and can perform queries quicker with parallel execution. Metrics like user information and purchase history are stored in HDFS and then accessed using queries built on top of Hive using Apache Hive.
  • 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.
Data warehouses that update and append records in batches or real time can be queried using Apache Hive. Tableau and other reporting tools may be used straight from Python searches on Apache data sets. Structured data and tables may be accessed using SQL-like syntax. Using a hive, you may build tables at various levels of the Data Lake. Transactional databases are not the best fit.
Prasanna Kumar TR | TrustRadius Reviewer
Score 6 out of 10
Vetted Review
Verified User
Review Source
We use Apache Hive to store a large set of data, which are huge documents such as problem statements and its answer, not only submitted by the site owners but also by the user of the site.
  • It is easy to store the data that are unstructured
  • Easy to retrieve using SQL queries instead of other complicated way
  • Large set of data can be stored efficiently
  • Apache Hive can provide more flexibility on the Integration.
Apache Hive wont is really useful when we just store small data sets. so sometimes our usage wont is suitable for Hive. we are planning to move to SQL Databases if it continues.
April 12, 2022

Help your dev team !

Score 8 out of 10
Vetted Review
Verified User
Review Source
We build our data lake and perform queries on large amounts of data. We group data from multiple sources into a common structure, making it easy for our developers to perform complex queries without leaving the simple framework provided by SQL. Although the deployment is not easy, once we have the infrastructure, the work is greatly simplified.
  • Simplify query to devs
  • Organize data
  • Batch process
  • Deploy
  • Maintenance
  • Support
It is great for laboratory environments and to start working with unstructured data about which we are not very clear about how we want to treat it. It also allows queries to be improved very quickly by allowing developers to work with SQL instead of map-reduce. As an improvement, in productive environments, troubleshooting is complicated and requires expert personnel.
Score 9 out of 10
Vetted Review
Verified User
Review Source
To manage and view Apache Hadoop data in a SQL-like format To be able to query databases across the organization, quickly To query data for the purpose of using on Spark projects To save queries
  • 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
Apache Hive is well-suited for querying Hadoop. If you use Hadoop you should consider Hive. It is well-suited for large organizations where there is lots of data that needs to be queried. However, there is significant overhead to set up and maintaining Hive (and Hadoop in general). Small companies and individuals should consider other means of storing data, such as SQL.
Camilo Palacios | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source
We have used the system to migrate data either for new versions or because we will use another operating program, the software helps us to synchronize programs between different operating systems, a history of information can be kept constant, and it can be sent to third parties the information already transformed.
  • 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.
Software work execution is on a large scale, it is good to use for new projects or organizational changes, data lineage mapping has always been dubious but this one has had good results. You can store and synchronize data from different departments, the storage process can be manual but it is best automated.
Omkar Marne | TrustRadius Reviewer
Score 6 out of 10
Vetted Review
Verified User
Review Source
I used Apache Hive on top of Hadoop for filtering and cleaning data using SQL. It was the part of the project which I was working on. Apache Hive gives SQL-like a platform where we can fire SQL queries. Apache Hive was a perfect choice for cleaning data as we were using Apache Hadoop and both are Apache products.
  • Filtering data
  • cleaning data
  • SQL like interface
  • Integrates with Hadoop
  • Uses lot of lot of memory
  • Not compatible with other databases like postgres, MySql
  • Limited support
  • Slow as compare o other interfaces
Apache Hive is best for ETL ( Extract Transform Load ) purposes. It gives its best performance when integrated with the Hadoop file distributed system. Its also very good for performing mathematical operations and when the data is organized and structured. It can handle large sizes of data ( petabytes) but requires a lot of in-memory in the system. It supports both unstructured and structured data nut best with structured data.
Pablo Gonzalez | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source
The software is intuitive from the first steps, one of the first features we take into account for the software does not allow duplicate files to be stored. It is advanced software that through data the system constantly learns and develops. The first phase is very effective, the analysis and checking of the information are verified in detail.
  • 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
In addition to the fact that the information is quickly accessible through the established security protocols, it has not helped us as users to maintain a fairly comfortable data processing flow, it is more profitable to process the data in batches, we have been able to unify data from different sources
Score 8 out of 10
Vetted Review
Verified User
Review Source
Main purpose for using Apache Hive was to get the insights from data. Analyzing the data and use it to take informed business decisions. Also the interface is similar to SQL working so it is easy to understand for a new person also.
  • 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
If you have workforce who are knowing SQL and you have a need to explore large-scale data and get insights from it then Apache Hive is perfect for you. If you have experienced people who have worked on big data earlier then using Splunk is better. For starting the journey in data-driven decisions and data analytics it is better to use Apache Hive first.
Score 9 out of 10
Vetted Review
Verified User
Review Source
Apache Hive is an open-source data warehouse solution built on top of Hadoop that helps to analyze a very large amount of data.
Our use case/scope is to work on a large data analytics project where the data frequency and velocity are very high. Apache Hive is very useful in processing both the unstructured and structured data in a seamless way. It help us in reducing to write complex queries as it is targeted to the SQL queries, we have a engineer team who are very proficient in writing SQL queries with the help of Apache Hive to process the big data.
We have identified no business issues using the solution.



  • 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.
Apache Hive is a data warehouse/ ETL solution that is being used for processing big data for analytics and visualizations. Apache Hive has great architecture that makes it very well suited for organizations.
The Metastore, is used for storing metadata for each table and its schema. The Driver operates as a controller for executions of the statements. Like other components such as Optimizer and CLI, Thrift Server are some components that enable the processing of big data transformation.

April 06, 2022

very useful for OLTP

Score 10 out of 10
Vetted Review
Verified User
Review Source
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 analytics purpose.
  • 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.
Keeps queries running very fast and takes very little time to write Hive queries in comparison to MapReduce code. Very easy to write queries including joins in Hive.
November 24, 2021

Apache Hive

Surendranatha Reddy Chappidi | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
1. Used Apache Hive to create external and internal tables in Hadoop / BigData projects on Cloudera and Azure platforms. 2. Apache Hive supports different file formats to create tables. Supported file formats are CSV, Parquet, Avro, JSON. 3. Apache Hive can store billions of records in distributed storage and retrieve them efficiently. 4. Apache hive used spark/ Tez / MapReduce engines in the backend for computation.
  • Apache Hive is fault-tolerant.
  • Apache Hive's latest version supports ACID transactions.
  • Apache Hive supports UPDATE, DELETE and MERGE.
  • Apache Hive should support ROLLBACK, COMMIT operations.
  • Apache Hive should support XML SerDe.
  • Apache Hive.
Well suited for: For accessing the structured data and tables using SQL-like syntax. A hive is a good option for creating tables in different layers of Data Lake. Not well suited for Transactional databases.
akshay kashyap | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
We are using Apache Hive over an on-premise big data setup built on top of Cloud ERA Servers. Use case behind using Apache Hive [it] is query efficient over distributed system and runs queries faster, with parallel execution. We save our metrics such as user info, purchase history, transaction and preferences in HDFS file system and use Apache Hive to query on top of it and run analytics to display output.
  • 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.
You can use Apache Hive to query over a large data warehouse which updates, append records on either batch or in real time. Apache queries can give you output in the desired format that you can use as any reporting tool such as Tableau, directly using Python.
Manjeet Singh | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
I have used Apache Hive in [the] last 3 companies and it's being used by the multiple departments spread across data analytics, engineering, data science and product management.
It's being used for fetching and generating all the product metrics, for fetching legal data whenever required. All the product history data is stored in it,
It's the one stop cheaper solution for storing and fetching all the analytics data
  • 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
It's fairly okay to set up and also cost is well within the pocket.
If our requirement of aggregation is within seconds for. Terabytes of data then we may have to lookup for other solutions
Score 9 out of 10
Vetted Review
Verified User
Review Source
We are using Apache Hive in our whole company as the main data warehouse software solution covering all needed data warehousing tasks. It is being used to interact with huge datasets located in a distributed storage. Since we are using a variety of data formats Apache Hive enables us to query anything with unified SQL syntax.
  • 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
Apache Hive fits perfectly if scalability, performance and fault-tolerance are essential for your data warehousing needs. If you are required to process batch jobs Apache Hive will keep your customers happy. On the other hand, if you are working with web logs data and append-only flat-file type of data, then there are better solutions on the market.
Since Apache Hive is an open source, community developed project the easiest way to get help is to reach out to issue tracker or mailing list. The response we got from there was always very quick and answers were usually relevant. Another great way to solve issues is an easily accessible version control system with well documented source code.
September 23, 2020

Big Data the SQL way

Score 8 out of 10
Vetted Review
Verified User
Review Source
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 big data processing, for example: removing urls, finding counts of specific words, etc. Mainly it assisted in all the processing, cleaning on big datasets we collected for our research.
  • 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.
Apache Hive is very well suited for those who are very familiar to SQL query syntax. Due to its easy to use syntax, it can really help in scenarios where a conventional database cannot be used for analysis of big datasets.

On the other hand, it's definitely slower than some other alternatives such as spark. Also, it's not recommended to use it in processing small datasets. Pandas and other normal data loading libraries can be useful to deal with small datasets.
While I used Hive a lot recently, I never faced issues that lead me to look for technical support. The documentation for developer reference is good enough, although I like the documentation for Spark much more. Since Hive follows SQL syntax, it's very easy to find references for queries online.
Kristjan Gannon | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
Review Source
We use Apache Hive to make data-driven decisions. It is used from finance to engineering to sales. It helps aggregate our massive data sets into distilled information.
  • Flexibility through schema on read
  • Familiar SQL like query language
  • Functions for complex queries and analysis
  • Slower processing than other tools on the market
Apache Hive is useful for regularly reporting and analyzing data. In terms of ad-hoc analysis and debugging, the cycles can be quite long for querying, feedback, debugging queries, etc.
Apache Hive is open-source, so there are a solid community and information around it for support.
Score 8 out of 10
Vetted Review
Verified User
Review Source
Hive plays a vital role in our company, together with Hadoop storage. It makes the query and aggregation much easier for old DBA background data analyst, while still benefiting a lot from the performance boost brought by Hadoop. It makes big data analysis more feasible and close to the daily business context.
  • 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).
Hive is suitable for big data analysis tasks on top of the historical data storage but is not quite suitable for any real-time data (if that is the case, Casandra should be considered). And as it is not real SQL, for a read-only operation and in-fly aggregation, it is very good, however, if data modification and transaction are needed, it is not suitable.
We take the advantage of the Apache community which provides a lot of value suggestions and support.
Ananth Gouri | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
As we all know that, Apache Hive sits on the top of Apache Hadoop and is basically used for data-related tasks - majorly at the higher abstraction level. I work as an Assitant Professor at NIE, Mysuru and I am a user of Apache Hive since the first time I taught Big Data Analytics as a PG Course to my students.
It was one of those technical sessions and I was supposed to demonstrate a word count program of a novel downloaded from the Project Gutenberg. I was successfully able to download the novel, load it into the Hadoop platform and execute a HiveQL (a SQL similar syntax used by Apache Hive) query to demonstrate for few unique words, their count, and related examples.
  • 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.
I would definitely recommend Apache Hive if sought by a colleague. Especially for people who are working at academic institutions, they can demonstrate programs like word count, tab count, space count, new lines count, and other related programs - with a basic setup of a HiveQL.

The only underlying problem could be that the Apache Hive is designed to run on the Apache Hadoop ecosystem. People who are not comfortable using a Linux tree structure based File System or even people who are not likely to use a Linux OS might not like to use Hive.
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.
Nicolas Hubert | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
It is only used in the IT department, mainly by IT engineers, Data Scientists, and Business Analysts with a technical background. It requires some time to master this tool, so this is only for engineer-related positions.
  • Reading databases
  • Writing databases
  • Storing databases
  • Distributed databases
  • Improvement techniques for handling Relational Data
  • Advanced optimizations
  • Transactions memory
Apache Hive acts as a hub for information to be stored and smoothly readable + analyzed by BI analysts in order to make wise and data-driven decisions. Users can read, write and manage data, too. This only requires some SQL intermediary knowledge, and we all know learning SQL is quite easy. I do not think of any scenario where Apache Hive would not be appropriate.
Documentation is so thorough and the community so huge that I never actually had to contact support for Apache Hive. I always found an answer to my questions on some forums. So I put a mark of 9 out of 10.
Score 7 out of 10
Vetted Review
Verified User
Review Source
Our company primarily uses Apache Hive to manage our data warehouse by being able to query multiple databases. We partition our tables as well as monitor query performance on very custom data queries by using this hive. Hive is only used by our data analysts and an overseas data warehouse team with only a few shared licenses existing on our virtual machines.
  • Monitor query performance
  • Manage tables in the data warehouse
  • Uses standard SQL
  • UI is quite dated and not intuitive
  • Open-source, so does not have consistent updates or support
  • Not the most optimal for ETL processes
Apache Hive is well suited for organizations looking for an initial tool to begin their process of managing their data warehouse as it is open-source and relatively easy to set up. This works well with some legacy systems and many consoles support this. While Hive used to be quite revolutionary, it has fallen behind many other tools that are more performant or specialized for managing DBs, writing queries, and partitioning tables.
Open-source software with little incentive to innovate or have consistent support to push out updates and changes to the platform
August 29, 2018

My Apache Hive Review

Kartik Chavan | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source
Apache Hive is being used in our company mainly for big data analysis. It has greatly helps us with data processing & analysis. It is being used across the whole organization. The business problem addressed by it is that it has been helping our organization in storing large data sets and easily accessing them.
  • 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.
It is perfectly suited for analytics.
Score 9 out of 10
Vetted Review
Verified User
Review Source
Hive is currently used in our Data Warehouse in our company. It helps us give more structure to our data and as Hive sits on top of Hadoop, the MR engine. It is a big plus when you want to run a complex query and get faster results. This helps us facilitate the Business Intelligence team to use Hive as a self-querying tool.
  • 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.
This is best suited for data analysts and scientists, it's not a programmers tool. You may still need an RDBMS to read data from as updates and deletes can get a bit more complicated, you can run batch jobs, this will have to be facilitated by additional tools.
Its good for fast query processing, for storing large amounts of data.
Score 9 out of 10
Vetted Review
Verified User
Review Source
Hive is not used across whole organization but used by certain teams which require querying data from our big data store infrastructure like HDFS. It provides an interface to interact with and directly query HDFS, similar to the way we do it with any relational databases. It is a powerful tool for querying big data.
  • Querying, joining and aggregating data
  • In built-in and user-defined functions
  • Speed
  • Support for other big data frameworks like Spark
  • Need better user interfaces for browsing datastores and querying
[Well suited for] Enterprises who want to create data warehouses on top of Hadoop ecosystem for reporting purpose or get summaries or aggregation from big data. In short, if you have implemented Hadoop then you need Hive.
Jordan Moore | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
Review Source
Hive allows us to run SQL queries against data sitting in Hadoop.
  • 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.
Hive is well-suited for providing an SQL engine on Hadoop, but there are alternative SQL on Hadoop projects that claim to have improvements over Hive.
Tejaswar Rao | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
We use hive for analyzing big sets of data and for developing rule-based applications. And also for visualization tools and where we query on large sets of data using hive for desired visualization. Hive is fast and also can be imported/exported using other hadoop components. We can use SQL to access data in hive and with no need to learn a new language.
  • 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
  1. To query on large sets of data
  2. Faster access compared to traditional Databases
  3. OLAP projects
  4. Data Warehousing project
  5. To get insights from GigaByte's or TeraByte's of data
  6. Rule based projects and also to identify the patterns in data
  7. For applying transformations on large sets of data
  8. Faster response time than traditional databases
  9. Also able to get connected with hadoop components
  10. For complex analytical and different types of data formats