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 33)
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Score 9 out of 10
Vetted Review
Verified User
Review Source
To query a huge, distributed dataset, Apache Hive was built by Facebook. Unlike Apache Hive, Apache Spark is an in-memory computation engine, which is why it is significantly quicker than Apache Hive at querying large amounts of data. In contrast to Apache HBase, Apache Hive is better suited for dealing with structured data stored on HDFS.
April 12, 2022

Help your dev team !

Score 8 out of 10
Vetted Review
Verified User
Review Source
Community support and ease of use -not deployment.

It enables querying and analyzing large amounts of data stored in HDFS, on the petabyte scale. It has a query language called HQL that transforms SQL queries into MapReduce jobs that run on Hadoop, and it is wonderful for the devs team that love it.
Score 9 out of 10
Vetted Review
Verified User
Review Source
Apache Spark is similar in the sense that it too can be used to query and process large amounts of data through its Dataframe interface. Hive is better for short-term querying while Spark is better for persistent and long-term analysis. Another product is Impala. For our purposes, Impala and Hive were similar, but in general, Impala is better for real-time analysis.
Camilo Palacios | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source
We have used a simple but necessary function such as merging certain data tables, which although they may be from different areas, complement each other or are necessary, you can use metadata if what you need is to validate the origin of your information and what impact it has, is also feasible.
Omkar Marne | TrustRadius Reviewer
Score 6 out of 10
Vetted Review
Verified User
Review Source
Apache Hadoop is built on top of the Hadoop File system so it gives its best when integrated with Hadoop. Data analysis and query optimization become very easy when used with Hadoop to perform Extract transform load operations. As Hadoop is a big data system and handles large data sizes, Apache hive can query large data with less time complexity.
Pablo Gonzalez | 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, it can be sent to third parties the information already transformed
Score 8 out of 10
Vetted Review
Verified User
Review Source
Queries are easy to write and interface is similar to SQL so learning overhead is reduced. Multi user and data type support is provided. Can be easily scaled for very large amount of analytics. It is very flexible in terms of using file formats.
akshay kashyap | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
Apache Hive is a query language developed by Facebook to query over a large distributed dataset. Apache is a query engine that runs on top of HDFS, so it utilizes the resources of HDFS Hadoop setup, while Apache Spark is an in memory compute engine, and that's why [it is] much faster than Apache Hive. While Apache HBase mostly deals with unstructured data, while Apache Hive is suitable using structured data stored on HDFS.
Manjeet Singh | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
Besides Hive, I have used Google BigQuery, which is costly but have very high computation speed.
Amazon Redshift is the another product, I used in my recent organisation.
Both Redshift and BigQuery are managed solution whereas Hive needs to be managed
Score 9 out of 10
Vetted Review
Verified User
Review Source
From my experience Apache Hive is the easiest for database admins and experts to learn due to similarities between SQL and HiveQL, which makes it the best choice for majority of customers. The remaining platforms are more hard to use and are targeted mainly towards programmers, which are able to fully utilize features available only via programmable APIs.
September 23, 2020

Big Data the SQL way

Score 8 out of 10
Vetted Review
Verified User
Review Source
Hive and Spark have the same parent company hence they share a lot of common features. Hive follows SQL syntax while Spark has support for RDD, DataFrame API. DataFrame API supports both SQL syntax and has custom functions to perform the same functionality. Spark is faster and can run on distributed systems while Hive is slower.
Ananth Gouri | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
One of the major advantages of using Presto or the main reason why people use Presto (Teradata) is due to that fact it can support multiple data sources - which is lacking as in the case of Apache Hive. But still, most people who come from a Structured data-based background like the old days of Dbase, or the later ones of SQL databases like MS SQL, MySQL, PostgreSQL - may still opt to go with Apache Hive for its HiveQL ease and functionality.
Nicolas Hubert | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
Easy to understand, well supported by the community, good documentation. However, it is possible that SAP Business Warehouse could be a good fit, too, even maybe better. I did not have the chance to try it though. We selected Apache Hive because it was far less expensive and handled all the tasks we wanted to perform with it.
Jordan Moore | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
Review Source
Hive was one of the first SQL on Hadoop technologies, and it comes bundled with the main Hadoop distributions of HDP and CDH. Since its release, it has gained good improvements, but selecting the right SQL on Hadoop technology requires a good understanding of the strengths and weaknesses of the alternative options.
Tejaswar Rao | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
  • Faster response time and also can handle complex analytical queries
  • Can able to write custom function using python and hive
  • Able to connect using hadoop components and also using R
  • Can handle different data formats
  • Can use Structured Query language to access the data
Bharadwaj (Brad) Chivukula | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source

For storing bulk amount of data in a tabular manner, and where there's no need need of primary key, or just in case, if redundant data is received, it will not cause a problem. For small amounts of data, it does run MR, so beware. If your intention is to use it as a transactional records, then do not go with it. Explore other tools like Spark also as many of the features that Hive does is now supported by Spark.


September 13, 2017

Apache Hive Review

Sameer Gupta | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source
I wasn't part of the evaluation process for Apache Hive. This was already implemented when I joined the company. I have worked with other big data plaftforms and I personally thinks most of them are quite comporable to one another. It really depends on what the company is going for. For exampel Google Cloud makes a ton of sense for a user if they developed their application on Google App Engine.