Apache Hive Reviews & Insights

Score8 out of 10

95 Reviews and Ratings

Community insights

TrustRadius Insights for Apache Hive are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.

Business Problems Solved

Apache Hive is a versatile software that has been widely used across various departments and organizations for different use cases. It has proven to be particularly helpful in handling large datasets, migrating data between different operating systems, synchronizing programs, and fetching and generating product metrics. Users have found value in using Hive for data analytics, engineering, data science, product management, and IT-related tasks such as improving analysis of big datasets stored in Hadoop HDFS.

Furthermore, Apache Hive has simplified the process of filtering and cleaning data using SQL, reducing the learning curve for handling big data. It allows users to run SQL queries against data in Hadoop, enabling efficient analysis of large datasets without the need to learn a new language. Additionally, Hive has been utilized for building reports, analyzing data stored in the Hadoop file system, processing events gathered in HDFS, and converting them into parquet files for fast querying.

Overall, users have praised Apache Hive for its scalability, accessibility, and cost-effectiveness in storing and retrieving analytics data. It has provided an intuitive solution for storing large datasets, querying big sets of data using SQL, aggregating massive datasets into distilled information for data-driven decision making, and creating external and internal tables in Hadoop/BigData projects. With its ability to process both unstructured and structured data efficiently, Hive has become an essential tool for data analysts, engineers, and business analysts across organizations.

Apache Hive Reviews

10 Reviews

Engineering

With Apache Hive, you can enter the world of Big Data

Rating: 8 out of 10

Incentivized

April 22, 2022

Use Cases and Deployment Scope

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.

Pros

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.

Cons

Due to the shuffled data, complex joins may take a long time to complete.
Execution is dependent on external storage and memory.

Likelihood to Recommend

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.

Alternatives

Apache Spark

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.

Verified User

Engineer in Engineering (Computer Software company, 201-500 employees)

Vetted Review

2 years of experience

Spectacular SQL-like interface for accessing Hadoop

Rating: 9 out of 10

Incentivized

April 11, 2022

Use Cases and Deployment Scope

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

Pros

Easy-to-use, interactive modern layout
Easy to organize data and view tables and views from across the organization
Fast speed for most queries

Cons

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

Likelihood to Recommend

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.

Alternatives

Apache Spark and Apache Kafka

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.

Verified User

Engineer in Engineering (Telecommunications company, 10,001+ employees)

Vetted Review

1 year of experience

Reliable and Cheaper one stop Data warehouse solution

Rating: 9 out of 10

Incentivized

December 28, 2020

Use Cases and Deployment Scope

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

Pros

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

Cons

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

Likelihood to Recommend

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

Alternatives

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

Manjeet Singh

Senior Manager - Engineering in Engineering at Hike Messenger (Internet, 51-200 employees)

Vetted Review

5 years of experience

View profile

Apache Hive: Big data querying tool w/SQL interface, but slower, more costly computation

Rating: 7 out of 10

Incentivized

September 21, 2020

Use Cases and Deployment Scope

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.

Pros

Flexibility through schema on read
Familiar SQL like query language
Functions for complex queries and analysis

Cons

Slower processing than other tools on the market

Likelihood to Recommend

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.

Kristjan Gannon

Senior Software Engineer in Engineering at Sovrn Holdings, Inc. (Internet, 51-200 employees)

Vetted Review

2 years of experience

View profile

Manage data for your warehouse as strong as a beehive using Apache HIve!

Rating: 9 out of 10

Incentivized

September 19, 2020

Use Cases and Deployment Scope

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.

Pros

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.

Cons

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.

Likelihood to Recommend

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.

Alternatives

Presto

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.

Ananth Gouri

Assistant Professor in Engineering at The National Institute of Engineering, Mysuru (Education Management, 501-1000 employees)

Vetted Review

2 years of experience

View profile

Hive is solid data analytical tool

Rating: 9 out of 10

Incentivized

June 7, 2018

Use Cases and Deployment Scope

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.

Pros

It's Fast!
You can store a different kind of data structures here other than the standard ones
Good scalability
Good redundancy too

Cons

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.

Likelihood to Recommend

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.

Alternatives

I have used Storm for real-time processing, but that only addresses a few data points. But for a larger access to data, Hive is well suited.

Verified User

Engineer in Engineering (Information Technology and Services company, 201-500 employees)

Vetted Review

4 years of experience

Apache Hive for ETL workloads

Rating: 5 out of 10

Incentivized

September 11, 2017

Use Cases and Deployment Scope

Apache Hive is being using across our organisation for analytical workloads. We use Hive along with Hortonworks distribution and it's a great SQL on Hadoop tool.

Pros

Hive is good for ETL workloads on Hadoop.
HiveQL translates SQL like queries into map reduce jobs.It supports custom map reduce scripts to plugged in.
Hive has two kinds of tables- Hive managed tables and external tables.
Use external table when other applications like pig, sqoop or mapareduce also using the file in hdfs. Once we delete the external table from Hive, it just deletes the metadata from Hive and original file in hdfs stays.

Cons

Use Hive for analytical work loads. Write once and read many scenarios. Do not prefer updates and deletes.
Behind scenes Hive creates map reduce jobs. Hive performance is slow compared to Apache Spark.
Map reduce writes the intermediate outputs to dial whereas Spark operates in in-memory and uses DAG.

Likelihood to Recommend

Use it for ETL workloads. I prefer repeat the same workload with Spark and decide the better performance

Alternatives

Apache Pig and Apache Spark

Hive is SQL compliant which makes it easy for the data folks compared to Pig

Verified User

Analyst in Engineering (Hospital & Health Care company, 501-1000 employees)

Vetted Review

2 years of experience

Apache Hive - Querying Big Data Made Easy!

Rating: 8 out of 10

Incentivized

April 26, 2017

Use Cases and Deployment Scope

We use Apache Hive for two main use cases, analyzing our ever growing data volume insights and reports, and as part of our ETL pipeline where we found writing in SQL like syntax to allow for more rapid development with low complexity to the overall system.

Apache Hive solves a few issues for us but the main one being the ability to analyze large volumes of data on S3 directly with overall strong performance. We have been able to analyze billions of records in a matter of minutes with relatively small EC2 cluster using Apache Hive. It also allows for our Data Analysts to simply write SQL and avoids the ramp up to use other tools such as Apache Pig.

Pros

Apache Hive allows use to write expressive solutions to complex problems thanks to its SQL-like syntax.
Relatively easy to set up and start using.
Very little ramp-up to start using the actual product, documentation is very thorough, there is an active community, and the code base is constantly being improved.

Cons

Debugging can be messy with ambiguous return codes and large jobs can fail without much explanation as to why.
Hive is only SQL-like, while more features are being added we have found that some things do not translate over (for example outer joins, inserts, columns can only be referenced once in a select, etc.).
For out ETL jobs it does not seem to be the optimal tool due to tunings and performance being difficult, Apache Pig may be better for heavy processing jobs.

Likelihood to Recommend

Apache Hive shines for ad-hoc analysis and plugging into BI tools. Its SQL-like syntax allows for ease of use not for only for engineers but also for data analysts. Through our experience, there are probably more desirable tools to use if you are planning on integrating Hive into your processing pipeline.

Alternatives

Apache Pig

Apache Pig is probably the most direct technology to compare to Hive and has several different use cases to Hive. If you want to simplify processing tasks that run using MapReduce then Apache Pig may be a better tool for the job. However if you are going to be running many ad-hoc queries to dig through your data then Apache Hive really shines and I would consider to be a much more valuable tool for this purpose. Both great tools, it just comes down to individual use cases and what strengths your team has.

Verified User

Engineer in Engineering (Computer Software company, 51-200 employees)

Vetted Review

3 years of experience

Hive Away, but not for everything!

Rating: 6 out of 10

Incentivized

February 28, 2017

Use Cases and Deployment Scope

We use apache hive across the whole organization. We built our own in-house hadoop cluster for data warehousing purposes complementary to HP Vertica which we were using. Vertica is limited to scale, and to achieve true scalability and process trillions of records we had to invest in a new solution. Enter Apache Hive. We are very data driven as an organization and hence to satisfy to appetite of people and also stick to something familiar to query data (SQL) we decided to invest in Apache Hive as a starting point in our new data infrastructure.

Pros

Hive which leverages traditional MapReduce at the core, can be used to process a large amount of data without a problem. Any problem that can be solved with MapReduce can now be simply expressed in SQL.
Hive leverages the disk in the case of processing large data and is not limited by physical memory of any one machine (which is a limitation for systems like presto). Hence it even allows reasonable fact-fact cross joins.
Hive is extensible with UDFs. For any common patterns you can quickly write your own function set and it can be leveraged by everyone.

Cons

Compute Speed - Hive will be my last option to query vs. something like Presto, which has a much smarter query engine. Hive is slow, and I'd use it only if we cannot use something like Presto/Impala.
SQL syntax of hive is unique and does not conform to ANSI SQL. This is quite painful for beginners.
The ability to upsert records would be nice to have. Hive is cumbersome for mutable data where partitions require them to be rewritten. No one has solved this really well. If this is solved - it could be leveraged by many systems.

Likelihood to Recommend

Process large datasets (especially joins of two large datasets, cross joins etc). Hive is not well suited for generic queries on one table and it can still be very slow. There are better solutions for that (Presto, Impala).

Alternatives

presto and Apache Pig

We selected Hive because it supports SQL, schema and provides structure on top of hadoop. Having data structured has its benefits, especially if there are thousands of users processing on the same data over and over again. Pig provides the ability to process unstructured data. However, it is hard to use and requires learning a new scripting language. On the processing side, Hive can lift and process any volume and any complex query. I'd recommend it for complex queries. However, for more simpler daily query, I'd recommend using Presto.

Praveen Murugesan

Engineering Manager - Ride Experience in Engineering at Uber (Internet, 5001-10,000 employees)

Vetted Review

3 years of experience

View profile

Hive, a very powerful open source data warehouse solution.

Rating: 10 out of 10

December 22, 2014

Use Cases and Deployment Scope

Hive is used by data team to store the largest datasets of the company. Data is partitioned in Hive and can be queried by Impala.

Pros

Partition to increase query efficiency.
Serde to support different data storage format.
Integrate well with Impala and data can be queried by Impala.
Support of parquet compression format

Cons

Speed is slower compared to Impala since it uses map reduce

Likelihood to Recommend

Hive is a data warehouse and it does not allow for updates and deletions. If data needs to be updated frequently, it might not be the best storage solution for that purpose.

Alternatives Evaluated

Impala queries faster than Hive on the same data but it highly depends on Hive. Also Impala does not support Serde allowing to query different data format (JSON, XML), but Hive does.

Yinghua Hu

Senior Data Scientist in Engineering at CARD.com (Financial Services, 11-50 employees)

Vetted Review

1 year of experience

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Hive Away, but not for everything!

Rating: 6 out of 10

Incentivized

February 28, 2017

Use Cases and Deployment Scope

Pros

Hive which leverages traditional MapReduce at the core, can be used to process a large amount of data without a problem. Any problem that can be solved with MapReduce can now be simply expressed in SQL.
Hive leverages the disk in the case of processing large data and is not limited by physical memory of any one machine (which is a limitation for systems like presto). Hence it even allows reasonable fact-fact cross joins.
Hive is extensible with UDFs. For any common patterns you can quickly write your own function set and it can be leveraged by everyone.

Cons

Compute Speed - Hive will be my last option to query vs. something like Presto, which has a much smarter query engine. Hive is slow, and I'd use it only if we cannot use something like Presto/Impala.
SQL syntax of hive is unique and does not conform to ANSI SQL. This is quite painful for beginners.
The ability to upsert records would be nice to have. Hive is cumbersome for mutable data where partitions require them to be rewritten. No one has solved this really well. If this is solved - it could be leveraged by many systems.

Likelihood to Recommend

Alternatives

presto and Apache Pig

Praveen Murugesan

Engineering Manager - Ride Experience in Engineering at Uber (Internet, 5001-10,000 employees)

Vetted Review

3 years of experience

View profile