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.
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Apache Hive Reviews
17 Reviews
Mid-sized Companies (51-1,000 employees)
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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.
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.
Pros
Please provide some detailed examples of things that Apache Hive does particularly well.
Migration to the cloud is modern and very secure.
Cons
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.
Likelihood to Recommend
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.
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.
Pros
The unification of the data will help to establish the commercial criteria.
We are sure that the data is protected
Cons
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
Likelihood to Recommend
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
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.
Pros
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
Cons
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
Likelihood to Recommend
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.
VU
Verified User
C-Level Executive in Product Management (51-200 employees)
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.
Pros
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.
Cons
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.
Likelihood to Recommend
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.
VU
Verified User
Program Manager in Information Technology (201-500 employees)
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
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.
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.
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.
Pros
Monitor query performance
Manage tables in the data warehouse
Uses standard SQL
Cons
UI is quite dated and not intuitive
Open-source, so does not have consistent updates or support
Not the most optimal for ETL processes
Likelihood to Recommend
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.
VU
Verified User
Analyst in Professional Services (201-500 employees)
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.
