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
13 Reviews
Enterprises (1,001+ employees)
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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.
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.
Pros
Filtering data
cleaning data
SQL like interface
Integrates with Hadoop
Cons
Uses lot of lot of memory
Not compatible with other databases like Postgres, MySql
Limited support
Slow as compare o other interfaces
Likelihood to Recommend
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.
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.
Pros
Used in data warehouse like similar to ETL tools.
Interface like SQL give data stored in various db group.
Enables analytics at massive scale.
Cons
Way of framework development can be improved.
OLTP is not supported.
Does not offer real time queries.
Likelihood to Recommend
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.
VU
Verified User
Administrator in Information Technology (1001-5000 employees)
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.
Pros
Apache Hive is fault-tolerant.
Apache Hive's latest version supports ACID transactions.
Apache Hive supports UPDATE, DELETE and MERGE.
Cons
Apache Hive should support ROLLBACK, COMMIT operations.
Apache Hive should support XML SerDe.
Apache Hive.
Likelihood to Recommend
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.
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.
Pros
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.
Cons
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.
Likelihood to Recommend
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.
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.
Pros
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.
Cons
No transaction support.
No sub-query support.
Can only deal with the cold data (non-real time).
Likelihood to Recommend
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.
VU
Verified User
Strategist in Information Technology (10,001+ employees)
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.
Pros
Reading databases
Writing databases
Storing databases
Distributed databases
Cons
Improvement techniques for handling Relational Data
Advanced optimizations
Transactions memory
Likelihood to Recommend
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.
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.
Pros
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.
Cons
Apache Hive isn't designed for and doesn't support online processing of data.
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.
Pros
Querying, joining and aggregating data
In built-in and user-defined functions
Speed
Support for other big data frameworks like Spark
Cons
Need better user interfaces for browsing datastores and querying
Likelihood to Recommend
[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.
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.
Pros
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
Cons
Security roles for different users should be implemented
All the functionalities of SQL should be available
Likelihood to Recommend
To query on large sets of data
Faster access compared to traditional Databases
OLAP projects
Data Warehousing project
To get insights from GigaByte's or TeraByte's of data
Rule based projects and also to identify the patterns in data
For applying transformations on large sets of data
Faster response time than traditional databases
Also able to get connected with hadoop components
For complex analytical and different types of data formats
