With Apache Hive, you can enter the world of Big Data
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 …
Apache Hive
Apache Hive
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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
| Operating Systems | Unspecified |
|---|---|
| Mobile Application | No |
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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.
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
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April 22, 2022
With Apache Hive, you can enter the world of Big Data
- 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.
April 19, 2022
Best Distributed Database in the market
- 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.
April 12, 2022
Help your dev team !
- Simplify query to devs
- Organize data
- Batch process
- Deploy
- Maintenance
- Support
April 11, 2022
Spectacular SQL-like interface for accessing Hadoop
- 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
April 09, 2022
This system makes active data of value.
- 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.
April 08, 2022
Best query platform for ETL.
April 08, 2022
It is an advance to the ease of the processes
- 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
April 07, 2022
Capabilities of Apache Hive
- 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
April 07, 2022
Excellent bigdata warehouse 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.
April 06, 2022
very useful for OLTP
- 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.
November 24, 2021
Apache Hive
- 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.
June 02, 2021
Walk into the World of Big Data with Apache Hive
- 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.
December 28, 2020
Reliable and Cheaper one stop Data warehouse solution
- 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
October 07, 2020
Data warehouse made simple, yet powerful!
- 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
September 23, 2020
Big Data the SQL way
- 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.
September 21, 2020
Apache Hive: Big data querying tool w/SQL interface, but slower, more costly computation
- Flexibility through schema on read
- Familiar SQL like query language
- Functions for complex queries and analysis
- Slower processing than other tools on the market
September 20, 2020
Hive: When SQL marries with Hadoop
- 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).
September 19, 2020
Manage data for your warehouse as strong as a beehive using Apache HIve!
- 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.
September 18, 2020
Reliable, cheap and trustworthy!
- Reading databases
- Writing databases
- Storing databases
- Distributed databases
- Improvement techniques for handling Relational Data
- Advanced optimizations
- Transactions memory
September 18, 2020
Apache Hive: SQL, open-source querying tool
- 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
August 29, 2018
My Apache Hive Review
- 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.
June 07, 2018
Hive is solid data analytical 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.
March 01, 2018
Hive - SQL-like query engine for big data platform
- 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
February 17, 2018
One of the first SQL on Hadoop tools. Perhaps not the best.
- 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.
December 05, 2017
Apache Hive Faster and Can handle large sets of data
- 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