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Apache Hive is a versatile software that has been widely used across various departments and organizations for different use cases. It has …
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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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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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Apache Hive Hadoop Ecosystem - Big Data Analytics Tutorial by Mahesh Huddar
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Connecting Microsoft Power BI to Apache Hive using Simba Hive ODBC driver
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Discover HDP 2.1: Interactive SQL Query in Hadoop with Apache Hive
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What is Apache Hive?
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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.
Reviewers rate Usability highest, with a score of 8.5.
The most common users of Apache Hive are from Mid-sized Companies (51-1,000 employees).
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- 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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Verified User
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.
- 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 12, 2022
Help your dev team !
We build our data lake and perform queries on large amounts of data. We group data from multiple sources into a common structure, making it easy for our developers to perform complex queries without leaving the simple framework provided by SQL. Although the deployment is not easy, once we have the infrastructure, the work is greatly simplified.
- Simplify query to devs
- Organize data
- Batch process
- Deploy
- Maintenance
- Support
April 11, 2022
Spectacular SQL-like interface for accessing Hadoop
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
- 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 08, 2022
Best query platform for ETL.
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.
April 07, 2022
Capabilities of Apache Hive
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.
- 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 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.
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.
- 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
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.
- 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.
June 02, 2021
Walk into the World of Big Data with Apache Hive
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.
- 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.
September 20, 2020
Hive: When SQL marries with Hadoop
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.
- 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!
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.
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.
- 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!
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.
- Reading databases
- Writing databases
- Storing databases
- Distributed databases
- Improvement techniques for handling Relational Data
- Advanced optimizations
- Transactions memory
June 07, 2018
Hive is solid data analytical tool
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.
- 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
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.
- 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
December 05, 2017
Apache Hive Faster and Can handle large sets of data
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.
- 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
September 13, 2017
Apache Hive Review
Hive is currently being used across the entire analytics organization at SurveyMonkey. The business problem that we solve through it is, accessing/storing large data sets(typically logs), in a scalable and accessible place.
- SQL like query engine, allows easy ramp up from a standard RDBMS
- Scalability is great
- If properly configured the data retreival is fantastic
- The way we currently have it implemented is quite slow, but I believe that's more of our implementation
- Joins tend to be slow
April 26, 2017
Apache Hive - Querying Big Data Made Easy!
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.
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.
- 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.
- 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.
February 14, 2017
Easy access to data in Hadoop
Apache Hive is primarily used by data analysts and data engineers at our company. We store most of our data in Hadoop and Apache Hive allows us to access the data faster than by writing MapReduce jobs.
- Faster than writing MapReduce or scalding jobs to access data in Hadoop.
- Syntax is essentially the same as that of SQL, making the barriers for entry to start using data low.
- Apache Hive can be quite slow and is not suitable for interactive querying. Simple queries will take many minutes and more complex queries can take a very long time to finish running.
September 16, 2016
Hive, last generation tooling but revolutionary for it's time.
Hive was once a part of our platform but it never lived up to the promise of performant SQL on HDFS and thus was only truly useful for the users who didn't have the expertise or time to write MapReduce. With the advent of Spark, Hive's time is numbered and I would not invest in learning it specifically but instead use SparkSQL which has some of the better parts of Hive under the covers along with Spark's better execution engine.
- Connect BI tools to non relational data stores
- Simplify writing legacy MapReduce
- Speed needs to be a lot better
- Concurrency is not up to snuff
September 13, 2016
As sweet as Honey - Apache Hive
Apache Hive is used for data processing and analysis in the company that I am working for. Apache Hive is being used by the IT department and the results it produces are shared across the whole organization. Performing operations on terabytes of data has become easy without worrying much about the complexity involved. Similarity with SQL related tools has increased the difficulty in looking for employees with big-data skills.
- Apache Hive works extremely well with large data sets. Analysis over a large data set (Example: 1PB of data) is made easy with hive.
- User-defined functions gives flexibility to users to define operations that are used frequently as functions.
- String functions that are available in hive has been extensively used for analysis.
- Joins (especially left join and right join) are very complex, space consuming and time consuming. Improvement in this area would be of great help!
- Having more descriptive errors help in resolving issues that arise when configuring and running Apache Hive.