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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 Enterprises (1,001+ employees).
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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 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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Score 8 out of 10
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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.
April 19, 2022
Best Distributed Database in the market
Apache hive gave more flexible than MS SQL server. Elasticsearch was little complex. GoogleBigQuery cost more.
April 12, 2022
Help your dev team !
Community support and ease of use -not deployment.
It enables querying and analyzing large amounts of data stored in HDFS, on the petabyte scale. It has a query language called HQL that transforms SQL queries into MapReduce jobs that run on Hadoop, and it is wonderful for the devs team that love it.
It enables querying and analyzing large amounts of data stored in HDFS, on the petabyte scale. It has a query language called HQL that transforms SQL queries into MapReduce jobs that run on Hadoop, and it is wonderful for the devs team that love it.
April 11, 2022
Spectacular SQL-like interface for accessing Hadoop
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.
April 09, 2022
This system makes active data of value.
- ACES (Automated Compliance and Evaluation System) Web Audit Technology, APARAVI and data intelligence & automation platform
We have used a simple but necessary function such as merging certain data tables, which although they may be from different areas, complement each other or are necessary, you can use metadata if what you need is to validate the origin of your information and what impact it has, is also feasible.
April 08, 2022
Best query platform for ETL.
Apache Hadoop is built on top of the Hadoop File system so it gives its best when integrated with Hadoop. Data analysis and query optimization become very easy when used with Hadoop to perform Extract transform load operations. As Hadoop is a big data system and handles large data sizes, Apache hive can query large data with less time complexity.
April 08, 2022
It is an advance to the ease of the processes
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, it can be sent to third parties the information already transformed
April 07, 2022
Capabilities of Apache Hive
Queries are easy to write and interface is similar to SQL so learning overhead is reduced. Multi user and data type support is provided. Can be easily scaled for very large amount of analytics. It is very flexible in terms of using file formats.
April 07, 2022
Excellent bigdata warehouse solution
April 06, 2022
very useful for OLTP
Due to effective queries resolved time and the performance and user-friendly framework compared to other products.
November 24, 2021
Apache Hive
- Azure Synapse Analytics (Azure SQL Data Warehouse) and Databricks Lakehouse Platform (Unified Analytics Platform)
June 02, 2021
Walk into the World of Big Data with Apache Hive
Apache Hive is a query language developed by Facebook to query over a large distributed dataset. Apache is a query engine that runs on top of HDFS, so it utilizes the resources of HDFS Hadoop setup, while Apache Spark is an in memory compute engine, and that's why [it is] much faster than Apache Hive. While Apache HBase mostly deals with unstructured data, while Apache Hive is suitable using structured data stored on HDFS.
December 28, 2020
Reliable and Cheaper one stop Data warehouse solution
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
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
September 23, 2020
Big Data the SQL way
Hive and Spark have the same parent company hence they share a lot of common features. Hive follows SQL syntax while Spark has support for RDD, DataFrame API. DataFrame API supports both SQL syntax and has custom functions to perform the same functionality. Spark is faster and can run on distributed systems while Hive is slower.
September 20, 2020
Hive: When SQL marries with Hadoop
Apache Hive decouples the query layer from the storage layer, it is more flexible and expandable.
September 19, 2020
Manage data for your warehouse as strong as a beehive using Apache HIve!
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.
September 18, 2020
Reliable, cheap and trustworthy!
Easy to understand, well supported by the community, good documentation. However, it is possible that SAP Business Warehouse could be a good fit, too, even maybe better. I did not have the chance to try it though. We selected Apache Hive because it was far less expensive and handled all the tasks we wanted to perform with it.
August 29, 2018
My Apache Hive Review
I considered Hive because it is the best suited option when it comes to larger data access. Besides, learning HiveQL is comparatively easy.
June 07, 2018
Hive is solid data analytical tool
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.
March 01, 2018
Hive - SQL-like query engine for big data platform
[We selected Apache Hive because] It's from apache and opensource. So it's free.
February 17, 2018
One of the first SQL on Hadoop tools. Perhaps not the best.
- Apache Impala, Apache Spark and PostgreSQL
Hive was one of the first SQL on Hadoop technologies, and it comes bundled with the main Hadoop distributions of HDP and CDH. Since its release, it has gained good improvements, but selecting the right SQL on Hadoop technology requires a good understanding of the strengths and weaknesses of the alternative options.
December 05, 2017
Apache Hive Faster and Can handle large sets of data
- Faster response time and also can handle complex analytical queries
- Can able to write custom function using python and hive
- Able to connect using hadoop components and also using R
- Can handle different data formats
- Can use Structured Query language to access the data
October 25, 2017
Bringing Structure to your Unstructured Data
For storing bulk amount of data in a tabular manner, and where there's no need need of primary key, or just in case, if redundant data is received, it will not cause a problem. For small amounts of data, it does run MR, so beware. If your intention is to use it as a transactional records, then do not go with it. Explore other tools like Spark also as many of the features that Hive does is now supported by Spark.
September 13, 2017
Apache Hive Review
I wasn't part of the evaluation process for Apache Hive. This was already implemented when I joined the company. I have worked with other big data plaftforms and I personally thinks most of them are quite comporable to one another. It really depends on what the company is going for. For exampel Google Cloud makes a ton of sense for a user if they developed their application on Google App Engine.
September 11, 2017
Apache Hive for ETL workloads
Hive is SQL compliant which makes it easy for the data folks compared to Pig