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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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- 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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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
September 11, 2017
Apache Hive for ETL workloads
Apache Hive is being using across our organisation for analytical workloads. We use Hive along with Hortonworks distribution and it's a great SQL on Hadoop tool.
- Hive is good for ETL workloads on Hadoop.
- HiveQL translates SQL like queries into map reduce jobs.It supports custom map reduce scripts to plugged in.
- Hive has two kinds of tables- Hive managed tables and external tables.
- Use external table when other applications like pig, sqoop or mapareduce also using the file in hdfs. Once we delete the external table from Hive, it just deletes the metadata from Hive and original file in hdfs stays.
- Use Hive for analytical work loads. Write once and read many scenarios. Do not prefer updates and deletes.
- Behind scenes Hive creates map reduce jobs. Hive performance is slow compared to Apache Spark.
- Map reduce writes the intermediate outputs to dial whereas Spark operates in in-memory and uses DAG.
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 28, 2017
Hive Away, but not for everything!
We use apache hive across the whole organization. We built our own in-house hadoop cluster for data warehousing purposes complementary to HP Vertica which we were using. Vertica is limited to scale, and to achieve true scalability and process trillions of records we had to invest in a new solution. Enter Apache Hive. We are very data driven as an organization and hence to satisfy to appetite of people and also stick to something familiar to query data (SQL) we decided to invest in Apache Hive as a starting point in our new data infrastructure.
- Hive which leverages traditional MapReduce at the core, can be used to process a large amount of data without a problem. Any problem that can be solved with MapReduce can now be simply expressed in SQL.
- Hive leverages the disk in the case of processing large data and is not limited by physical memory of any one machine (which is a limitation for systems like Presto). Hence it even allows reasonable fact-fact cross joins.
- Hive is extensible with UDFs. For any common patterns you can quickly write your own function set and it can be leveraged by everyone.
- SQL syntax of hive is unique and does not conform to ANSI SQL. This is quite painful for beginners.
- The ability to upsert records would be nice to have. Hive is cumbersome for mutable data where partitions require them to be rewritten. No one has solved this really well. If this is solved - it could be leveraged by many systems.
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.
May 25, 2016
Hive brings the power of SQL to Hadoop
I have used Hive at an enterprise company where I interned. It was being used by the IT department to improve analysis of large datasets stored in the company's Hadoop HDFS. It was also being used because of its support for HiveQL which is a SQL like language enabling queries on large datasets. It also reduced the learning curve for handling big data because of HiveQL's similarity to SQL.
- Supports SQL like queries
- Various storage types including RCFile, HBase, ORC, etc.
- Supports indexing for acceleration
- HiveQL does not have all the features of SQL
- No support for transactions
April 20, 2016
HiveQL, Almost SQL, but not quite.
Hive is being used to put an SQL interface to our Hadoop cluster. This works well because most of our organization is very SQL friendly, so when introducing a new technology, such as Hadoop, the technical users are easily able to adapt to the new technology with no problem.
- Run SQL queries to an Hadoop cluster.
- Many different consoles can use it.
- Users don't have to write map reduce.
- Hive needs more SQL support.
- Enabling more date functions.
- Enabling more SQL table functions, such as inserting into a temp table.
December 22, 2014
Hive, a very powerful open source data warehouse solution.
Score 10 out of 10
Vetted Review
Verified User
Hive is used by data team to store the largest datasets of the company. Data is partitioned in Hive and can be queried by Impala.
- Partition to increase query efficiency.
- Serde to support different data storage format.
- Integrate well with Impala and data can be queried by Impala.
- Support of parquet compression format
- Speed is slower compared to Impala since it uses map reduce