Apache Hive

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.

We use Apache Hive to store a large set of data, which are huge documents such as problem statements and its answer, not only submitted by the site owners but also by the user of the site.

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.

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

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, and it can be sent to third parties the information already transformed.

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.

The software is intuitive from the first steps, one of the first features we take into account for the software does not allow duplicate files to be stored. It is advanced software that through data the system constantly learns and develops. The first phase is very effective, the analysis and checking of the information are verified in detail.

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.

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.

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.

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.

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.

I have used Apache Hive in [the] last 3 companies and it's being used by the multiple departments spread across data analytics, engineering, data science and product management.
It's being used for fetching and generating all the product metrics, for fetching legal data whenever required. All the product history data is stored in it,
It's the one stop cheaper solution for storing and fetching all the analytics data

We are using Apache Hive in our whole company as the main data warehouse software solution covering all needed data warehousing tasks. It is being used to interact with huge datasets located in a distributed storage. Since we are using a variety of data formats Apache Hive enables us to query anything with unified SQL syntax.

I am working as a Research Assistant where I have to process tons of data to produce appropriate findings. Our NLP lab used it for all its big data processing, for example: removing urls, finding counts of specific words, etc. Mainly it assisted in all the processing, cleaning on big datasets we collected for our research.

We use Apache Hive to make data-driven decisions. It is used from finance to engineering to sales. It helps aggregate our massive data sets into distilled information.

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.

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 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.

Our company primarily uses Apache Hive to manage our data warehouse by being able to query multiple databases. We partition our tables as well as monitor query performance on very custom data queries by using this hive. Hive is only used by our data analysts and an overseas data warehouse team with only a few shared licenses existing on our virtual machines.

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.

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.

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.

Hive allows us to run SQL queries against data sitting in Hadoop.

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.