Apache Hive Reviews and Ratings

Use Cases and Deployment Scope

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.

Likelihood to Recommend

Data warehouses that update and append records in batches or real time can be queried using Apache Hive. Tableau and other reporting tools may be used straight from Python searches on Apache data sets. Structured data and tables may be accessed using SQL-like syntax. Using a hive, you may build tables at various levels of the Data Lake. Transactional databases are not the best fit.

Use Cases and Deployment Scope

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.

Likelihood to Recommend

Apache Hive wont is really useful when we just store small data sets. so sometimes our usage wont is suitable for Hive. we are planning to move to SQL Databases if it continues.

Use Cases and Deployment Scope

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.

Likelihood to Recommend

It is great for laboratory environments and to start working with unstructured data about which we are not very clear about how we want to treat it. It also allows queries to be improved very quickly by allowing developers to work with SQL instead of map-reduce. As an improvement, in productive environments, troubleshooting is complicated and requires expert personnel.

Use Cases and Deployment Scope

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

Likelihood to Recommend

Apache Hive is well-suited for querying Hadoop. If you use Hadoop you should consider Hive. It is well-suited for large organizations where there is lots of data that needs to be queried. However, there is significant overhead to set up and maintaining Hive (and Hadoop in general). Small companies and individuals should consider other means of storing data, such as SQL.

Use Cases and Deployment Scope

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.

Likelihood to Recommend

Software work execution is on a large scale, it is good to use for new projects or organizational changes, data lineage mapping has always been dubious but this one has had good results. You can store and synchronize data from different departments, the storage process can be manual but it is best automated.

Use Cases and Deployment Scope

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.

Likelihood to Recommend

Apache Hive is best for ETL ( Extract Transform Load ) purposes. It gives its best performance when integrated with the Hadoop file distributed system. Its also very good for performing mathematical operations and when the data is organized and structured. It can handle large sizes of data ( petabytes) but requires a lot of in-memory in the system. It supports both unstructured and structured data nut best with structured data.

Use Cases and Deployment Scope

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.

Likelihood to Recommend

In addition to the fact that the information is quickly accessible through the established security protocols, it has not helped us as users to maintain a fairly comfortable data processing flow, it is more profitable to process the data in batches, we have been able to unify data from different sources

Use Cases and Deployment Scope

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.

Likelihood to Recommend

If you have workforce who are knowing SQL and you have a need to explore large-scale data and get insights from it then Apache Hive is perfect for you. If you have experienced people who have worked on big data earlier then using Splunk is better. For starting the journey in data-driven decisions and data analytics it is better to use Apache Hive first.

Use Cases and Deployment Scope

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.

Likelihood to Recommend

Apache Hive is a data warehouse/ ETL solution that is being used for processing big data for analytics and visualizations. Apache Hive has great architecture that makes it very well suited for organizations.

The Metastore, is used for storing metadata for each table and its schema. The Driver operates as a controller for executions of the statements. Like other components such as Optimizer and CLI, Thrift Server are some components that enable the processing of big data transformation.

Use Cases and Deployment Scope

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.

Likelihood to Recommend

Keeps queries running very fast and takes very little time to write Hive queries in comparison to MapReduce code. Very easy to write queries including joins in Hive.