Apache Hive

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

Well suited for: For accessing the structured data and tables using SQL-like syntax. A hive is a good option for creating tables in different layers of Data Lake. Not well suited for Transactional databases.

You can use Apache Hive to query over a large data warehouse which updates, append records on either batch or in real time. Apache queries can give you output in the desired format that you can use as any reporting tool such as Tableau, directly using Python.

It's fairly okay to set up and also cost is well within the pocket.
If our requirement of aggregation is within seconds for. Terabytes of data then we may have to lookup for other solutions

Apache Hive fits perfectly if scalability, performance and fault-tolerance are essential for your data warehousing needs. If you are required to process batch jobs Apache Hive will keep your customers happy. On the other hand, if you are working with web logs data and append-only flat-file type of data, then there are better solutions on the market.

Apache Hive is very well suited for those who are very familiar to SQL query syntax. Due to its easy to use syntax, it can really help in scenarios where a conventional database cannot be used for analysis of big datasets.

On the other hand, it's definitely slower than some other alternatives such as spark. Also, it's not recommended to use it in processing small datasets. Pandas and other normal data loading libraries can be useful to deal with small datasets.

Apache Hive is useful for regularly reporting and analyzing data. In terms of ad-hoc analysis and debugging, the cycles can be quite long for querying, feedback, debugging queries, etc.

Hive is suitable for big data analysis tasks on top of the historical data storage but is not quite suitable for any real-time data (if that is the case, Casandra should be considered). And as it is not real SQL, for a read-only operation and in-fly aggregation, it is very good, however, if data modification and transaction are needed, it is not suitable.

I would definitely recommend Apache Hive if sought by a colleague. Especially for people who are working at academic institutions, they can demonstrate programs like word count, tab count, space count, new lines count, and other related programs - with a basic setup of a HiveQL.

The only underlying problem could be that the Apache Hive is designed to run on the Apache Hadoop ecosystem. People who are not comfortable using a Linux tree structure based File System or even people who are not likely to use a Linux OS might not like to use Hive.

Apache Hive acts as a hub for information to be stored and smoothly readable + analyzed by BI analysts in order to make wise and data-driven decisions. Users can read, write and manage data, too. This only requires some SQL intermediary knowledge, and we all know learning SQL is quite easy. I do not think of any scenario where Apache Hive would not be appropriate.

Apache Hive is well suited for organizations looking for an initial tool to begin their process of managing their data warehouse as it is open-source and relatively easy to set up. This works well with some legacy systems and many consoles support this. While Hive used to be quite revolutionary, it has fallen behind many other tools that are more performant or specialized for managing DBs, writing queries, and partitioning tables.

It is perfectly suited for analytics.

This is best suited for data analysts and scientists, it's not a programmers tool. You may still need an RDBMS to read data from as updates and deletes can get a bit more complicated, you can run batch jobs, this will have to be facilitated by additional tools.
Its good for fast query processing, for storing large amounts of data.

[Well suited for] Enterprises who want to create data warehouses on top of Hadoop ecosystem for reporting purpose or get summaries or aggregation from big data. In short, if you have implemented Hadoop then you need Hive.

Hive is well-suited for providing an SQL engine on Hadoop, but there are alternative SQL on Hadoop projects that claim to have improvements over Hive.