TrustRadius Insights for Apache Hive are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.
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.
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.
Pros
Simple query language built on top of Ma reduce paradigm.
Provides parallel execution over distributed system.
Tabular format and connectors available for all cloud platforms.
Cons
Complex joins may take time to execute due to shuffling of data.
Static queries mostly.
Slower than Apache Spark by almost 100 times.
Dependent on external memory and storage to execute.
Likelihood to Recommend
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.
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.
Pros
Querying in Apache Hive is very simple because it is very similar to SQL.
Hive produces good ad hoc queries required for data analysis.
Another advantage of Hive is that it is scalable.
Cons
Apache Hive isn't designed for and doesn't support online processing of data.