Hive - A great choice of project management tool for distributed teams
December 30, 2022
Hive - A great choice of project management tool for distributed teams
Score 7 out of 10
Vetted Review
Verified User
Overall Satisfaction with Hive
We were using Hive for our project management as it is a great project management tool. These are the following use cases Hive solved for us
- Task tracking: Hive allowed out users to create and track tasks, assign them to team members, and set deadlines.
- Collaboration: Hive includes tools for team communication and collaboration, including a team chat feature, the ability to share files and documents, and the ability to leave comments and feedback on tasks.
- Project planning: Hive offers tools for project planning, including the ability to create and track project timelines, create and assign tasks, and manage resources.
- Integration: Hive can be integrated with a variety of other tools and services, such as Google Calendar, Slack, and Trello, to allow teams to work seamlessly across different platforms.
- Data warehousing: Hive is often used as a data warehousing platform, allowing users to store and analyze large amounts of structured and semi-structured data. It is especially good at handling data that is too large to be stored and analyzed on a single machine, and supports a wide variety of data formats.
- Batch processing: Hive is designed for batch processing of large datasets, making it well-suited for tasks such as data ETL (extract, transform, load), data cleansing, and data aggregation.
- Data transformation: Hive allows users to perform data transformations and manipulations using custom scripts written in Java, Python, or other programming languages. This can be useful for tasks such as data cleansing, data aggregation, and data transformation.
- Integration with other tools: Hive integrates with a wide variety of other tools and services in the Hadoop ecosystem, such as Pig, Spark, and HBase, allowing users to perform a wide range of data analysis and management tasks.
- Real-time queries: Hive is a batch-oriented system, which means that it is designed to process large amounts of data in a batch mode rather than in real-time. While it is possible to use Hive for real-time queries, it may not be the most efficient choice for this type of workload.
- Complex queries: Hive is optimised for simple queries on large datasets, but may struggle with more complex queries or queries that require multiple joins or subqueries.
- Data management: While Hive supports a wide variety of data formats and storage options, it may not offer as many advanced data management features as some other tools, such as data partitioning and indexing.
- Hive has had a positive impact on our business by providing us with such a powerful tool for data analysis and management by taking better data driven decisions
- It helped our business to analyze customer data to better understand customer, identify trends and patterns, and improve customer segmentation. Helped in informed decisions and marketing decisions
- we had a tough time setting up and maintaining a Hadoop cluster system for use with which was complex and time-consuming,
One key difference between Hive and Spark is the way they process data. Hive is a batch-oriented system, which means that it is designed to process large amounts of data in a batch mode rather than in real-time.
In contrast, Spark is a real-time processing platform that is designed to handle streaming data and support interactive queries.
Another difference is the way they execute queries. Hive uses a SQL-like query language called HiveQL, while Spark supports a wide range of languages and APIs, including SQL, Python, Scala, and R.
But we chose Hive due to its simple queries on large datasets and for data warehousing tasks.
In contrast, Spark is a real-time processing platform that is designed to handle streaming data and support interactive queries.
Another difference is the way they execute queries. Hive uses a SQL-like query language called HiveQL, while Spark supports a wide range of languages and APIs, including SQL, Python, Scala, and R.
But we chose Hive due to its simple queries on large datasets and for data warehousing tasks.
Do you think Hive delivers good value for the price?
Yes
Are you happy with Hive's feature set?
Yes
Did Hive live up to sales and marketing promises?
I wasn't involved with the selection/purchase process
Did implementation of Hive go as expected?
I wasn't involved with the implementation phase
Would you buy Hive again?
Yes