Hive

I would say the best area where Hive can and should be implemented is in the time and project tracking systems. What I would not use Hive for is invoice tracking or financial information as pertains to the business. Also, using Hive in replacement for an external messaging system similar to Slack is not advisable. The app and the messenger feature are just not there yet.

I love that my tasks are ordered so that I know one thing depends on another. Like, I am not going to start this task until this other one is done, and Hive lets me know about that.
Sometimes the fact that a task and its subtasks are all over the place on a status view makes my head hurt. It could be a fault of our own organization, but sometimes it's hard to keep track of what is where.

Hive is great for managing projects with your team. Assigning tasks is simple enough using Hive. It helps manage team goals for the projects. We are able to create reports (via the dashboard) for the progress and updates to provide to the team based on completed stages. Works great for bigger projects.

Hive is a powerful tool for data analysis and management that is well-suited for a wide range of scenarios. Here are some specific examples of scenarios where Hive might be particularly well-suited:

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.Simple queries on large datasets: Hive is optimized for simple queries on large datasets, making it a good choice for tasks such as data exploration and summary statistics.

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.

On the other hand, here are some specific examples of scenarios where Hive might be less appropriate:

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 optimized for simple queries on large datasets, but may struggle with more complex queries or queries that require multiple joins or subqueries.Very large datasets: While Hive is designed to scale horizontally and can handle large amounts of data, it may not scale as well as some other tools for very large datasets or complex workloads.

Hive has better road mapping for a product development team that works in unison across different locations. It's easy to keep track of tasks or if someone needs a hand. Through its simplified communication workflow, it's easier to anticipate any roadblocks in the overall project. This service has scope to improve the mobile application support as working with Hive from a mobile device is less than a pleasant experience.