Hive

If you want to move onto another platform, Hive is one platform that offers multiple features. It's an extremely useful tool for teamwork and collaboration, as we can invite other people to take part. The product has really helped us with its automation ability, which allows us to complete tasks on time.

Hive will fit right in to any workflow that manages multiple projects. It's also a great fit for collaboration on a per-project basis as external users can collaborate with paid users. Across a very large team it would take more planning on how to fit multiple workflows and groups into the workspace but it can be done.

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 is great for workflows that need approvals and reviews. Hive allows you to upload files and then review and comment on those files in a sophisticated manner. This is absolutely the number one feature I would say. IF this is not something you need, then a tool like ClickUp might suit you better, as it is more feature rich but has a steeper learning curve at first.

Relying on Hive really permitted us to move swiftly and effectively across a project, which would not have been possible without it due to current remote working models. A lot of our team members work in different cities or sometimes in different countries/timezones so it would have been impossible to have everyone working together in the same room.