Hive vs. ProofHub

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.

Incentivized

For small teams or a freelancer needing to receive work via forms and submit assets for simple yes/no approvals, it may be a great fit. It was just too lacking in the forms and proofing department for what we needed. There was also a lot of workarounds we were going to have to implement to try and achieve our desired workflow.

Incentivized

• 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.

Incentivized

• There are times when we have to make a website for a particular client. And this would involve not only the design, but also the content that would be reflected on the website. For this there has to be complete sync between the design and copywriting department. Earlier, this lead to a lot of goofups due to non-availability of one central medium for collaboration. But now with Proofhub, there’s one place for all people, files, docs and designs related to any active project.

• Organizing tasks by assignees could be better. It's a little cumbersome to check off each person you want. Can you group these?
• I don't really use any view besides task view. Is there something better I could be using?
• It would be nice if attachments showed up in a nicer format, maybe with a preview?

Incentivized

• Well, we are insatiable users, and keep wanting more and more. However, the team at Proofhub itself is always up on breaking the norms and bringing something new every now and then. So, that goes without saying that they would keep re-inventing and improving things with time.

I would surely renew it, as with Proofhub work has become much simpler and easier. Tasks are done faster, files, docs and designs are shared easily and feedback is received as and when required, directly on the file itself

Its a easy tool, the best way to organize the workflow but has room for more improvements.

It's convenient, simple, and clear.

Our CSR is easily accessible and they have support built into the app itself. They also have a pretty robust support site. We also took advantage of the free trial and learned so much by putting Hive through the paces and figuring out the best way to mold it to our needs.

Incentivized

Great support website and had reps follow up multiple times in our trial process. Getting started was very straightforward and adding people is easy too.

Incentivized

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.

Incentivized

We used Basecamp in the past, but we did encounter some problems while working with it. But these concerns have been better addressed in ProofHub. It has a simple interface which is so easy to operate and this was not the case in Basecamp. ProofHub lets us quickly chat with our team members over various work matters through its inbuilt chat feature and resolve them fast while Basecamp provides this feature through a third-party tool which added to our expenses and it was not a very convenient affair. ProofHub has got an inbuilt proofing tool which allows us to get quality feedback over designs instantly saving our time and costs, whereas Basecamp enables proofing through a third party tool which again was not convenient for us. ProofHub’s casper mode feature helps us to protect privacy and secrecy over confidential issues but Basecamp lacks such an important feature. So ProofHub has more to offer and better too in comparison to Basecamp. (answer to Describe how ProofHub stacks up against them and why you selected ProofHub.

• I've gotten to know my colleagues better, knowing their roles makes it faster to contact them to complete tasks and that speed makes us optimize and earn better results
• The jobs speed made us focus on optimization and customization for the client, and that in a better treatment by the client and better revenue
• We can understand which tasks takes more time and to stimate better what we can ask for

Incentivized

• Proofing: We have everything from press releases, social media posts, brochures, ads, etc. that have to be proofed. It has saved us tons of time!
• Project Management: Our team has to schedule fewer meetings about project progress and "where are we at" discussions. Everyone can see it and everyone is accountable. It makes the days more productive by not getting trapped in those meetings.

Incentivized