Hive vs. MongoDB

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Hive
Score 8.4 out of 10
N/A
Hive Technology offers their eponymous project management and process management application, providing integrations with many popularly used applications for productivity, cloud storage, and collaboration.
$12
per month per user
MongoDB
Score 8.0 out of 10
N/A
MongoDB is an open source document-oriented database system. It is part of the NoSQL family of database systems. Instead of storing data in tables as is done in a "classical" relational database, MongoDB stores structured data as JSON-like documents with dynamic schemas (MongoDB calls the format BSON), making the integration of data in certain types of applications easier and faster.
$0.10
million reads
Pricing
HiveMongoDB
Editions & Modules
No answers on this topic
Shared
$0
per month
Serverless
$0.10million reads
million reads
Dedicated
$57
per month
Offerings
Pricing Offerings
HiveMongoDB
Free Trial
YesYes
Free/Freemium Version
YesYes
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional DetailsFully managed, global cloud database on AWS, Azure, and GCP
More Pricing Information
Community Pulse
HiveMongoDB
Considered Both Products
Hive

No answer on this topic

MongoDB
Chose MongoDB
We selected MongoDB because of the following
  • Ease of deployment
  • Use and provisioning on their cloud
Top Pros
Top Cons
Features
HiveMongoDB
Project Management
Comparison of Project Management features of Product A and Product B
Hive
7.5
14 Ratings
0% below category average
MongoDB
-
Ratings
Task Management8.314 Ratings00 Ratings
Resource Management7.314 Ratings00 Ratings
Gantt Charts7.713 Ratings00 Ratings
Scheduling7.913 Ratings00 Ratings
Workflow Automation7.513 Ratings00 Ratings
Team Collaboration8.014 Ratings00 Ratings
Support for Agile Methodology8.411 Ratings00 Ratings
Support for Waterfall Methodology7.610 Ratings00 Ratings
Document Management7.112 Ratings00 Ratings
Email integration7.412 Ratings00 Ratings
Mobile Access7.010 Ratings00 Ratings
Timesheet Tracking7.48 Ratings00 Ratings
Change request and Case Management7.010 Ratings00 Ratings
Budget and Expense Management6.68 Ratings00 Ratings
Professional Services Automation
Comparison of Professional Services Automation features of Product A and Product B
Hive
7.2
11 Ratings
2% below category average
MongoDB
-
Ratings
Quotes/estimates6.99 Ratings00 Ratings
Invoicing7.26 Ratings00 Ratings
Project & financial reporting7.89 Ratings00 Ratings
Integration with accounting software6.88 Ratings00 Ratings
NoSQL Databases
Comparison of NoSQL Databases features of Product A and Product B
Hive
-
Ratings
MongoDB
9.1
38 Ratings
3% above category average
Performance00 Ratings9.038 Ratings
Availability00 Ratings9.738 Ratings
Concurrency00 Ratings8.638 Ratings
Security00 Ratings8.638 Ratings
Scalability00 Ratings9.438 Ratings
Data model flexibility00 Ratings9.138 Ratings
Deployment model flexibility00 Ratings9.137 Ratings
Best Alternatives
HiveMongoDB
Small Businesses
Avaza
Avaza
Score 9.7 out of 10
IBM Cloudant
IBM Cloudant
Score 8.1 out of 10
Medium-sized Companies
Todoist
Todoist
Score 9.2 out of 10
IBM Cloudant
IBM Cloudant
Score 8.1 out of 10
Enterprises
Quickbase
Quickbase
Score 9.2 out of 10
IBM Cloudant
IBM Cloudant
Score 8.1 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
HiveMongoDB
Likelihood to Recommend
8.4
(14 ratings)
9.4
(78 ratings)
Likelihood to Renew
-
(0 ratings)
10.0
(67 ratings)
Usability
-
(0 ratings)
9.0
(14 ratings)
Availability
-
(0 ratings)
9.0
(1 ratings)
Support Rating
9.4
(2 ratings)
9.6
(13 ratings)
Implementation Rating
-
(0 ratings)
8.4
(2 ratings)
User Testimonials
HiveMongoDB
Likelihood to Recommend
Hive Technology
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.
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MongoDB
If asked by a colleague I would highly recommend MongoDB. MongoDB provides incredible flexibility and is quick and easy to set up. It also provides extensive documentation which is very useful for someone new to the tool. Though I've used it for years and still referenced the docs often. From my experience and the use cases I've worked on, I'd suggest using it anywhere that needs a fast, efficient storage space for non-relational data. If a relational database is needed then another tool would be more apt.
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Pros
Hive Technology
  • Simplicity, it offers a clean environment without risking the outcome. An example of this are the timesheets that allow a fast way to keep track of progress
  • Interaction, the different options make it faster and easier to interact and collaborate in the development of a product. An example of this would be Hive Notes for meetings
  • The different visualisations it offers allow to explore the best ways to affront your projects. I really like the Gantt mappings view to understand who can be contacted at each point
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MongoDB
  • Being a JSON language optimizes the response time of a query, you can directly build a query logic from the same service
  • You can install a local, database-based environment rather than the non-relational real-time bases such a firebase does not allow, the local environment is paramount since you can work without relying on the internet.
  • Forming collections in Mango is relatively simple, you do not need to know of query to work with it, since it has a simple graphic environment that allows you to manage databases for those who are not experts in console management.
Read full review
Cons
Hive Technology
  • 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?
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MongoDB
  • An aggregate pipeline can be a bit overwhelming as a newcomer.
  • There's still no real concept of joins with references/foreign keys, although the aggregate framework has a feature that is close.
  • Database management/dev ops can still be time-consuming if rolling your own deployments. (Thankfully there are plenty of providers like Compose or even MongoDB's own Atlas that helps take care of the nitty-gritty.
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Likelihood to Renew
Hive Technology
No answers on this topic
MongoDB
I am looking forward to increasing our SaaS subscriptions such that I get to experience global replica sets, working in reads from secondaries, and what not. Can't wait to be able to exploit some of the power that the "Big Boys" use MongoDB for.
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Usability
Hive Technology
No answers on this topic
MongoDB
NoSQL database systems such as MongoDB lack graphical interfaces by default and therefore to improve usability it is necessary to install third-party applications to see more visually the schemas and stored documents. In addition, these tools also allow us to visualize the commands to be executed for each operation.
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Support Rating
Hive Technology
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.
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MongoDB
Finding support from local companies can be difficult. There were times when the local company could not find a solution and we reached a solution by getting support globally. If a good local company is found, it will overcome all your problems with its global support.
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Implementation Rating
Hive Technology
No answers on this topic
MongoDB
While the setup and configuration of MongoDB is pretty straight forward, having a vendor that performs automatic backups and scales the cluster automatically is very convenient. If you do not have a system administrator or DBA familiar with MongoDB on hand, it's a very good idea to use a 3rd party vendor that specializes in MongoDB hosting. The value is very well worth it over hosting it yourself since the cost is often reasonable among providers.
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Alternatives Considered
Hive Technology
Hive is a bit different than Jira and Monday, which I used mostly. Overall does a great job managing project and helps with team communication. Removes dependency of asking team members for updates by going to conference rooms. With Hive, the team updates the status, and we can easily track it.
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MongoDB
We have [measured] the speed in reading/write operations in high load and finally select the winner = MongoDBWe have [not] too much data but in case there will be 10 [times] more we need Cassandra. Cassandra's storage engine provides constant-time writes no matter how big your data set grows. For analytics, MongoDB provides a custom map/reduce implementation; Cassandra provides native Hadoop support.
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Return on Investment
Hive Technology
  • Workflow Management will help you better move your projects along which saves time and money.
  • Time tracking will allow you to better manage the hours and keep your contractors accountable.
  • Overall visibility of projects allow you to keep your margins down and combat "bleeding" and hidden costs or surprises.
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MongoDB
  • Open Source w/ reasonable support costs have a direct, positive impact on the ROI (we moved away from large, monolithic, locked in licensing models)
  • You do have to balance the necessary level of HA & DR with the number of servers required to scale up and scale out. Servers cost money - so DR & HR doesn't come for free (even though it's built into the architecture of MongoDB
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ScreenShots

Hive Screenshots

Screenshot of HIver Technology

MongoDB Screenshots

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