Hive Technology offers their eponymous project management and process management application, providing integrations with many popularly used applications for productivity, cloud storage, and collaboration.
$24
per month per user
Tableau Desktop
Score 8.3 out of 10
N/A
Tableau Desktop is a data visualization product from Tableau. It connects to a variety of data sources for combining disparate data sources without coding. It provides tools for discovering patterns and insights, data calculations, forecasts, and statistical summaries and visual storytelling.
$75
per month
Pricing
Hive
Tableau Desktop
Editions & Modules
Free
$0
Lite
$24
per month per user
Growth
$34
per month per user
Pro
$59
per month per user
Elite
Contact Sales
Tableau
$75
per month per user
Tableau Enterprise
$115
per month per user
Offerings
Pricing Offerings
Hive
Tableau Desktop
Free Trial
Yes
No
Free/Freemium Version
Yes
No
Premium Consulting/Integration Services
No
Yes
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
A discount is offered for annual pricing.
All pricing plans are billed annually.
More Pricing Information
Community Pulse
Hive
Tableau Desktop
Features
Hive
Tableau Desktop
Project Management
Comparison of Project Management features of Product A and Product B
Hive
9.0
15 Ratings
15% above category average
Tableau Desktop
-
Ratings
Task Management
9.015 Ratings
00 Ratings
Resource Management
8.915 Ratings
00 Ratings
Gantt Charts
9.914 Ratings
00 Ratings
Scheduling
7.014 Ratings
00 Ratings
Workflow Automation
9.014 Ratings
00 Ratings
Team Collaboration
9.915 Ratings
00 Ratings
Support for Agile Methodology
10.012 Ratings
00 Ratings
Support for Waterfall Methodology
8.011 Ratings
00 Ratings
Document Management
9.913 Ratings
00 Ratings
Email integration
9.913 Ratings
00 Ratings
Mobile Access
8.011 Ratings
00 Ratings
Timesheet Tracking
10.09 Ratings
00 Ratings
Change request and Case Management
9.911 Ratings
00 Ratings
Budget and Expense Management
7.09 Ratings
00 Ratings
Professional Services Automation
Comparison of Professional Services Automation features of Product A and Product B
Hive
7.0
12 Ratings
10% below category average
Tableau Desktop
-
Ratings
Quotes/estimates
7.010 Ratings
00 Ratings
Invoicing
7.07 Ratings
00 Ratings
Project & financial reporting
7.010 Ratings
00 Ratings
Integration with accounting software
7.09 Ratings
00 Ratings
BI Standard Reporting
Comparison of BI Standard Reporting features of Product A and Product B
Hive
-
Ratings
Tableau Desktop
8.4
175 Ratings
3% above category average
Pixel Perfect reports
00 Ratings
8.1145 Ratings
Customizable dashboards
00 Ratings
9.1174 Ratings
Report Formatting Templates
00 Ratings
8.1151 Ratings
Ad-hoc Reporting
Comparison of Ad-hoc Reporting features of Product A and Product B
Hive
-
Ratings
Tableau Desktop
8.3
172 Ratings
3% above category average
Drill-down analysis
00 Ratings
8.5167 Ratings
Formatting capabilities
00 Ratings
8.4170 Ratings
Integration with R or other statistical packages
00 Ratings
8.0126 Ratings
Report sharing and collaboration
00 Ratings
8.5165 Ratings
Report Output and Scheduling
Comparison of Report Output and Scheduling features of Product A and Product B
Hive
-
Ratings
Tableau Desktop
8.3
166 Ratings
1% above category average
Publish to Web
00 Ratings
8.0155 Ratings
Publish to PDF
00 Ratings
8.0154 Ratings
Report Versioning
00 Ratings
8.3120 Ratings
Report Delivery Scheduling
00 Ratings
8.6128 Ratings
Delivery to Remote Servers
00 Ratings
8.778 Ratings
Data Discovery and Visualization
Comparison of Data Discovery and Visualization features of Product A and Product B
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.
The best scenario is definitely to collect data from several sources and create dedicated dashboards for specific recipients. However, I miss the possibility of explaining these reports in more detail. Sometimes, we order a report, and after half a year, we don't remember the meaning of some data (I know it's our fault as an organization, but the tool could force better practices).
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
An excellent tool for data visualization, it presents information in an appealing visual format—an exceptional platform for storing and analyzing data in any size organization.
Through interactive parameters, it enables real-time interaction with the user and is easy to learn and get support from the community.
Our use of Tableau Desktop is still fairly low, and will continue over time. The only real concern is around cost of the licenses, and I have mentioned this to Tableau and fully expect the development of more sensible models for our industry. This will remove any impediment to expansion of our use.
Tableau Desktop has proven to be a lifesaver in many situations. Once we've completed the initial setup, it's simple to use. It has all of the features we need to quickly and efficiently synthesize our data. Tableau Desktop has advanced capabilities to improve our company's data structure and enable self-service for our employees.
When used as a stand-alone tool, Tableau Desktop has unlimited uptime, which is always nice. When used in conjunction with Tableau Server, this tool has as much uptime as your server admins are willing to give it. All in all, I've never had an issue with Tableau's availability.
Tableau Desktop's performance is solid. You can really dig into a large dataset in the form of a spreadsheet, and it exhibits similarly good performance when accessing a moderately sized Oracle database. I noticed that with Tableau Desktop 9.3, the performance using a spreadsheet started to slow around 75K rows by about 60 columns. This was easily remedied by creating an extract and pushing it to Tableau Server, where performance went to lightning fast
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.
Tableau support has been extremely responsive and willing to help with all of our requests. They have assisted with creating advanced analysis and many different types of custom icons, data formatting, formulas, and actions embedded into graphs. Tableau offers a weekly presentation of features and assists with internal company projects.
It is admittedly hard to train a group of people with disparate levels of ability coming in, but the software is so easy to use that this is not a huge problem; anyone who can follow simple instructions can catch up pretty quickly.
I think the training was good overall, but it was maybe stating the obvious things that a tech savvy young engineer would be able to pick up themselves too. However, the example work books were good and Tableau web community has helped me with many problems
Again, training is the key and the company provides a lot of example videos that will help users discover use cases that will greatly assist their creation of original visualizations. As with any new software tool, productivity will decline for a period. In the case of Tableau, the decline period is short and the later gains are well worth it.
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.
I have used Power BI as well, the pricing is better, and also training costs or certifications are not that high. Since there is python integration in Power BI where I can use data cleaning and visualizing libraries and also some machine learning models. I can import my python scripts and create a visualization on processed data.
Tableau Desktop's scaleability is really limited to the scale of your back-end data systems. If you want to pull down an extract and work quickly in-memory, in my application it scaled to a few tens of millions of rows using the in-memory engine. But it's really only limited by your back-end data store if you have or are willing to invest in an optimized SQL store or purpose-built query engine like Veritca or Netezza or something similar.
Tableau was acquired years ago, and has provided good value with the content created.
Ongoing maintenance costs for the platform, both to maintain desktop and server licensing has made the continuing value questionable when compared to other offerings in the marketplace.
Users have largely been satisfied with the content, but not with the overall performance. This is due to a combination of factors including the performance of the Tableau engines as well as development deficiencies.