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Top Rated
41 Ratings
7 Ratings
Top Rated
41 Ratings
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Score 8.7 out of 101
7 Ratings
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Score 8.3 out of 101

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Likelihood to Recommend

Anaconda

Anaconda is great for academic and private organizations that cannot afford more expensive Python/R package managers. Also, it is more appropriate for intermediate to advanced Python users--Anaconda can be somewhat frustrating for beginners, as it takes some practice to get comfortable with the workflow. I find it particularly useful for working in teams, because if everyone uses the same package manager, it is easier to troubleshoot issues and makes for reproducible research. For wealthier organizations, a premium package management system (with tech support) would be ideal. Anaconda is also great for people working independently on code development.
Maike Holthuijzen profile photo

Databox

Databox is fantastic for companies or agencies that regularly report on a variety of metrics from multiple data sources. It's ideal for any company that uses a variety of different tools (as long as Databox integrates with them). It's particularly good for HubSpot agencies and companies that use HubSpot. It's maybe not necessary for companies that only use one tool or don't do reporting.
Daniel Berry profile photo

Feature Rating Comparison

Data Exploration

Anaconda
7.5
Databox
Visualization
Anaconda
7.0
Databox
Interactive Data Analysis
Anaconda
8.0
Databox

Data Preparation

Anaconda
7.0
Databox
Interactive Data Cleaning and Enrichment
Anaconda
7.0
Databox
Data Transformations
Anaconda
8.0
Databox
Data Encryption
Anaconda
6.0
Databox
Built-in Processors
Anaconda
7.0
Databox

Platform Data Modeling

Anaconda
5.7
Databox
Automated Machine Learning
Anaconda
4.0
Databox
Single platform for multiple model development
Anaconda
7.0
Databox
Self-Service Model Delivery
Anaconda
6.0
Databox

Model Deployment

Anaconda
4.5
Databox
Flexible Model Publishing Options
Anaconda
5.0
Databox
Security, Governance, and Cost Controls
Anaconda
4.0
Databox

BI Standard Reporting

Anaconda
Databox
7.2
Pixel Perfect reports
Anaconda
Databox
3.0
Customizable dashboards
Anaconda
Databox
9.7
Report Formatting Templates
Anaconda
Databox
9.0

Ad-hoc Reporting

Anaconda
Databox
7.7
Drill-down analysis
Anaconda
Databox
6.7
Formatting capabilities
Anaconda
Databox
7.3
Integration with R or other statistical packages
Anaconda
Databox
7.0
Report sharing and collaboration
Anaconda
Databox
9.7

Report Output and Scheduling

Anaconda
Databox
8.2
Publish to Web
Anaconda
Databox
9.5
Publish to PDF
Anaconda
Databox
8.5
Report Versioning
Anaconda
Databox
5.7
Report Delivery Scheduling
Anaconda
Databox
9.3
Delivery to Remote Servers
Anaconda
Databox
8.0

Data Discovery and Visualization

Anaconda
Databox
8.5
Pre-built visualization formats (heatmaps, scatter plots etc.)
Anaconda
Databox
8.5
Location Analytics / Geographic Visualization
Anaconda
Databox
8.0
Predictive Analytics
Anaconda
Databox
9.0

Pros

  • Package Management. Some packages are difficult to install on different platforms. This is simplified with Anaconda.
  • Dedicated servers. More control over security.
  • Collaboration. Analysts can interact with and checkout notebooks and datasets.
No photo available
  • Combining and integrating with data sources
  • Beautifully displaying metrics
  • Customizing metrics and visualization styles
  • Time range filtering
Daniel Berry profile photo

Cons

  • Requires dedicated administration.
  • Expensive.
  • Removes some control from end-users (analysts).
No photo available
  • It can be very difficult to learn Databox, so definitely account for a learning curve if you make the investment. We've run into several cases where we spend weeks trying to figure out how to report on something, but when we get support to help us they figure it out in like 2 seconds. Tory is awesome!
  • Sometimes when HubSpot makes an update, it breaks our Databoards and then we have to go in and redo everything :(
Stephanie Tran profile photo

Alternatives Considered

Anaconda is way easier to set-up. On Anaconda we have users working on Machine Learning in minutes, where on PyCharm is takes a lot longer to set-up and often involves getting help from IT. PyCharm is easier to integrate with Code repositories (such as GitHub), so if that's very important to you, you might want to take a look at PyCharm
No photo available
HubSpot's reporting tool simply isn't enough. It also doesn't help that the reporting add-on for HubSpot is an additional $200/month, and it only reports on HubSpot stuff. That's why we selected Databox--to report on other platforms like FB ads, Google Analytics, etc. However, we're still in the middle of evaluating Grow as a solution to some of our issues with Databox. They're both VERY different from each other, and we haven't made a decision on Grow yet.
Stephanie Tran profile photo

Return on Investment

  • Centralized repository for all notebooks and analysis projects.
  • Solved for some security concerns from IT.
  • Saves money by avoiding ad-hoc computer resources for analysts.
No photo available
  • Honestly, Databox has been a bit of a pain for us these last few months. There are some things it just can't provide us, and some of our clients don't like it. Sometimes it takes too much effort to create a Databoard than it's worth. And since we're a HubSpot partner agency, we're limited to making smart lists for a lot of what we're looking for.
  • On the other hand, high-level reporting is awesome. And they're always updating the tool with new templates/features to stay ahead of the game.
Stephanie Tran profile photo

Pricing Details

Anaconda

General
Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No
Additional Pricing Details

Databox

General
Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No
Additional Pricing Details