Easy to Use
Ease of use is always appreciated, and it’s no different here.
Oftentimes, developers of professional tools and systems deprioritize the user experience, and only focus on the offered functionality. That’s when function goes over form, when they both are supposed to be on par with each other.
At Order Group, we put emphasis on a positive user experience, and we like to polish our interfaces. Iza Mrozowska, our Head of Design, is there to make sure everything looks up to par.
On the other hand, using Cognite Data Fusion is quite easy, and technical documentation is there to help you dive into the world of CDF.
Extensive Documentation and Training
Having extensive technical documentation surely helps whenever you’re either starting out or whenever you have an issue. CDF’s documentation gives you information on:
- How to Search for Data
- How to Analyze Data
- CDF’s Canvas
- CDF’s Charts
- Integrations with Grafana, Excel, and Power BI
That’s not it. There is also a whole section dedicated to data engineering, as well as CDF Admin. The depth of articles within ensures users get answers to all the most common questions.
Furthermore, there is also a link to the documentation on Cognite’s Python Industrial Data Science Library.
Flexible Data Extractor Deployment
Data extractors push data in the original format to Cognite Data Fusion. It’s the second step in the pipeline, after data sources, and before the staging area.
There are several pre-built ones that are super easy to deploy. They are often available as a Docker container or even as a Windows .exe file. Pre-built extractors include:
- Cognite DB extractor
- Cognite PI extractor
- Cognite PI AF extractor
- Cognite SAP Extractor
- and more
Publishing docker containers makes deploying an extractor effortless. You may then simply deploy it to Azure, AWS, or Google Cloud in a matter of minutes. It’s also a breeze to deploy it on your infrastructure, should the need arise.
If prebuilt extractors don’t cut it, you may easily build custom ones. Cognite maintains two SDKs: one for Python, and another for .NET. For more information, go here.
More Pros
- Strong Technical Support
- Robust API
