Dataloader.io Review
Use Cases and Deployment Scope
I’m importing new data for systems that aren’t integrated with Salesforce. If the company isn’t interested in spending additional funds for something that has an open-facing API to connect to Salesforce natively, then I have to manually ingest that data somewhere and use Dataloader.io to get it into Salesforce.
I can’t just have it connected and free-flowing — it has to be something I’m manually doing. And since I’m dealing with hundreds of thousands of records, Dataloader.io is really the only option.
Salesforce does have an in-app offering, but I think it only handles about a thousand records a day, so that doesn’t work for me.
Also, I’m using Dataloader.io because we store a lot of files that become irrelevant over time. We want to delete and remove those after a certain period — some after three years, some after two. So it’s much easier to do a running export of everything there and then perform an import as a delete using those ID numbers.
Pros
- It does things very fast and it allows me for easier field mapping.
Cons
- It is very glitchy. For example, windows 11 just came out and it broke everything, so we had to reinstall a new version and it only works for me, it's not working for my employees. But yet we all have Windows 11 machines, same machine, same installation. So sometimes it's glitchy, we'll log in, it'll say we're already logged in, it just goes through a loop and doesn't do it. But when it works, it works. It works more often than it doesn't.
Likelihood to Recommend
Replacing data. If we've put something in a category or a bucket that is no longer named that anymore because we've evolved with the times and we want to rebrand everything, it makes it way easier to do a quick import with the new terms.
