Dataloader.io vs Matillion

Dataloader is an incredible asset for a large organization or an organization that has a robust Salesforce environment. Specifically, Dataloader has allowed our sales team to focus on driving sales while our operations team can load the data they need in a manner that allows for robust reporting and tracking on our sales process. Organizations with less robust Salesforce environments or Salesforce environments in which many people are expected to maintain their own information likely do not need Dataloader.

I always suggest Matillion for organizations that do not want to spend money and time developing their own ETL tool, Matillion is a mature one and meet all requirements about it. It is important to check if you have any edge case and have a look if it is covered by Matillion or not.

• Extracting Salesforce attachments in original file format! I do not know of a tool that can do this better, or more efficiently! This is a huge benefit to companies that would like to extract attachments from Salesforce for tasks like data migrations.
• Cross-object data extract within one file. You can pull data from related objects as long as there is a populated lookup from the object you are extracting, to another object (Child or Parent).
• UI is simple and requires very little to no training. Given the acquisition of Mulesoft by Salesforce, I would not be surprised if DataLoader.IO is rolled out as the new global data loading tool for Salesforce.

• Extremely user-friendly workflow orchestration between multiple languages such as SQL, Python, bash, and various API connectors
• Salesforce connectors to pull and push data between systems save us a ton of time
• Matillion Exchange workflows allow for easy sharing of templated best practice transformation jobs with ease
• Very responsive support

• At the moment, I can't find a way to rename jobs. This would be useful to organize what was previously created hastily by techs in a rush.
• A preview of the job, especially upserts, would take a great deal of stress away from some of us (especially those who are not so confident in their ETL practice).
• A native vlookup equivalent may be a welcome addition.

• Passing variables between jobs (orchestration or transformation) feels a bit clunky. It can also be frustrating that you can't pass a variable back up to the calling orchestration job, you can only pass it down to child jobs.
• It would be great to have some kind of debug mode, through which you're able to 'step through' the various tasks in an orchestration/transformation job.
• Matillion's generic API functionality is difficult to understand. Things like handling pagination and rate limiting are complex. Although I understand improvements have been made in recent versions.

It is easy to use and doesn't require a security token, so I enjoy using it. It also doesn't require any download or installation, which is sometimes a blocker to gettingthings done if the company has limits. also, the dataloader.io is easy for other people to pick up, so others can have visibility into the data jobs that have occurred

With the current experience of Matillion, we are likely to renew with the current feature option but will also look for improvement in various areas including scalability and dependability. 1. Connectors: It offers various connectors option but isn't full proof which we will be looking forward as we grow. 2. Scalability: As usage increase, we want Matillion system to be more stable.

It is an intuitive application to use. Within a few clicks, you can be signed in to your org and ready to perform tasks. Data imports/exports/updates are streamlined so you can quickly start and configure your jobs. These can run in the background while you set up new tasks. Job history and tasks currently running on are on your home screen.

Pretty good UI, especially for a completely web-based system. Source control integration is a bit clunky at times, but overall makes code versioning and branching much more simple, since it's largely within the same context as the rest of the code. Finding some core settings can be difficult - they're just not where one would expect them to be.

Dataloader is made for updating, inserting, and deleting of contacts. For these operations this tool is excellent.

I always feel like there is room for improvement, so a 10/10 in my book is not possible. Due to the easy-to-learn trail on Salesforce Trailhead for exporting, importing, etc. I find it exactly what any customer may need for their data management and integrity. This tool saves time, resources, and has helped our organization take a step forward in the right direction.

Overall, I've found Matillion to be responsive and considerate. I feel like they value us as a customer even when I know they have customers who spend more on the product than we do. That speaks to a motive higher than money. They want to make a good product and a good experience for their customers. If I have any complaint, it's that support sometimes feels community-oriented. It isn't always immediately clear to me that my support requests are going to a support engineer and not to the community at large. Usually, though, after a bit of conversation, it's clear that Matillion is watching and responding. And responses are generally quick in coming.

Not really a part of the implementation to give fare insight.

I have used salesforce inspector also for operations like import and export of data from custom objects but it doesn't work well when you have data in huge numbers. Instead of using Salesforce Inspector, one should go for Dataloader.io if the number of records is huge to be dealt with.

We used Airflow for a year before switching to Matillion. We switched to Matillion because the Airflow servers were not stable, and we didn't have any in-house expertise that could manage the Linux OS which Airflow is built on. We were constantly frustrated by the fact that we had to wait for a consultant whenever we wanted to manage one of the servers, or if something was down we had to wait for someone to fix it. Also, Airflow doesn't have a GUI that is drag and drop. It is a lot more work to manage the flow of the scripts in Airflow, and requires a lot more explanation, especially for an entry level data engineer. There is a lot more coding in Airflow, which is fine if you are a good coder. However, Matillion offers a bigger tool set for those who just want to focus on building an ETL pipeline, and don't care to do a lot of coding.

The product scales great for small to medium size teams or groups. For groups larger it becomes more challenging and requires more individual instances depending on the volume of running jobs and concurrent users - it handles 10's of concurrent users and jobs very well but much larger than that would be extremely difficult on the largest instance or cluster that is available at this time.

• HUGE time saving. When we need to clean or review data, we used to have to do it line by line. This can do the work within excel and make cleanup/management an afternoons work as opposed to a week.
• Rollback what you did/change/deleted is relatively simple if you remember to back up the data you are manipulating.

• Matillion had had a positive impact on our ability to scale up our ETL process without increasing our headcount. We've been able to double the number of jobs we have in the last year.
• A positive impact on the amount of time we need to investigate failures. Coming from other tools that had high rates of failures (say 2 jobs failing a week), we have not seen as many technical failures from Matillion. This reduces the number of resources we focus on tracking down failures, fixing, and re-running jobs by 25%.