Microsoft's Azure Data Factory is a service built for all data integration needs and skill levels. It is designed to allow the user to easily construct ETL and ELT processes code-free within the intuitive visual environment, or write one's own code. Visually integrate data sources using more than 80 natively built and maintenance-free connectors at no added cost. Focus on data—the serverless integration service does the rest.
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Dataloader.io
Score 9.0 out of 10
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Dataloader.io delivers a cloud based solution to import and export information from Salesforce.
Well-suited Scenarios for Azure Data Factory (ADF): When an organization has data sources spread across on-premises databases and cloud storage solutions, I think Azure Data Factory is excellent for integrating these sources. Azure Data Factory's integration with Azure Databricks allows it to handle large-scale data transformations effectively, leveraging the power of distributed processing. For regular ETL or ELT processes that need to run at specific intervals (daily, weekly, etc.), I think Azure Data Factory's scheduling capabilities are very handy. Less Appropriate Scenarios for Azure Data Factory: Real-time Data Streaming - Azure Data Factory is primarily batch-oriented. Simple Data Copy Tasks - For straightforward data copy tasks without the need for transformation or complex workflows, in my opinion, using Azure Data Factory might be overkill; simpler tools or scripts could suffice. Advanced Data Science Workflows: While Azure Data Factory can handle data prep and transformation, in my experience, it's not designed for in-depth data science tasks. I think for advanced analytics, machine learning, or statistical modeling, integration with specialized tools would be necessary.
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.
It allows copying data from various types of data sources like on-premise files, Azure Database, Excel, JSON, Azure Synapse, API, etc. to the desired destination.
We can use linked service in multiple pipeline/data load.
It also allows the running of SSIS & SSMS packages which makes it an easy-to-use ETL & ELT tool.
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.
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.
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
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.
We have not had need to engage with Microsoft much on Azure Data Factory, but they have been responsive and helpful when needed. This being said, we have not had a major emergency or outage requiring their intervention. The score of seven is a representation that they have done well for now, but have not proved out their support for a significant issue
Customer support might be where Dataloader.io saves money. Most of the competitors offer 24/7 live support but Dataloader.io only offers support via email and the community. Those types of support work fine until you need an answer right away. Some questions can't wait until the next business day or business hours for a reply.
The easy integration with other Microsoft software as well as high processing speed, very flexible cost, and high level of security of Microsoft Azure products and services stack up against other similar products.
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.
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.