Azure Data Factory Reviews

We use the solution to move data from on-premises to the cloud.

The tool’s workflow is not user-friendly. It should also improve its orchestration monitoring.

I have been using the product since 2019.

The tool is a stable product.

I would rate the solution a seven out of ten.

Our primary use case is mainly for ETL and transforming the data and then using it for power VA. So there we are handling multiple projects. It is not just a single thing, but mainly used at the end is data analytics only.

I would like to see this time travel feature in Snowflake added to Azure Data Factory. In addition to taking care of data internally, how it is instead of using indexing, they have some different mechanisms to quickly execute the query instead of normal indexes. Both of these would be great to see implemented in future upgrades.

I have been working with Azure Data Factory for the past twelve months.

I am one hundred percent happy with the stability.

Technical support is excellent.

Positive

I would rate Azure Data Factory an eight on a scale of one to ten.

I like Azure Data Factory, it works quite well.

It is always effective. It's extremely consistent.

The only thing I wish it had was real-time replication when replicating data over, rather than just allowing you to drop all the data and replace it. It would be beneficial if you could replicate it.

Real-time replication is required, and this is not a simple task.

We are using the latest version of Azure Data Factory.

I have no issues with the stability of the Azure Data Factory. We have not had any glitches.

Azure Data Factory is extremely simple to scale.

This solution is used by a dozen people in our company.

I have not contacted technical support. We haven't needed the assistance of technical support.

We use Microsoft products such as Azure Databricks.

Azure Data Factory is the only solution that I have used since I started with this company.

The initial setup is pretty straightforward. However, there is a bit of a learning curve, but once you get it, it's pretty simple.

It's not particularly expensive.

It works very well.

I would rate Azure Data Factory an eight out of ten.

In my new organization, we are currently analyzing this solution. In my previous organization, I have worked with Azure Data Factory and Databricks.

It is deployed in a virtual private cloud.

Its integrability with the rest of the activities on Azure is most valuable.

It is also easier to work with. It has a nice user interface that would seem very familiar to some of the people who are coming from Microsoft SSIS and other similar kinds of activities. It makes it easy for them to pick it up quickly.

It can improve from the perspective of active logging. It can provide active logging information.

It should provide support for changing data capture on several other platforms.

I have been using this solution for two years.

It is scalable. From the security perspective, it depends on how we implement it. It depends on the organization, but I don't see a challenge. 

In my previous organization, we had close to a hundred people.

Their technical support is very good.

It was very easy.

I would generally recommend this solution if you have an Azure setup or Azure as a baseline.

I would rate Azure Data Factory an eight out of 10.

We use the solution to gather data from four separate operational systems.

The data copy template is a valuable feature. With the pipeline template, it takes only a few clicks for the on-premises data to come in. 

There is no built-in function for automatically adding notifications concerning the progress or outline of a pipeline run. I believe this requires a bit of development through power ups, but it would be nice to have a task for firing off an email through the GUI.

I have been using Azure Data Factory for around six months.

When it comes to stability, the solution is similar to the previous Azure Synapse Analytics. They are both very easy.

When it comes to scalability, the solution is similar to the previous Azure Synapse Analytics. They are both very easy.

The licensing cost is included in the Synapse.

I rate Azure Data Factory as an eight out of ten. 

Our primary use case is data loading.

The most valuable features are data transformations.

The speed and performance can be further improved.

This solution should be able to connect with custom APIs.

We have been using this solution for one or two months.

The stability of the Azure Data Factory is very good.

The scalability is good. We have about five users for this solution and they are all developers.

The technical support for this solution is good.

We did not use another solution prior to this one.

The initial setup of the Azure Data Factory was simple. It took us one week to deploy it.

We deployed this solution with our in-house team.

Good

Our licensing fees are approximately 15,000 ($150 USD) per month. There are no additional fees.

We evaluated a solution by Talend before selecting the Azure Data Factory.

My advice for anybody who is implementing this solution is to first get in touch with the Microsoft team to get their support so that you don't spend too much on research.

I would rate this solution a eight out of ten.

We primarily used this solution for getting data from a client's server, or online data, to an Azure Data Lake. We create pipelines to orchestrate the data flow from source to target.

The most valuable feature is the copy activity. Instead of creating a pipeline and then doing a source to target copy, this feature will create the pipeline directly.

Snowflake connectivity was recently added and if the vendor provided some videos on how to create data then that would be helpful. I think that everything is there, but we need more tutorials.

We have been working with Azure Data Factory for the past two years.

Occasionally, there are timeout issues that happen when connecting to the source or the target. Normally, you just hit refresh and it will connect properly.

Being a cloud product, it can scale as much as we need.

In my project, there are ten people using this product.

I have been in contact with technical support and it's good, as long as you directly connect with the appropriate technical person right away. When you raise an issue, sometimes the people who are available are unfamiliar with that particular technology, so they have to route the issue to the concerned person. This routing takes at least one or two days, so if you can directly get the corresponding person allocated to the ticket, that would be great. 

The initial setup is simple and it only takes perhaps five minutes to deploy.

When one of our developers wants to use it, they can create their own jobs.

Our deployment was completed by an in-house team.

No maintenance is required and when we experience problems, we normally solve them on our own.

The licensing is a pay-as-you-go model, where you pay for what you consume.

If you have Azure as a cloud service and you want to perform ETL then Azure Data Factory is a product that I can recommend.

I would rate this solution a six out of ten.

Azure Data Factory is for data transformation and data loading. It works from your transaction systems, and we are using it for our HRMS, Human Resource Capital Management System. It picks up all the transactional data pick and moves into the Azure Data Warehouse. From there, we would like to create reports in terms of our financial positions and our resource utilization project. These are the reports that we need to build onto the warehouse.

The purpose of Azure Data Factory is more about transformations, so it doesn't need to have a good dashboard. But, it has a feeding user interface for us to do our activities and debug actions. I think that's good enough.

Azure Data Factory's most valuable features are the packages and the data transformation that it allows us to do, which is more drag and drop, or a visual interface. So, that eases the entire process.

Azure Data Factory setup is quite user-friendly.

I am happy with the interface.

We are too early into the entire cycle for us to really comment on what problems we face. We're mostly using it for transformations, like ETL tasks. I think we are comfortable with the facts or the facts setting. But for other parts, it is too early to comment on.

We are still in the development phase, testing it on a very small set of data, maybe then the neatest four or bigger set of data. Then, you might get some pain points once we put it in place and run it. That's when it will be more effective for me to answer that.

We are building Azure Data Factory right now internally to extract data from our transactional systems and put them into the warehouse so that the reporting engine can be built too.

We have not tried it scaling up. But, Azure promises the stability and scalability should not be an issue.

From a development perspective, I think there were four developers who use Azure Data Factory. From a warehouse perspective, once we roll out the reports out, it should be used by at least 40 or 50 people minimum.

Generally, the documentation is pretty decent. All the issues that come up are here in the documentation part. We've not really had to go to Microsoft as of now from a support perspective. The documentation and the support that we get over the internet is quite good.

The initial setup was very straightforward.

The initial setup was quite quick, nothing much to do. Now, we are more developing the use cases. A use case with data generally takes around four or five days a use case because it will start right from identifying the right field, getting the data, transforming it, and finalizing the warehouse structure. That makes a bit of a thing, but it's pretty straightforward.

We are a technical team so we implemented it in-house.

It's a pay-as-you-go module. I'm not very sure about cost because our usage currently is very low. But, I feel that if the usage extends beyond a certain threshold, it will start getting expensive.

It depends what the threshold is. I see we're not at that threshold right now, so it's pretty decent right now.

We were looking at certain other projects and products. For example, we were looking at Snowflake that has a data warehouse. But the project wasn't working. That's why we selected Azure. The primary reason is the skills are very easily available for Azure. The second is from our strategy perspective, because we were trying to be a Microsoft shop it fits into our strategy. That's all.

If you're a Microsoft shop, if you want to get there easily, I think Azure is one of the better choices. Otherwise, other tools generally require specialized skills and specialized partners to come and implement it. Once implemented, then it becomes much easier to install.

I can't comment right now. I've not talked to it in that fashion. Whatever was required by us, business users have been satisfied in the Data Factory setup.

On a scale of one to ten, I would give Azure Data Factory an eight.