Azure Data Factory Reviews

We generally implement this product for data transformation for our clients. We create the pipelines and provide training before handing it over to them. We generally deal with large-scale organizations. I'm a senior solutions architect. 

I think the main benefit of this solution is the ease of use, especially for companies that have come from an SSIS type of background where they are used to Microsoft tools. 

If you have a very simple pipeline you can use Data Factory for transformations, but it's really for serious analytics. This is an excellent tool for pipeline orchestration; connecting the different components and activities as well as gathering data. It's an orchestration tool, not a transformation tool. The monitoring feature has drastically improved.

Data Factory is embedded in the new Synapse Analytics. The problem is if you're using the core Data Factory, you can't call a notebook within Synapse. It's possible to call Databricks from Data Factory, but not the Spark notebook and I don't understand the reason for that restriction. To my mind, the solution needs to be more connectable to its own services.

There is a list of features I'd like to see in the next release, most of them related to oversight and security. AWS has a lake builder, which basically enforces the whole oversight concept from the start of your pipeline but unfortunately Microsoft hasn't yet implemented a similar feature.

I've been using this solution for five years. 

From what I've seen this is a stable solution. 

The solution is easy to scale keeping in mind that Data Factory doesn't do any computations. We use it mainly to push the computations to Databricks or Synapse. Projects with our clients generally last a few months and only until they go into production. I believe the ability to increase is always there.

We typically do not use customer support, but there were a few cases several years ago as the product was moving to the cloud that things were not so stable and we contacted support services - they were very good. 

When I first started in this field, everything was basically Hadoop on-premise and Hadoop infrastructure. With the increase in cloud integrations, things have changed. Once the big data services got introduced, we were probably one of the few companies in North America that were actually into analytics and big data and we were the first to implement related Microsoft products in Canada.

The initial setup is straightforward. I'm a huge fan and user of CI/CD pipelines and never do deployments manually. It's all automated and deployment takes a few minutes.

Licensing costs of Data Factory are reasonable. The cost is mainly on the Synapse and Databricks side of things because they are the tools where the computations are done and where you need more nodes and servers.

It's important to study the solution before purchasing it. The problem in this market is that because most users are generally not very knowledgeable, they typically fall for services that are not compatible with their use case. Data Factory comes with all the transformations but that doesn't work for serious analytics customers who generally need to resort to Databricks or Synapse which involves training and education. Since it's a new field and everything has just blasted off, it's very hard for people to catch on.

In my opinion, Airflow still ranks as number one but I would rate Data Factory an eight out of 10. 

Used Azure Data Factory, Data Flow (private preview) and Databricks to develop data integration processes from multiple and varied external software sources to an OLTP application Azure SQL database. The tools are impressively well-integrated, allowing quick development of ETL, big data, data warehousing and machine learning solutions with the flexibility to grow and adapt to changing or enhanced requirements. I can't recommend it highly enough.

This solution will allow the organisation to improve its existing data offerings over time by adding predictive analytics, data sharing via APIs and other enhancements readily. It's as simple as extending the data pipelines with new modules and components. The solution is improving the organisation by offering something the organisation can grow with.

Data Flow and Databricks are going to be extremely valuable services, allowing data solutions to scale as the business grows and new data sources are added.

Data Flow is in the early stages — currently public preview — and it is growing into a tool that will offer everything other ETL tools offer. There are a few features still to come. The thing we missed most was data update, but this is now available as of two weeks ago. A feature that is confirmed as coming soon is the ability to pass in a parameter and filter, etc.

We had an old, traditional data warehouse. We decided to put it into the cloud and we used Azure Data Factory to reform the EEL process from SQL server integration services to extra data.

Azure Data Factory was chosen by the team that I was not on at the time and who decided that this would be the move to the future. So I just went along with it.

In terms of my personal experience, it works fine.

We didn't have a very good experience. The first steps were very easy but it turned out that we used Europe for a Microsoft data center, also partly abroad for our alpha notes. As soon as we started using Azure Data Factory, the bills got higher and higher. At first we couldn't understand why, but it is very expensive to put data into a data center abroad. So instead, we decided to use only Northern Europe, which worked out for a while in the beginning. And then we had nothing to show for it. They gave me a really hard time for this.

Azure Data Factory should be cheaper to move data to a data center abroad for calamities in case of disasters.

What I really miss is the integration of Microsoft TED quality services and Microsoft Data services. If they were to combine those features in Data Factory, I think they would have a very strong proposition. They promise something like that on Microsoft Congress. That was years ago and it's still not here.

I have been using Azure Data Factory for a couple of years now, since about 2017.

Azure Data Factory is a stable solution.

Yes, Azure Data Factory is scalable.

We previously used SSIS because of Microsoft.

The installation is straightforward.

We use data engineers to do the install.

We pay monthly for this.

On a scale of one to ten, I would give Azure Data Factory a seven. Compared to Informatica, it's really crude. I think it's a very crude solution. 

Would I recommend Azure Data Factory? It depends if they need a straight reading in data, then I would say it's perfect. But with Informatica, you can do data storing and data quality checks - there is a lot there than just a data center.

I think Azure Data Factory is a mature product. We used Version One in my project and a lot of it isn't possible on this version. The Version Two is much faster and much better. It's not at the same level as Informatica.

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.

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 primarily use the solution for my small and medium-sized clients.

So far, I'm quite happy with the solution overall. It's got a lot of tools that I use in my work, and these are items I'm already recommending to my clients. I'm quite happy with it.

I'm not sure if I have any complaints about the solution at the moment. There are a few bits and pieces that we would like to see improved. These include improvements related to the solution's ease of use and some quality flash upgrades. However, these are minor complaints. 

If the user interface was more user friendly and there was better error feedback, it would be helpful.

I've been working with the solution for three to four years now.

On the tool itself, we've never experienced any bugs or glitches. There haven't been crashes. Stability has been good.

I haven't had the ability to scale any of my projects personally. I also wouldn't need to scale too high if I did. I'm not sure if I can speak to aspects of scalability as I've never dealt with it.

I've contacted Microsoft technical support in the past. I have to say that I've had a good experience with them. I've been quite satisfied with their level of service.

The initial setup was very straightforward. I wouldn't describe it as complex.

Deployment is fast. It only takes about half an hour at most.

I don't have any information about the pricing. I don't deal with that aspect of the solution.

I typically work with small to medium-sized companies. I'm a consultant, so I give different advice based on what my clients need and what they want to do. However, I would recommend this product. 

I'd rate the solution eight out of ten. All of the issues I have with the solution are very minor, however, it means the solution isn't exactly perfect just yet.

We use Data Factory in our projects, which we deliver to customers. We have almost five implementations in which we're using Data Factory.

It's cloud-based, but there's the integration runtime, which you can connect to on-premises sources. You can transfer data from on-premises to the cloud. We're integrating on-premise data sources to cloud data-based services, like rescue or finance and so on.

Azure Data Factory is very good for enterprise organizations like banks and international insurance companies.

It's cloud-based.

I like the basic features like the data-based pipelines.

We have had some issues, but it's critical to use Data Factory for what it was designed for. It's not very good for iteration processes for loop data activity because you must wait for the runtime. This is a downgrade, but we developed some workaround for it, and we're running the Azure function for these iteration processes.

There's space for improvement in the development process of the data pipelines.

I would rate the stability as nine out of ten, but there are some issues with the responsiveness of the interface.

It's easy to scale.

I would rate the technical support as four out of five.

I'm mostly working with cloud technology, and we use other vendors. On-premises, there is another team in my company that's working with Oracle tools. I'm focusing only on Azure technology, which means that I personally don't have hands-on experience with different tools.

Setup is very straightforward and simple, but it depends on the customers. We have more than 15 data engineers who have data engineer certification from Microsoft. For them, it's a very easy process. 

Of course, there are some issues when configuring the on-premise integration runtime because you have to deal with the network settings on on-premise infrastructure. In terms of ADF as a product, there are very good guides and documentation that you can use to navigate how to solve certain issues.

The price is fair.

I would rate this solution as nine out of ten.

For any customer who needs to transfer or transform the data from sources for analytical purposes, Azure Data Factory is a good product for that.

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.