Azure Data Factory Reviews

I am a manager of a team that uses this solution.

Azure Data Factory is primarily used for data integration, which involves moving data from sources into a data lake house called Delta Lake.

It's fairly simple to use. The most valuable feature of this solution would be ease of use.

It does not appear to be as rich as other ETL tools. It has very limited capabilities. It simply moves data around. It's not very good after that because it's taking the data to the next level and modeling it.

I have been working with Azure Data Factory for less than a year.

I would say that we are working with the latest version.

The stability of Azure Data Factory is good. The performance is good.

I haven't had to scale this solution as of yet.

We have six people in our company who use this solution.

Increasing the usage is not on our strategy pathway.

I have not contacted technical support. I have not required any yet.

I have had very little contact with Microsoft support, but it's been good.

I have also worked with Talend. I didn't switch products, but rather companies.

Talend is a more robust enterprise-wide solution that can handle everything from start to finish, whereas Azure Data Factory is more of an ingestion tool.

I was not involved with the initial setup.

We are an enterprise that uses an integrator.

It does not require any maintenance, it's simple.

I don't see a cost; it appears to be included in general support. I have been told that you have to be very careful because it can blow out. I have not experienced it yet, but I've been warned that as Azure ingestion increases, the costs can rise.

In my opinion, the price is competitive.

It's a good tool, a good product that does what it's supposed to do well, which is ingesting data from a source to your target, to another cloud, to another source. Just be conscious to monitor your costs.

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

We are currently using it as an ETL (Extract, Transform, and Load) tool. We are using it to connect to various information providers or, in general, to various sources, to extract data, and then to insert it to our storage devices, databases, or data warehouses.

The two most valuable features of Azure Data Factory are that it's very scalable and that it's also highly reliable.

One area for improvement is documentation. At present, there isn't enough documentation on how to use Azure Data Factory in certain conditions. It would be good to have documentation on the various use cases.

Sometimes, it's really difficult to find the answers to very technical questions regarding certain conditions.

I've been using Azure Data Factory since 2019.

It has been stable so far.  

Azure Data Factory is a very scalable solution. Including internal developers and external consultants working for us, we have about 10-15 people using this solution.

We had been using various ETL tools during the years before moving to the cloud. We picked Azure Data Factory because we were moving towards the Azure cloud.

The initial setup is very easy.

We used a consultant as it was a big project. We had five to six specialists, including both internal and external employees, working on it. It took about about three to six months to complete.

Azure Data Factory is a very easy to use tool. If you want to extract, manipulate, and load data to any type of Azure repository, I recommend this solution. However, I would not recommend it if the manipulation of data is very deep and complicated.

I would rate this solution at eight on a scale from one to ten.

We use Azure Data Factory to connect to clients' on-premise networks and data sources to bring the data into Azure. Additionally, Azure Data Factory orchestrates data movement and transformations. It can connect to a number of different cloud data sources to bring the information into something, such as a data lake or a formal SQL database. Azure Data Factory has the ability to handle large data workloads and can orchestrate them well.

The most valuable features of Azure Data Factory are the flexibility, ability to move data at scale, and the integrations with different Azure components.

Azure Data Factory can improve by having support in the drivers for change data capture.

I have been using Azure Data Factory for approximately three years.

Azure Data Factory is a very reliable and stable solution.

The solution is highly scalable.

The technical support is very good, they are responsive.

We previously use Attunity and we switch to Azure Data Factory mainly because of cost reasons and integration.

The biggest difference between Azure Data Factory and Attunity is Attunitys has the ability to perform change data capture. Whereas Azure Data Factory is more centered around batch or bulk loads.

The initial setup is of a moderate level of difficulty. However, it can be complex. The solution is able to fit both of our use cases.

We normally use one or two people to update and maintain Azure Data Factory.

There's no licensing for Azure Data Factory, they have a consumption payment model. How often you are running the service and how long that service takes to run. The price can be approximately $500 to $1,000 per month but depends on the scaling.

My advice to others that want to implement Azure Data Factory is to use a metadata approach.

I rate Azure Data Factory an eight out of ten.

We use this solution to perform ELTs so that we do not need to keep code within a database.

The overall performance is quite good.

Occasionally, there are problems within Microsoft itself that impact the Data Factory and cause it to fail.

I've worked with this solution for two and a half years.

I wouldn't consider it to be stable since it fails at times.

The solution is scalable.

Support is quite slow and they have bugs that they are unaware of and claim that that is how the system is supposed to work.

My company used Informatica PowerCenter in the past but I was not involved in that.

The initial setup was quick and easy. The whole process took about fifteen minutes. We have about a hundred users at the moment and have plans to increase.

Two of our in-house developers were able to complete the setup.

This solution has good performance but could use better stability. I would rate this a nine out of ten.

My primary use cases for this solution are integration and connecting to the different data stores where we get data and migration activity, deployment, and integrations into using linked services and deployment models.

This solution has allowed me to quickly get analysis, sales data, supply chain data, and eCommerce data.

The most valuable feature of this solution is that it allows more data between on-premises and cloud solutions. It's also useful for orchestration for complex data flows and allows us to do ETL-based transitions heavily. In addition, it allows us to integrate with other third-party systems and compare features and pricing. Other valuable features include database replication, SQL service products, SLA support, data sharing, vendor lock-in, and support for developer tools.

Areas for improvement would be the product's performance and its mapping of data flow. In addition, some of the optimization techniques are not scalable, some naming connections are not supported, and automated testing is not supported in all cases. In the next release, I would like to see support so we can enhance based on the next-level pipelines, writing from scratch, flexible scheduling, and pipeline activity.

I've been using this solution for about a year.

This solution is very reliable.

This solution is scalable.

I am satisfied with the technical support.

I previously worked with Azure SQL database.

The initial setup was complex, but the deployment only took 30 to 40 minutes.

This product is priced at the market standard, which is good given that the product contains all the available assets.

When selecting services, make sure to choose only those you need in order to reduce your costs. I would rate this solution as seven out of ten.

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. 

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.

Our customers use it for data analytics on a large volume of data. So, they're basically bringing data in from multiple sources, and they are doing ETL extraction, transformation, and loading. Then they do initial analytics, populate a data lake, and after that, they take the data from the data lake into more on-premise complex analytics.

Its version depends on a customer's environment. Sometimes, we use the latest version, and sometimes, we use the previous versions.

It is very modular. It works well. We've used Data Factory and then made calls to libraries outside of Data Factory to do things that it wasn't optimized to do, and it worked really well. It is obviously proprietary in regards to Microsoft created it, but it is pretty easy and direct to bring in outside capabilities into Data Factory.

It is very flexible. You can build any features you want.

There is always room to improve. There should be good examples of use that, of course, customers aren't always willing to share. It is Catch-22. It would help the user base if everybody had really good examples of deployments that worked, but when you ask people to put out their good deployments, which also includes me, you usually got, "No, I'm not going to do that." They don't have enough good examples. Microsoft probably just needs to pay one of their partners to build 20 or 30 examples of functional Data Factories and then share them as a user base.

I have been using this solution for the last five years, but probably, the last three years have been significant.

It has been stable. I have not experienced any issues.

It is decent for most things. I'm not sure if it is necessarily intended for large volume and high-speed streams of data. By large, I mean really big, but for pretty much anything that most users would want to do, including ourselves, it is fine. Our clients are large government organizations.

It scales fine within its environment. You can literally throw another Data Factory in or replicate one and do things pretty quickly. So, it is not at all hard to increase your processing footprint, but you have to pay for it. It doesn't end up being quite expensive. Although I haven't really done it, I would suspect that if I did the equivalent in AWS, Azure would be more expensive than AWS because of the way they price data.

They're all right. I would rate them a seven out of 10. They do fine, but there is a lot that they don't do.

I'm not sure if even Microsoft has enough SMEs from a user point of view. They are helpful for getting it set up, making it work, and helping you figure out why it doesn't work. If you want to ask them about something that you are trying to do, they'll try to direct you to a partner, which is fine, but the partners also don't necessarily have an experience. It is Catch-22. There aren't a lot of people out there with Azure experience because Azure started to be in demand only over the last two years.

The customer used a lot of homebrew stuff. They were doing a lot of internal stuff and some Oracle stuff. They were doing things, and they made a workaround and said, "Okay, we'll bring it into Oracle Database, and then we'll do all these things to it." We're like, "Okay, that works, but then you're taking it out of that database and putting it over into the data lake. I don't understand why are you doing that?" That's what they were doing.

It is pretty straightforward. Devil is in the details, but you can easily get up and running in a day with Data Factory. Anybody who is comfortable in Azure can set up Data Factory, but it takes experience to know what it can and can't do or should and shouldn't do.

It is proven, and it works. Make sure you have a well-defined use case and build a quick prototype to ensure that it, in fact, does what you need. Give yourself some benchmarks. That's exactly what we did. We defined the use case, and then we set up Data Factory. We found a couple of things that it didn't do. We figured out a way to work around those things and have it do those things. After that, we confirmed it. It is operational, and it is doing its job. It has been pretty much error-free since then.

It would become easier to use as more people become Azure-capable. If I want to find an AWS SME, I can get tons. They're expensive, but I have them. If I want to find an Azure SME, I usually have to create them. Azure was later to market than AWS. So, there are fewer people who are experts in Azure, and they are in high demand.

I would rate Azure Data Factory a nine out of 10. They just don't have enough good examples out there of things.