Azure Data Factory Reviews

We created data ingestion solutions. We have built interpreters, and we have data factories that pull data from our clients. They submit data via Excel spreadsheets, and we process them into a common homogeneous format.

It has helped with some automation. Instead of individual people reviewing these files, we were able to automate the ingestion process, which saved a bunch of time. It saved hours of repeated manual work.

The data mapping and the ability to systematically derive data are nice features. It worked really well for the solution we had. It is visual, and it did the transformation as we wanted.

I couldn't quite grasp it at first because it has a Microsoft footprint on it. Some of the nomenclature around sync and other things is based on how SSRS or SSIS works, which works fine if you know these products. I didn't know them. So, some of the language and some of the settings were obtuse for me to use. It could be a little difficult if you're coming from the Java or AWS platform, but if you are coming from a Microsoft background, it would be very familiar.

For some of the data, there were some issues with data mapping. Some of the error messages were a little bit foggy. There could be more of a quick start guide or some inline examples. The documentation could be better.

There were some latency and performance issues. The processing time took slightly longer than I was hoping for. I wasn't sure if that was a licensing issue or construction of how we did the product. It wasn't super clear to me why and how those occurred. There was think time between steps. I am not sure if they can reduce the latency there. 

I have been using this solution for a year and a half.

It is very stable.

It is very scalable. It is a cloud product. It is being used by business analysts, business managers, and Azure cloud architects. We have just one developer/integrator for deployment and maintenance purposes.

We have plans to increase its usage. We'll be rolling it out for other clients.

Microsoft has these things well-documented. There were videos. I was able to find answers when I needed them. To the uninitiated, it was a little difficult, but we got there.

It was of medium complexity. Because it goes to the cloud, the duration was short. The deployment was minutes and hours.

We are a consultant and integrator. You can use our company for its implementation.

I would rate this solution a nine out of ten.

We use Azure Data Factory for data transformation, normalization, bulk uploads, data stores, and other ETL-related tasks.

Azure Data Factory allows us to create data analytic stores in a secure manner, run machine learning on our data, and easily adapt to changing schema.

The most valuable feature of Azure Data Factory is that it has a good combination of flexibility, fine-tuning, automation, and good monitoring.

The documentation could be improved. They require more detailed error reporting, data normalization tools, easier connectivity to other services, more data services, and greater compatibility with other commonly used schemas.

I would like to see a better understanding of other common schemas, as well as a simplification of some of the more complex data normalization and standardization issues.

It would be helpful to have visibility, or better debugging, and see parts of the process as they cycle through, to get a better sense of what is and isn't working.

It's essentially just a black box. There is some monitoring that can be done, but when something goes wrong, even simple fixes are difficult to troubleshoot.

I have been working with Azure Data Factory for a couple of years.

There is only one version.

Overall, I believe the stability has been good, but there have been a couple of occasions when Microsoft's resources needed to be allocated were overburdened, and we had to wait for unacceptable amounts of time to get our slot. It has now happened twice which is not ideal.

There is no limit to scalability.

We only have a few users. One is a data scientist, and the other is a data analyst.

We use it to push up various dashboards and reports, it's a transitional product for transferring, transforming, and transitioning data.

It is extensively used, and we intend to expand our use.

You don't really get that kind of support; it's more about documentation and the community support that is available. I would rate it a three out of five compared to others.

You could call them, and pay for their consulting hours directly, but for the most part, we try to figure it out or look through documentation. 

I think their documentation is lagging because it's not as popular of a tool, there's just not a lot, or as much to fall back on.

Neutral

We had only our own tools, and we switched because you get to leverage all of the work done in a SaaS or platform as a service, or however they classify it. As a result, you get more functionality, faster, for less money.

The initial setup is straightforward.

It is a working tool. You can start using it within an hour and then make changes as needed.

We only need one person to maintain the solution; it doesn't take much to keep it running.

It's not a problem; it's a platform.

We completed the deployment ourselves.

We have seen a return on investment. I can't really share many details, but for us, this becomes something that we sell back to our clients.

You pay based on your workload. Depending on how much data you process through it, the cost could range from a few hundred dollars to tens of thousands of dollars.

Pricing is comparable, it's somewhere in the middle.

There are no additional fees to the standard licensing fee.

We looked at some other tools, such as Databricks, AmazonGlue, and MuleSoft.

We already had most of our infrastructure connected to Azure in some way. So the integration of where our data resided appeared to be simpler and safer.

I believe it would be beneficial if they could find someone experienced in some of the tools that are a part of this, such as Spark, not necessarily Data Factory specifically, but some of those other tools that will be very familiar and have a very quick time for productivity. If you're used to doing things in a different way, it may take some time because there isn't as much documentation and community support as there is for some more popular tools.

I would rate Azure Data Factory a seven out of ten.

As a management consultancy company, we help our clients deploy Azure Data Factory or any other cloud-based solution depending on data integration needs. Regarding how we use Azure Data Factory within our company, we are on the Microsoft Stack, so we use the solution primarily for data warehousing and integration.

The feature I found most helpful in Azure Data Factory is the pipeline feature, including being able to connect to different sources.

I also found running Python codes whenever you need to valuable in Azure Data Factory, especially for certain features of the solution, such as data integrations, aggregations, and manipulations.

Azure Data Factory also has built-in security, which is another valuable feature.

I also like that you get access to the whole Azure suite through Azure Data Factory, so the overall architecture design, defining security and access, role-based access management, etc. It's helpful to have the whole suite when designing applications.

Areas for improvement in Azure Data Factory include connectivity and integration.

When you use integration runtime, whenever there's a failure, the backup process in Azure Data Factory takes time, so this is another area for improvement.

Database support in the solution also has room for improvement because Azure Data Factory only currently supports MS SQL and Postgres. I want to see it supporting other databases.

If you want to connect the solution from on-premises to the cloud, you will have to go with a VPN or a pretty expensive route connection. A VPN connection might not work most of the time because you have to download a client and install it, so an interim solution for secure access from on-premise locations to the cloud is what I want to see in Azure Data Factory.

I've been using Azure Data Factory for about a year now.

Azure Data Factory is very stable, so it's a four out of five for me. In some instances, the solution failed, but I wouldn't wholly blame Azure Data Factory because my company connected to some on-premise databases in some cases. Sometimes, you'll get errors from self-hosted integration, faulty connections, or the on-premise server is down, so my rating for stability is a four.

Scalability-wise, Azure Data Factory is a four out of five because Microsoft is still developing certain tiers, which means you can't upgrade an older skill or tier. In contrast, the more modern, newer tiers could be upgraded easily. Rarely will you get stuck in one platform where you have completely destroyed that container and then fit a new container. Most of the time, Azure Data Factory is pretty easy to scale.

We haven't used Microsoft support directly because whenever we have issues with Azure Data Factory, we can find resolutions through their online documentation.

We're using both Azure Data Factory and SSIS.

We had several in-house solutions, but we moved to Azure Data Factory because it was straightforward. From a deployment standpoint, the solution comes with different services, so we didn't have to worry about separate hardware or infrastructure for networking, security, etc.

The initial setup for Azure Data Factory was easy, so I'd rate the setup a four out of five.

The implementation strategy was looking into what my organization needed overall, then planning and direct deployment. My company first did a test, a dummy version, then a UAT with stakeholders before going into production.

It took about two months to complete the deployment for Azure Data Factory.

An in-house team, the digital data engineering team, deployed Azure Data Factory.

We're still computing the ROI from Azure Data Factory. It's too early to comment on that.

My company is on a monthly subscription for Azure Data Factory, but it's more of a pay-as-you-go model where your monthly invoice depends on how many resources you use.

On a scale of one to five, pricing for Azure Data Factory is a four.

It's just the usage fees my company pays monthly. No support fees because my company didn't need support from Microsoft.

If you're not using core Microsoft products, the cost could be slightly higher, for example, when using a Postgres database versus an MS SQL database.

My company uses Azure Data Factory, SSIS, and for a few other instances, Salesforce.

My company currently has about fifty Azure Data Factory users, but not directly exposed to the solution compared to the developers; for example, members of corporate management and other teams apart from the development team are exposed to Azure Data Factory.

Shortly, there could be about two hundred users of Azure Data Factory within the company.

The developer team working directly on Azure Data Factory comprises ten individuals.

For the maintenance of the solution, my company has two to three staff, but it could reach up to eight or ten for the entire product. It's a mix of engineers and business analysts who handle Azure Data Factory maintenance.

I'd rate Azure Data Factory as eight out of ten.

My company is an end user of Azure.

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.

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.

My primary use case of Azure Data Factory is supporting the data migration for advanced analytics projects. 

One of the most valuable features of Azure Data Factory is the drag-and-drop interface. This helps with workflow management because we can just drag any tables or data sources we need. Because of how easy it is to drag and drop, we can deliver things very quickly. It's more customizable through visual effect. 

Data Factory could be improved by eliminating the need for a physical data area. We have to extract data using Data Factory, then create a staging database for it with Azure SQL, which is very, very expensive. Another improvement would be lowering the licensing cost. 

I have been using this solution for the past year. 

This solution is stable. We are using an Azure subscription, so there is no maintenance or direct updates, it's just always the latest version.

This solution is automatically scalable, since it's in the cloud. At my company, there were more than one thousand people using this solution because we were a big, media-based company. If there are many user requests in the front end application and the system is not responding much or has slow performance, the system will automatically scale up the performance hardware requirements. 

I have contacted technical support. I have never faced an issue like that with Denodo. Fortunately, we got some kind of a tutorial PDF, which helps us to deploy everything quickly. 

Before working with Azure, I worked with Python. In the culture I was working in, there was no integration. We were using Pure Python scripting and Python data manipulation tools. For example, we used Python's pandas library, which we coded to transform and orchestrate the data, which is necessary for the endpoint. It was not at all a visual tool. It took more time than Denodo. 

There is no installation because it's on the cloud. You just log on to the cloud with your subscription credentials, then you can use Data Factory directly. 

I implemented through an in-house team. 

Data Factory is very expensive. We are using an Azure subscription, so Data Factory has no direct updates, it's just always the latest version. Compared to Denodo, Azure is very costly. Azure Framework has multiple services, not only Data Factory. So in the cloud-based solution, if you're selecting a particular service, like Data Factory, you need to pay for each request.

I also use Denodo. Data Factory is like a transformation layer, but we need an additional staging database or a data storage facility, which is very expensive compared to implementing Denodo. So we extracted the data using Data Factory, then created a staging database with Azure SQL, which cost a huge amount since it's a physical data area. In Denodo, we just implement a layer, which is all handled in Denodo, and not a physical storage mechanism. I prefer customizable data solutions because they improve performance, creativity, and are helpful for front end people.

In comparison to Data Factory's drag-and-drop interface, Denodo developers need to create all the unified views by coding, so we have to create SQL queries to execute. With Data Factory, you can quickly drag and drop data or tables, but in Denodo, it takes more time because you need to code and test and all that.

I rate Data Factory an eight out of ten, mainly because you need a staging database. I recommend Azure to others, but it depends on architecture. In Data Factory, there is no virtualization environment, no layer of virtualization to help integration and doing caching mechanisms. Though Data Factory is there, Denodo is going further. 

My primary use case is getting data from the sensors.

The sensors are installed on the various equipment across the plant, and this sensor gives us a huge amount of data. Some are captured on a millisecond basis.

What we are able to do is the data into Azure Data Factory, and it has allowed us to scale up well. We are able to utilize that data for our predictive maintenance of the assets of the equipment, as well as the prediction of the breakdown. Specifically, we use the data to look at predictions for future possible breakdowns. At least, that is what we are looking to build towards.

It has helped us to take care of a lot of our analytics requirements. We are running a few analytics models on Data Factory, which is very helpful.

The overall architecture has been very valuable to us. It has allowed us to scale up pretty rapidly. That's something that has been very good for us. 

The solution can scale very easily.

The stability is very good and has improved very much over time.

My only problem is the seamless connectivity with various other databases, for example, SAP. Our transaction data there, all the maintenance data, is maintained in SAP. That seamless connectivity is not there. 

Basically, it could have some specific APIs that allow it to connect to the traditional ERP systems. That'll make it more powerful. With Oracle, it's pretty good at this already. However, when it comes to SAP, SAP has its native applications, which are the way it is written. It's very much AWS with SAP Cloud, so when it comes to Azure, it's difficult to fetch data from SAP.

The initial setup is a bit complex. It's likely a company may need to enlist assistance.

Technical support is lacking in terms of responsiveness.

We've been using the solution roughly for about a year and a half.

It hasn't been an extremely long amount of time. 

From a security perspective, the product has come up a long way.

With the Azure Cloud Platform, in 2015, I was in a different organization and it was not reliable at all. It has become much more reliable since then and is very stable at the moment. It's reliable.

The solution is pretty easy to scale on Azure. I have found it to be very efficient and it is pretty fast. You just need to get the order done properly, and then you will be able to scale up.

We have about five to seven people using it at this time.

Technical support isn't the best, as it's a bit delayed at times.

Whenever we need some urgent support, wherein we have to restart or something has stuck, it takes a bit of time. Some improvements can be made in the customer support area.

In summary, we are not completely satisfied with the support.

The initial setup is not straightforward. It's a bit complex. A company may need to hire someone to assist them with the process.

The solution's deployment took about eight weeks.

I had to hire technical experts who could help us in the process. We could not handle the implementation ourselves.

Cost-wise, it is quite affordable. It's not a factor in the decision-making process when it comes to whether or not we should use it. That said, the pricing is very reasonable.

We evaluated both Oracle and SAP before choosing Azure Data Factory.

We are customers and end-users.

I'd advise companies considering the solution that they need to be very clear about the use case they are trying to address. They need to understand the data ecosystem that they have and what percentage of data is coming in from the various ERP systems.

Do that study properly and then come up with the right solution. If, for example, it is that the underlying data that they want to analyze is more than 60% residing in SAP, then probably Azure would not be the right platform to move ahead with.

We're mostly satisfied with the product. However, getting it connected to closed ERP systems like SAP would make it more powerful.

I would rate the solution eight out of ten.

In my company, we use Azure Data Factory for everything related to data warehousing. Depending on my customer's wants, I will use SSIS or Azure Data Factory. If my customers want Fivetran, I will use it for them. If the customer wants a suggestion from me on what they should use, then I will look at what they have today and their skills. According to the inputs I receive from my customers, I will recommend what makes more sense for a particular customer. I can be called a software agnostic.

I can do everything I want with SSIS and Azure Data Factory.

There aren't many third-party extensions or plugins available in the solution. Adjunction or addition of third-party extensions or plugins to Azure Data Factory can be a great improvement in the tool. Creation of custom codes, custom extensions, or third-party extensions, like Lookup extension, should be made possible in the tool.

I am unsure if Azure Data Factory bridges the gap between on-premises, cloud, and hybrid solutions. I would like to see a version that would work equally well in both on-premises and cloud environments. I would like to see the aforementioned offerings made to customers as valuable alternatives to the old SSIS tool.

I have been using Azure Data Factory for many years. I started using the tool since it was called DTS and then, later, SSIS. I currently use Microsoft SQL SSIS 2019.

The solution is deployed on the cloud.

Overall, I rate the solution an eight out of ten.