Azure Data Factory Reviews

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.

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. 

It's a PaaS service. It's a hybrid solution. The cloud provider is Microsoft.

We are not using Azure Data Factory as for users. Rather, we're using it as a process base. We're just using it for orchestration, not for any kind of ETL stuff.

We have plans to increase usage. It's going to take a major role in any kind of traditional data warehousing. It has big potential, especially as a PaaS offering.

There has been improvement in data resilience, in the way that we're moving the data from on-prem to cloud and vice versa.

The most important feature is that it can help you do the multi-threading concepts. It's in Informatica, but the resourcing is quite robust. You can scale up and scale down as per your needs.

There should be a way that it can do switches, so if at any point in time I want to do some hybrid mode of making any data collections or ingestions, I can just click on a button. I can change a switch and make sure a batch can be a streaming process.

I've been using Azure Data Factory for more than two years.

The stability of Azure as a PaaS could be improved.

It's scalable.

I would rate their technical support 3 out of 5. It's not great, but it isn't bad.

The setup is complex. It has nothing to do with the technology but with the design. We were wondering how to leverage the orchestration layer where we are having the Azure Data Factory and how to integrate with the Databricks. That's where we had some challenges in terms of choosing the right product.

You can do deployment in-house. 

I would rate this solution 8 out of 10.

For someone who is looking to use this solution, my advice is to do proper due diligence of your current application, know where your application is fitting, and look for the requirements. It all depends upon the current use case that you have currently in your system.

The primary use case is to connect to various different data sets and do an EAT into our data warehouse.

We have been using drivers to connect to various data sets and consume data. The solution gives everything under one roof, which is an important feature.

We require Azure Data Factory to be able to connect to Google Analytics.

I have been using the solution for two years.

The solution is stable.

The solution is scalable.

We had a few technical calls with the Microsoft technical support team for some issues that we were facing, which they helped us resolve.

Positive

The initial setup is straightforward and the team is able to deploy between six and seven days.

The implementation was completed in-house.

The cost is based on the amount of data sets that we are ingesting. The more data we ingest the more we pay.

I give the solution a nine out of ten. We have been happy with all the customer implementations, and the customers are satisfied with the ADF pipelines. We are also currently examining the Synapse pipelines, which are likely similar.

We have six developers using the solution in our organization.

People should use the solution for two reasons. Firstly, we can switch off any data pipelines we set up to save costs. Secondly, there are several connectors available in one place, including most standard connectors.

Depending on their pipeline, our customers use Azure Data Factory for their ELT or ETL transformation processes.

I like that it's a monolithic data platform. This is why we propose these solutions.

The performance could be better. It would be better if Azure Data Factory could handle a higher load. I have heard that it can get overloaded, and it can't handle it.

We have been providing customers Azure Data Factory solutions for about five years. 

Azure Data Factory is a stable solution. The performance is good.

Microsoft technical support is good. We are a Gold partner, and we have got good tech support from them.

From a cloud perspective, the initial setup is straightforward. We have a data engineering team with about 15 professionals managing and maintaining this solution.

We have an accelerated solution around it. We utilize our accelerated solutions to spin all these services into the cloud. So, for us, it does not take much time.

I would recommend this solution depending on whether they want AWS, Azure, or GCP. We recommend all of them to our customers. We have about 50 to 80 people who are using this solution.

On a scale from one to ten, I would give Azure Data Factory a nine.

Azure Data Factory is an all-in-one solution for ETL in our company.

My company doesn't use the product for development purposes.

I use the solution in my company as an ETL tool and for orchestration.

As a DevOps engineer, I feel that the CI/CD part and the tool's integration with GitHub are the product's best features. If you compare it with other tools, like Glue, AWS, and other solutions, I feel Azure Data Factory's deployment part is a lot easier to manage. The code promotions and the data pipeline promotions to higher environments are a lot easier with Azure Data Factory.

The product's technical support has certain shortcomings, making it an area where improvements are required. Instead of sending out documents, I think the tool's support team should focus on how to troubleshoot issues. I want the tool's support team to have real-time interaction with users.

The product's price can be problematic for small businesses, making it an area where improvements are required.

I have experience with Azure Data Factory. I am the end user of the tool. Azure Data Factory is a PaaS solution. I use the solution's latest version.

It is a stable solution since it is a PaaS product. Stability-wise, I rate the solution an eight out of ten.

The scalability of the product is impressive. Scalability-wise, I rate the solution an eight out of ten.

Most of the people in my company work on Azure, and those who want to use the native ETL capabilities provided by the product opt for Azure Data Factory.

The product is useful in medium to large-sized businesses. Smaller businesses can opt for other options other than Azure Data Factory, considering the amount of money they are ready to spend. There are better options available in the market than Azure Data Factory.

I rate the technical support a five to six out of ten.

Neutral

I rate the product's initial setup phase a seven or eight on a scale of one to ten, where one is difficult and ten is easy.

In my company, we take care of the product's deployment process and maintenance phase.

The solution is deployed using Azure's cloud services.

The solution can be deployed in ten to fifteen minutes.

For deployments, my company usually creates codes in Terraform so that we can have automated deployments, and it is connected to us with a CI/CD tool like Azure DevOps. Azure DevOps does the automated deployment for our company.

During the setup phase, users may face issues when it comes to infrastructure deployment and the configuration around it, especially if you consider the integration runtime, as it is something that is too complicated for a normal developer to understand. There is a need for a cloud expert with a good understanding to be able to take care of the deployment in the right manner and in a secure way. The networking setup and security part of the product are a bit complicated, which I might understand since I am a DevOps engineer, but a developer who is new to the product might not understand such parts of the tool. The deployment of the service in an infrastructure can be possible only if the person involved in the deployment has a basic level of understanding related to the product.

I rate the product price as six on a scale of one to ten, where one is low price and ten is high price.

I wanted to compare Azure Data Factory with Fivetran.

Users rely on Azure Data Factory's connectors to meet data integration and transformation needs. Users use connectors that are native to Azure Data Factory. The tool offers more than 90 connectors that can be used to ingest data from different sources.

The feature of the solution I find to be the most beneficial for data management tasks is its connectors, and it can even be used for hybrid scenarios. The tool can connect to a different cloud, like AWS. The product can connect to your on-premises systems. In general, users are able to ingest data from everywhere, and the best part is that all of the aforementioned areas can be managed through GUI. The tool is like a low code-no code solution.

The visual interface of the solution impacts workflow efficiency because I think it is easier to start with for any developer who wants to use the tool. It is easier to start with and also easier to troubleshoot or debug, especially at a time when you cannot expect all your developers to understand codes. It would be good to have an intuitive GUI. Azure Data Factory

does a pretty good job when you compare it with its competitors.

Most of the time, my company uses integration runtime, so we mostly use a self-hosted integration runtime. In short, my company has not seen my impact has not seen much impact on a project from the product's scalability capabilities on any projects because we use it according to the needs of our customers.

I rate the tool an eight out of ten.

My task involves extracting data from a source, performing necessary transformations, and subsequently loading the data into a target destination, which happens to be Azure SQL Database.

The company is experiencing significant benefits as one of our customers is successfully implementing the solution we provide. We offer support to the customer in utilizing Azure Data Factory, and their satisfaction level is quite high.

The most valuable aspect is the copy capability.

Implementing a standard pricing model at a more affordable rate could make it accessible to a larger number of companies. Currently, smaller businesses face a disadvantage in terms of pricing, and reducing costs could address this issue.

I have been working with it for more than six months.

The stability is quite satisfactory. I would rate it nine out of ten.

It provides impressive scalability. There are a total of eight switches currently in use. I would rate it nine out of ten.

Technical support is available for all products, and you can reach them without any hassle. They offer assistance through various channels and are proficient in addressing technical issues, ensuring the highest level of support. I would rate it nine out of ten.

Positive

In the past, we used Oracle Data Integrator.

The initial setup has been smooth and without any difficulties.

Deployment time is contingent on the volume of data. If there are millions of records to process, it will understandably take a significant amount of time. Conversely, for smaller datasets, the deployment can be relatively quick, often within a matter of minutes to reach the destination. In the cloud deployment process, the initial step involves defining the instance. Subsequently, in the copy activity, we specify both the source and destination. Following this, communication with the destination takes place. This process constitutes the necessary steps for deployment, and we proceed accordingly. Due to the cloud system being provided by Microsoft, they handle the maintenance of the servers.

The routing value is quite impressive, especially considering that we successfully implemented it for one of our clients, and they expressed satisfaction. For our latest projects, we've acquired additional customers who also require this product for their setups. I would rate it eight out of ten.

While I can't specify the actual cost, I believe it is reasonably priced and comparable to similar products.

I would recommend considering this solution because, from my perspective, it is not overly expensive. The pricing seems reasonable, making it a viable option to explore. Overall, I would rate it nine out of ten.

We use Azure Data Factory to set up a data lake information repository.

Most of our customers are Microsoft shops and prefer Azure Data Factory because they have good licensing options and a trust factor with Microsoft. Our core customers are traditional Microsoft shops who prefer to expand on Microsoft on-cloud.

Azure Data Factory's pricing in terms of utilization could be improved. Our customers complain that the solution's bills keep growing.

I have worked with Azure Data Factory for more than five years as a consultant.

Azure Data Factory is a pretty stable solution. I rate Azure Data Factory a nine out of ten for stability.

Azure Data Factory is a scalable solution. We have around 15 to 20 customers, of which about 8 to 10 use Azure Data Factory.

Azure Data Factory's initial setup needs some amount of professional training and qualification.

It seems very low initially, but as the data grows, the solution’s bills grow exponentially.

Azure Data Factory is deployed on-cloud for our clients.

Azure Data Factory is a pretty solid solution with all the factors and integration built in. It's as good as any product.

I recommend users compare with Snowflake before choosing Azure Data Factory. We've had customers who prefer Snowflake just for its ease of use. Since we're not a Microsoft official reseller, I give options to customers and then let them pick and choose some from Azure Data Factory, Snowflake, or anything on Amazon.

Overall, I rate Azure Data Factory an eight out of ten.