What advice do you have for others considering Azure Data Factory?

Azure Data Factory is specifically meant for data integration and nothing more. For reporting and other capabilities, different Microsoft tools should be used. I'd rate the solution nine out of ten.

We have used the Key value pair for authentication with the adoption. I can rate it around eight out of ten. I recommend the solution. Overall, I rate the solution a nine out of ten.

Overall, I rate the solution a nine out of ten.

I rate the overall product an eight out of ten.

Data Factory integrates seamlessly within the Azure ecosystem, offering robust data management capabilities across large-scale projects. I rate it a nine out of ten.

The tool has met our projects' growing data needs effectively so far. I rate it an eight out of ten.

In terms of handling complex data transformations and cleansing, Azure Data Factory is capable for simple to medium tasks, but for more complex tasks, we resort to custom coding solutions. Overall, I would recommend Azure Data Factory for data integration and management, and I would rate it an eight out of ten for its flexibility and ability to support third-party integrations.

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.

I would definitely recommend using it It's a good tool. Because there isn't a comparable native Azure product for cloud-based integrations, using ADF is often necessary. If working with multiple clouds (Azure, AWS, etc.), we end up using tools like Informatica or Snowflake. Overall, I would rate the solution a seven out of ten. It has some limitations, especially with streaming data. Compared to Talend, Snowflake, or Informatica, which have rich screen-based GUIs, ADF's visual capabilities are weaker. There's a steeper learning curve, as you need to understand its technical UIs and data flows. However, with Microsoft's acquisition of Power BI, I suspect they might integrate these capabilities as 'Microsoft Fabric' in the future.

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.

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

I'm using the solution as an end-user. We are using the latest version of the solution. I'd rate the solution seven out of ten.

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.

I think Data Factory is a good fit when you need a light, inexpensive way to ingest data. I would rate it eight out of ten.

I would definitely recommend this solution. If you decide to implement Data Factory, I suggest reaching out to qualified professionals because there are a lot of moving parts. That said, if you have internally qualified staff, deployment shouldn't be a problem. Apart from a few minor issues, it's pretty reliable with good support and a whole bunch of resources available on the web. I rate this solution a solid nine out of 10.

My best advice is to keep an eye on the pricing because we found out the hard way. Pricing is actually related to the way you use what the solution calls activity. This activity stuff drastically changes the coding to the rate you gather information from your client environment. So, when marketing guys tell you to gather information every minute, you have to weigh the heavy implications in comparison to collecting data once an hour or day. Programmers and developers designed the solution based on usage activity and building tasks or jobs. Pay a lot of attention to the pricing implications from the starting point of view. Technically, you can solve all issues but you need to keep an eye on the pricing. From a technical point of view, the solution is rated an eight out of ten. Because of pricing, the solution's overall rating is downgraded to a seven out of ten.

I'm a customer and end-user. Our company chose to use this solution based on the fact that it is a Microsoft product. We're using a lot of solutions, including Outlook and Teams. We also use Power BI. We try to use Microsoft so that everything is under one umbrella. That way, there is no difficulty with connecting anything. It's a good solution to use. There are lots of videos available on YouTube, and it is very easy to learn. It's very easy to perform things on it as well, which is one thing that a product like ThoughtSpot lacks. There is no training needed like Power BI. I'd rate the solution nine out of ten.

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

I rate the solution a six out of ten. The solution is good but its exception handling and logging mechanisms can be improved. I advice users considering this solution to go for it especially if their integrations are heavy on the data side.

I give the solution ten out of ten. The only thing you need to deploy the solution is to click on publish. The solution includes a feature that increases the number of processors used which makes it very powerful and adds to the scalability. We have three people using the solution in our organization and one engineer that maintains it. I recommend to any potential user to factor in the five-minute warm-up time that is required for each execution.

I rate the solution a seven out of ten. The solution is good and constantly improving, but the concept of Flowlets can be reconfigured to retain the changes we make. I advise users considering this solution to thoroughly understand what Azure Data Factory is and evaluate what's available in the market. Secondly, to assess the nature of the use cases and the kind of products they will be building before deciding to choose a solution.

Data Factory is a good, mature solution, and I would rate it as eight out of ten.

I would rate Data Factory seven out of ten.

We would recommend this solution as it is very solid and has good security features. I would rate this solution an eight out of 10.

I rate Azure Data Factory nine out of 10. It isn't perfect, but it's solid. Data Factory has improved how we deal with various aspects of Azure. It has always met our needs in terms of the transformations and jobs we want to create and schedule.

I’d rate the solution eight out of ten overall.

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.

Azure Data Factory is a good tool. Given that the data platform ecosystem is provided by Microsoft, you know it is good. I would rate the solution an eight out of ten overall.

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.

My only advice is that Azure Data Factory, particularly for data ingestion, is a good choice. But if you want to go further and build an entire data lake solution, I believe Synapse, is preferred. In fact, Microsoft is developing and designing it in such a way that, it's an entirely clubbing of data ingestion, and data lake, for all things. They must make a decision: is the solution dedicated to only doing that type of data ingestion, in which case I believe Data Factory is the best option. I would have preferred, but I'm not a frequent user there right now. I need to think beyond Data Factory as an open-source project to include machines and everything else. As a result, as previously stated, Data Factory becomes very small at the enterprise architect level. I was inundated with power automation, power ops, power virtualizations, and everything else in Microsoft that I had to think about. I would rate Azure Data Factory a seven out of ten.

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.

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.

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

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.

It works very well. I would rate Azure Data Factory an eight out of ten.

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.

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.

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.

I would tell potential users that there are many technologies to do this. For example, if you like to manage big data and do something with it, it would be better to use Databricks. On a scale from one to ten, I would give Azure Data Factory a nine.

We are like an integrator. We are a data warehouse NPI consulting company and we use Data Factory to pull data from different legacy systems and do all these transformations that are necessary in order to provide analytical models. In our normal scenario is that we are providing Azure SQL Databases together with Azure Data Factory and Power BI. 80% of our customers have recognized such a scenario. On a scale from one to ten, I'd rate the solution at an eight. We've been largely happy with the capabilities of the product.

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 general, I would recommend this product. However, it depends on the target IT ecosystem. If they are utilizing a lot of Microsoft products like Power BI, Office, Project, SharePoint, and so forth, it's better to implement Data Factory because it will reduce a lot of effort spent to consume data from other sources. At this point, I can only rate based on my pre-implementation experience, so I would rate this solution an eight out of ten.

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.

I would definitely recommend this solution to anyone who is interested in using it. I would rate Azure Data Factory an eight out of ten.

I would definitely recommend Azure Data Factory. On a scale from one to ten, I would give this solution a rating of eight. If there were a larger amount of automated features, I would give them a higher rating. As I mentioned earlier, if we are working on complex applications, then there is a lot of coding involved. What we hope is that over time, there'll be less coding and more "off the shelf" functionality.

I would rate this solution a five out of ten.

To this point, we are still learning the system and have only tried a very simple data flow. At this point, we haven't had any issues. I would rate this solution an eight out of ten.

I would rate this solution a seven out of ten.

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.

Within the next six months, we are planning to enter into the machine learning part of this solution. This is a product that I can recommend. I would rate this solution an eight out of ten.

The advice that I would give to someone considering this solution is to have some background in data warehousing and ETL concepts. Have the background about data warehousing and ETL that extract, transform, and load. If you have the background you need, you will be successful. If not, then my advice would be to learn a little more about it before using Data Factory. I would rate Data Factory as an eight out of ten.

We have nine people using Azure Data Factory. On a scale of one to ten, I give it an eight.

The advice I would give to someone who is looking to implement this product is to understand the IT technology of the product first and why it would be needed. That is the point where you have to start. Next, you have to understand if the product itself fits your organizational needs. That is you have to look at the business requirements and see whether the product really fits the organization and solves the problems while conforming to the business model. On a scale from one to ten where one is the worst and ten is the best, I would rate the product overall as an eight-out-of-ten.

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 seven out of ten.

We use the public cloud deployment model. I'd warn others to ensure that the design should be frozen before you start building because overriding each other's code and managing code takes effort. To avoid or to reduce that effort, ensure that the design is frozen. You can build some configurable code rather than hard-coding everything into the jobs. That's the biggest recommendation. I'd rate the solution seven out of ten. It's a pretty good solution, but over the past year, I've been limited on the number of cases I have on it. If it had a better user interface and was more intuitive I would have rated it higher.

We primarily use the solution in a hybrid environment. We're Azure partners. I would suggest others start with a small POC and with a smaller dataset or simple pipeline to test the product before fully implementing it. Users shouldn't hesitate to try it, because there are not so many risks. Practice is the best way to start with this service, as opposed to planning. I'd rate the solution eight out of ten.

We use both the on-premises and cloud deployment models. We typically work with enterprise-level companies. Azure Data Factory is pretty good but should be considered as an orchestrator, not as an integrated tool. We can use some building components in that tool to orchestrate the entire workflow but if we are thinking about more details, processing, or data modification during the flow, we'd have to consider Azure Databricks or Data Flow for making those calculations or changes. Users will need Azure Data Factory plus third party tools to reach that level of functionally. I would recommend using Data Factory. I don't have a lot of experience with integration or with integration services, for example, SQL server integration services. However, there are points that should be considered if you are already using SQL server integration services already. You can implement the packages already prepared in Azure Data Factory. It's something that needs to be considered when deciding which technology you are going to use. I'd rate the solution eight out of ten.

I don't use Azure Data Factory for my own company; I help clients implement the solution for their companies. In terms of advice that I would give to others looking to implement the solution, I would say you have to learn. You have to really understand the overall concept. It does not allow you to just click and go. I would rate this solution ten out of ten.