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.

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.

There was a need to bring a lot of CRM and marketing data for some PNL analysis. We are connecting to the Salesforce cloud. In it, there's a specific solution in Salesforce Core CRM for the pharmaceutical industry. We are using the solution to connect to that and we are bringing in the various dimensions and transactions from that data source.

Data Factory itself is great. It's pretty straightforward. You can easily add sources, join and lookup information, etc. The ease of use is pretty good. 

They have a lot of other components like a newer monitor, which helps track and generate alerts for any failed jobs and things of that nature, which is helpful.

At this point in time, they should work on somehow integrating the big data capabilities within it. I have not explored it, but it would be good if somehow we could call a Spark job or something to do with the Spark SQL within ADS so that we wouldn't need a Spark tested outside.

On the UI side, they could make it a little more intuitive in terms of how to add the radius components. Somebody who has been working with tools like Informatica or DataStage gets very used to how the UI looks and feels. 

In ADS, adding a new table or joining a new table and overriding that with an override SQL that I could customize would be helpful.

Being able to debug from the design mode itself would be helpful.

I've been using the solution for one year.

In the latest version, the v2 version, the solution is pretty stable. It does not give unknown letters or things like that.

The solution allows you to create reusable components, so it can be scaled pretty easily.

Being an IT services company, we have a gold or a platinum partnership with Microsoft. For us, getting the technical support we need is not a big issue. Their community is also pretty active in responding to any issues. It's quite good. We've been satisfied with the level of support that is offered.

We were not actually involved in the initial setup. That was all with the client, so I won't be able to comment on it.

In terms of licensing costs, we pay somewhere around S14,000 USD per month. There are some additional costs. For example, we would have to subscribe to some additional computing and for elasticity, but they are minimal. 

For disaster recovery and readability setups, we did that on Data Lake.

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 use this solution to quickly instantiate certain components in Azure with the aim of having consistent use of certain components and objects. It provides one point of reference rather than having the need to replicate points of references, and then having to keep them in sync. 

It has helped our company by providing consistency. For example, by making sure that the way certain objects and components are defined is consistent throughout every step. If things change at any point, they should be reflected at all points. It's the consistency that makes implementations less buggy and less complex.

The function is the central point of reference and the most valuable thing about Data Factory.

I'd like to see videos on YouTube or the Microsoft site with more detailed implementations. The solution lacks a decent UI that allows us to see what kinds of requests are for what objects and how the population of objects is being requested and compared. Right now we have to look at logs to get an idea of what types of calls the data factory receives in what sequence, for example. It would be nice to be able to see it graphically because we currently have to interpret the logs and then create a graphical representation to have an idea of what's going on. In general, it could be simplified and made more user-friendly.

I've been using this solution for six months.

This solution is stable. 

There are thousands of users so the solution is scalable. 

The customer service is excellent. 

Positive

I previously used a combination of solutions to achieve the same end. Data Factory simplifies things which is why we switched to it. 

The initial setup was complex. The deployment was carried out in-house and we had around 10 people involved in the implementation. 

In terms of time and effort savings, we have a return on our investment. 

It's important to have a good data model before you start using the solution; an idea of the types of data architecture, data objects and components in order to use them. Because of the lack of more user-friendly interfaces, especially for the people debugging the system, I rate this solution eight out of 10.

Essentially, Azure Data Factory is more aligned to ETL, but I wanted to provide a solution for a full data lake solution where I could leverage functionality, whether it is ETL, data ingestion, data warehousing, or data lake.

I was planning to switch to Synapse and was just looking into Synapse options.

I wanted to plug things in and then put them into Power BI. Basically, I'm planning to shift some data, leveraging the skills I wanted to use Synapse for performance.

I am not a frequent user, and I am not an Azure Data Factory engineer or data engineer. I work as an enterprise architect. Data Factory, in essence, becomes a component of my solution. I see the fitment and plan on using it. It could be Azure Data Factory or Data Lake, but I'm not sure what enhancements it would require.

User-friendliness and user effectiveness are unquestionably important, and it may be a good option here to improve the user experience. However, I believe that more and more sophisticated monitoring would be beneficial.

I work as an enterprise architect, and I have been using Azure Data Factory for more than a year.

I am working with the latest version.

Azure Data Factory is a stable solution.

Azure Data Factory is a scalable product.

In my current company, I have a team of five people, but in my previous organization, there were 20.

Technical support is good. We encountered no technical difficulties. Microsoft supported us when we planned to provision Azure Data Factory over a private link. As a result, we received excellent support from Microsoft.

Products such as Azure Data Factory and Informatica Enterprise Data Catalog were evaluated. This is something I'm working on. I work as an enterprise architect, so these are the tools that I frequently use.

Previously, I worked with SSIS. We did not change. Because we were building a cloud-based ETF solution Azure Data Factory was an option, but when it came to on-premises solutions, the SQL server integrating the SSIS tool was one option.

The initial setup is easy.

It took three to four weeks to get up to speed and get comfortable using it.

Pricing appears to be reasonable in my opinion.

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.

The solution is primarily used for data integration. We are using it for the data pipelines to get data out of the legacy systems and provide it to the Azure SQL Database. We are using the SQL data source providers mainly.

The data pipeline and the orchestration functionality are the most valuable aspects of the solution.

The interface is very good. It seeks to be very responsive and intuitive.

The initial setup is very quick and easy.

I'm more of a general manager. I don't have any insights in terms of missing features or items of that nature.

Integration of data lineage would be a nice feature in terms of DevOps integration. It would make implementation for a company much easier. I'm not sure if that's already available or not. However, that would be a great feature to add if it isn't already there.

We've used the solution for the last 12 months or so.

From what I have witnessed, the solution is quite stable. It doesn't crash or freeze. There are no bugs or glitches. It's reliable.

We work with medium to enterprise-level organizations. Customers have anywhere from 300 employees up to 160,000 employees.

Microsoft offers a great community. There's a lot of support available. We're quite satisfied with the level of assistance on offer.

Since the solution is a service, it's basically just a click and run setup. It's very simple. There's very little implementation necessary. A company should be able to easily arrange it. The deployment doesn't take very long at all.

We do provide the implementation for our clients. We're able to provide templates as well. We have predefined implementation space in Data Factory and provide it to the customer.

While clients might individually evaluate other options, however, we're not aware of that information. I can't say what other solution clients might consider before ultimately choosing Microsoft. I would say that it is likely Talend and maybe SQL Server Integration Services.

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 use Data Factory for automating ETL processes, data management, digital transformation, and scheduled automated processes. My team has about 11 people, and at least five use Data Factory. It's mostly data engineers and analysts. 

Each data analyst and engineer manages a few projects for clients. Typically, it's one person per client, but we might have two or three people managing and building out pipelines for a larger project.  

It's cloud-based, allowing multiple users to easily access the solution from the office or remote locations. I like that we can set up the security protocols for IP addresses, like allow lists. It's a pretty user-friendly product as well. The interface and build environment where you create pipelines are easy to use. It's straightforward to manage the digital transformation pipelines we build.

Data Factory has so many features that it can be a little difficult or confusing to find some settings and configurations. I'm sure there's a way to make it a little easier to navigate.

In the main ADF web portal could, there's a section for monitoring jobs that are currently running so you can see if recent jobs have failed. There's an app for working with Azure in general where you can look at some segs in your account. It would be nice if Azure had an app that lets you access the monitoring layer of Data Factory from your phone or a tablet, so you could do a quick check-in on the status of certain jobs. That could be useful.

We've been using Azure Data Factory for about three years.

I've been happy with it overall. I don't think we've had any major issues. We've been able to do what we needed, whether connecting to different data sources or setting up different types of transformations and processes. 

It's a cloud solution, so it's inherently scalable. I don't know If we have to raise the limits on resources like clusters and processing power or if it will just automatically scale up. I can't remember offhand. 

We managed the same actions with a combination of tools. We used SFTP servers to move data from one place to another. We used scripts for loading and some other stored procedures or processes for data transformation within a database. It took two or three pieces of technology or systems to manage the same types of operation. Data Factory lets us consolidate those steps into a single pipeline. 

Setting up Azure Data Factory is pretty straightforward. We had an Azure account already, and Data Factory was just something we could add as an extra service. We had to create instances and pipelines, and it took us about two weeks to get our first pipelines scheduled and running. 

We do everything in-house.

We see a return on Data Factory if we compare the time and effort that would be necessary to perform the equivalent processes manually. 

I'm not too familiar with the cost, but I believe we're reasonably happy with what we're paying. My understanding is that the cost of Data Factory is tied to consumption. It depends on the amount of data or the number of pipelines running, and the cost varies from month to month depending on the usage. 

You'll obviously pay more if you're scheduling heavy digital transformation processes to run every hour, but I don't think there are any other hidden costs or anything extra. When you set up a new account, you have a trial period that enables you to create a test pipeline or process that's typical of your use case and then do a benchmark test to see if Data Factory can achieve the efficiency you need. You'll also get some idea of how much the process will cost to run. From there, it's straightforward to do a cost evaluation or comparison to see if it's the right fit for your company. 

We were looking for a single solution, and Data Factory was the first one that interested us. I don't think we looked at many others. We were pretty set on Azure, and Data Factory seemed to fit our needs, so we didn't make a full comparison with the alternatives.

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 mainly use Data Factory to load data for ETL processes or to Azure Storage and for testing purposes in our business unit.

Data Factory's most valuable feature is Copy Activity.

I've been using Data Factory for around two years.

Data Factory is stable.

We've had no problems with Data Factory's scalability.

Microsoft's technical support is responsive and quick to help.

We used consultants to implement Data Factory.

Data Factory's cost is too high.

I would advise anybody thinking of implementing Data Factory to calculate their costs at the initial stage in order to have some knowledge about future costs for the whole project. I would rate Data Factory as eight out of ten.