Azure Data Factory Reviews

I'm a customer. I'm using Azure Data Factory.

I find that the solution integrates well with cloud technologies, which we are using for different clouds like Snowflake and AWS. It is much more flexible in terms of transferring data from on-premise or on cloud. There is no need to create different mappings for different tables. The platform has the capability to handle metadata efficiently. So all our needs are being fulfilled with the platform we have right now.

I do not have any notes for improvement.

I have used it for almost six years.

The stability is quite good.

We wanted to move on to the cloud.

The initial setup is almost not difficult for technical people.

I used to work on Informative Support Center. Now we are using Azure Data Factory. 

I rate it eight out of ten. 

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.

Our company uses the solution as a data pipeline. We get information outside the cloud from our factory such as data relating to production. We categorize it, clean it up, and transfer it to a database and data model. From there, we analyze the data using BI and other things. We gather information in data lake products like Microsoft Synapse and Microsoft Data Lake. 

We have two to three administrators who use the solution in a quite standard, mainstream way with nothing extreme. They handle administration, security, and development. 

It is difficult to define the total number of users because that depends on the number of data factory agents. We built the solution to have a different data factory agent for every customer. For example, if we have ten customers then we have ten users. We hope to increase usage but growth depends on our marketing efforts and how well we sell our products. 

The data factory agent is quite good and programming or defining the value of jobs, processes, and activities is easy. We have the agent installed on-premises in order to gather information.  

The cloud includes all kinds of API connections so we can easily gather information from other services. 

The solution seamlessly integrates with the Azure infrastructure. 

The pricing model should be more transparent and available online. When you start programming, you define the fields, variables, activities, and components but don't know the implication on price. You get a general idea but the more activities you add, the more you pay. It would be better to know price implications up front. 

There is a calculator you can run to simulate price but it doesn't help a lot. Practically speaking, you have to build your job and run it to see the exact price implications. This is an issue because you might realize you are paying too much so you have to reprogram or change things. 

I have been using the solution for three years. 

The solution is stable with no issues. Stability is rated a nine out of ten. 

We did have some breaches, but that was because we misconfigured something. Since we corrected it, we haven't had any issues. 

The solution is scalable with no performance issues. We haven't yet reached our limit that would require scaling. Scalability is rated an eight out of ten. 

We have discussions with our Microsoft partner all the time. 

In the last three years, we have contacted Microsoft directly three or four times. Once was for a general architectural issue and the rest were for the data factory or other items. Each time, we talked together with Microsoft and our partner. 

Support gave us answers and solved our issues. Sometimes, we didn't like the answer but we accepted that it was the correct answer. 

Support is rated a nine out of ten. 

Positive

We have not used another solution to this magnitude for real development and production. We work a little bit on Google Cloud. 

The initial setup was quite quick. Deployment was fairly simple and took less than a week. The setup got us up and running. 

After that, we had to write the implications of the data model and the kinds of activities. We are still doing this today because we make changes all the time.

The initial setup was seamless because we worked with a gold star Microsoft partner. Our side of setup was pretty quiet. We talked with our partner and told them what we needed from a security and monitoring point of view. We had a few high-level discussions from the block diagram perspective. Basically we said we need this or that, and our partner made it happen. 

The team included one person from our partner and three in-house team members with varying expertise across data modeling, security, and devops. We always worked with the same person but maybe behind the scenes he talked with coworkers. He did talk several times with Microsoft but we don't really know how many people were involved.

The solution does not require infrastructure maintenance. If we ever have issues, we can use Azure Defender to resolve them. We only make slight changes at the application level. 

We haven't calculated ROI on a formal level, but the fact is we need the solution. Because of the integration, we save a lot but haven't run exact numbers. 

The pricing model is based on usage and is not cheap. Based on our activity, we pay about $2,000 per month. 

Pricing is rated a four out of ten. 

If we didn't have the solution, we would have to find another tool because data pipelines are an essential part of our business. 

The biggest advantage to the solution is its integration with the Azure infrastructure that includes the active directory, security, Synapse, Data Lake, Power BI, and the data factory agent. 

All of the integration was a big consideration for us. We had general guidelines that said working with one vendor would provide the best integrations. The guideline was to use Microsoft unless there was an issue. 

We did not look at a third party or open source even though there are similar tools available. 

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. 

We use the solution for building a few warehouses using Microsoft services.

We worked on a project for the textile industry where we needed to build a data warehouse from scratch. We provided a solution using Azure Data Factory to pull data from multiple files containing certification information, such as CSV and JSON. This data was then stored in a SQL Server-based data warehouse. We built around 30 pipelines in Azure Data Factory, one for each table, to load the data into the warehouse. The Power BI team then used this data for their analysis.

For the integration task, we used Azure Data Factory for orchestration and data flows for transformation. The data flows were beneficial, allowing us to perform multiple transformations. Additionally, we utilized web API activities to log data from third-party API tools, which greatly assisted in loading the necessary data into our warehouse.

When we initiated the cluster, it took some time to start the process. Most of our time was spent ensuring the cluster was adequately set up. We transitioned from using the auto integration runtime to a custom integration runtime, which showed some improvement.

I have been using Azure Data Factory for four years.

When running the process server, we encountered frequent connection disconnect issues. These issues often stemmed from internal problems that we couldn’t resolve then, leading to repeated disruptions.

I rate the stability as seven out of ten.

20 people are using this solution daily. I rate the scalability around eight out of ten.

Customer service supported us whenever we needed it.

Positive

We have used SQL Server.

The initial setup is easy and takes four to five hours to complete.

They have reduced the infrastructure burden by 60 percent.

Pricing is reasonable when compared with other cloud providers.

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.

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.

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.

Our company uses the solution to extract, transform, and load the data into the database.

Feature-wise, one of the most valuable ones is the data flows introduced recently in the solution.

Currently, our company requires a monitoring tool, and that isn't available in Azure Data Factory. Although the platform displays which pipelines are running, it doesn't offer a monitoring tool that allows for the sequential execution of pipelines and the ability to visualize end-to-end data flow. As such, this feature is currently missing from the platform.

I have been using Azure Data Factory for more than six years. Also, I am an end-user of the solution, and I initially used to work on Azure Data Factory V1. Now, I have switched to Azure Data Factory V2.

It is a stable solution. Stability-wise, I rate the solution an eight or nine out of ten.

Scalability-wise, I rate the solution a seven or eight out of ten. So, scalability can be improved. Also, there are around 150 people in my company using the solution. Moreover, we use the solution daily in our company.

I rate the technical support between eight to nine out of ten.

Positive

Previously, I was using Informatica. My company wanted to shift to a solution that could be deployed on the cloud, so we chose Azure Data Factory.

The solution's initial setup process was easy. On a scale where one is difficult and ten is easy, I rate the initial setup process an eight out of ten. The solution is deployed on the cloud.

Since multiple projects are going on in my organization, there is no uniformity in the time taken to deploy the solution in our company. However, I can say that it only takes a few days to carry out the deployment process.

Our organization has multiple project teams, so each team carries out its deployment process.

To give an average, I would consider that if there are fifty ongoing projects in our company, and if we consider a person from each project, fifty people are needed for the deployment and maintenance process.

The solution's implementation process was done with our in-house team's help.

I cannot comment on the pricing parts since our company's admin team handles it.

Those who want to move to a cloud platform can choose Azure Data Factory since it is the best tool. Since certain improvements are required in the solution, I rate the overall solution an eight out of ten.

I use Azure Data Factory for architecture creation, for example, loading data from Oracle DB to Azure Synapse Analytics, creating facts and dimensions using Azure Data Pipeline, and creating Azure Synapse notebooks for data transformation. 

Another use case for Azure Data Factory is dashboard creation to help customers make informed decisions.

Compared to the on-premise SSIS, Azure Data Factory has better infrastructure. It also benefits my company because you can scale the solution up or down with different resources.

Azure Data Factory is also on a pay-as-you-go or pay-as-you-use model, which is suitable for the company because my company only pays for its usage or requirement.

The solution is also very user-friendly, and the Azure Data Factory support team responds quickly whenever my team has a loading issue.

One advantage of Azure Data Factory is that it's fast, unlike SSIS and other on-premise tools.

It's also very convenient because Azure Data Factory has multiple connectors. It has sixty connectors which you can't find in SSIS. The availability of native connectors allows you to connect to several resources to analyze data streams.

I also like that you can set up your own VM and infrastructure on Azure Data Factory without any help from the IT team because it only requires a single click.

What's missing in Azure Data Factory is an Oracle connector. If you want to connect directly to the Oracle database, you must copy and transform the data. There's no Oracle connector if you want to do transformation using data flow activity, so Azure Data Factory needs more connectors for data flow transformation.

Sending out emails after a job is completed is another area for improvement in the tool.

I've been using Azure Data Factory for three years.

Azure Data Factory is a scalable tool.

We used SSIS, but its on-premise version is slower than Azure Data Factory, and Azure Data Factory, infrastructure-wise, is better, so we went with Azure Data Factory.

The initial setup for Azure Data Factory is an eight out of ten.

Manually deploying Azure Data Factory is easy and doesn't take much time, but I'm not sure how long it takes for an automated approach to deployment.

The licensing model for Azure Data Factory is good because you won't have to overpay. Pricing-wise, the solution is a five out of ten. It was not expensive, and it was not cheap. It's in the middle.

I have experience with both Azure Data Factory and SSIS.

I'm using the latest version of Azure Data Factory.

My rating for Azure Data Factory is eight out of ten.

My company is an Azure Data Factory user.