Azure Data Factory Reviews

I can't go into specifics about the use case for Azure Data Factory, but it's for analytics related to an assessment.

What I like best about Azure Data Factory is that it allows you to create pipelines, specifically ETL pipelines.

I also like that Azure Data Factory has connectors and solves most of my company's problems. I can't recall a case where I couldn't use the solution for solving problems.

I'm also happy about the Azure Data Factory dashboard.

A room for improvement in Azure Data Factory is its speed. Parallelization also needs improvement. As for the rest of the features of Azure Data Factory, I'm happy.

I cannot suggest an additional feature I'd like to see in Azure Data Factory in the future because some of the features aren't available internally because the features undergo security evaluation first, and my organization controls which features would become available to users.

I've been using Azure Data Factory for the last two years.

We're happy with the stability of Azure Data Factory.

Azure Data Factory is scalable, with clusters available on demand. There isn't any issue with scaling the solution.

We have an internal support team and the Azure Data Factory support team. We raise tickets and follow up on those tickets, and on a scale of one to five, we'd rate support as four because sometimes there are delays. Otherwise, we are satisfied with Azure Data Factory support.

My company didn't set up Azure Data Factory as the Azure team did it.

We outsourced the implementation of Azure Data Factory directly to the Azure team.

I have no idea how much Azure Data Factory costs.

We're using AWS apart from Azure Data Factory. We're trying out Palantir Foundry as well. They are the leading service providers in the data analytics and ETL world.

I'm familiar with Palantir Foundry, but my company just recently got the Palantir Foundry license, so I'm still not using it, but checking it for shortcomings.

I have experience with Azure Data Factory, too.

I'm unsure of the exact version of Azure Data Factory, but I'm using the latest version or whatever's available on Azure.

I have a vague figure of users of Azure Data Factory, but it's more than one thousand to one thousand five hundred people.

I'd tell people who want to use Azure Data Factory that Microsoft offers excellent courses, ESI (Enterprise Skill Initiatives). You should register and take the courses. Azure Data Factory is a solution I'd recommend to others.

I'd rate Azure Data Factory as nine out of ten because it has a lot of connectors, even custom connectors, for data onboarding. It can also integrate with Spark notebooks and allows my organization to parallelize code. Azure Data Factory also has provisions for Spark and SQL scripts or any scripts, plus the infrastructure is highly scalable, so it's a nine for me.

My organization is a customer of Azure Data Factory.

The primary use case of this solution is to extract ETLS, transform and load data, and organize database synchronization.

The most valuable feature of this solution is the data flow, which is the same SQL server in important service, integration services, which is a very robust and powerful tool to transform data.

The solution can be improved by decreasing the warmup time which currently can take up to five minutes.

I have been using the solution for two years.

The solution is extremely stable.

The solution is scalable.

Previously I used AWS Glue and SSIS.

The initial setup is straightforward.

The implementation was completed in-house and is immediate because it is a native cloud tool.

I have seen an ROI with the time saved migrating data for reports.

The solution's fees are based on a pay-per-minute use plus the amount of data required to process. The more data you process the more CPUs and time is required which increases the cost of using this solution.

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.

We use the product for data warehouses. It helps us to load data to warehouses. 

The tool's most valuable features are its connectors. It has many out-of-the-box connectors. We use ADF for ETL processes. Our main use case involves integrating data from various databases, processing it, and loading it into the target database. ADF plays a crucial role in orchestrating these ETL workflows. 

The tool's visual interface is good. The ADS scheduling feature impacts data management by determining when jobs must be run and setting up dependencies. This capability eliminates the need to rely on enterprise data scheduling tools. 

When working with AWS, we have noticed that the difference between ADF and AWS is that AWS is more customer-focused. They're more responsive compared to any other company. ADF is not as good as AWS, but it should be. If AWS is ten out of ten, ADF is around eight out of ten. I think AWS is easier to understand from the GUI perspective compared to ADF.

I have been using the product for 6 months. 

ADF is stable. 

I rate the tool's scalability an eight out of ten. 

The tool's deployment is easy. The deployment typically takes around two to three days to set up. However, the duration may vary depending on factors such as the number of integrated endpoints. In our company, the deployment team had three to four people. This team consisted of an IT engineer, a network engineer, and an ETL admin.

We still haven't required much maintenance since we're still in the development phase. However, as time progresses and we move into production, we'll better understand the maintenance requirements.

ADF is cheaper compared to AWS. 

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

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.

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.

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.

Our primary use case for the solution is data integration and we deploy it only on Azure.

When we were integrating the Ports product with our internal data warehouse, we had to update all the reports to our internal data warehouse on the Ports system database. However, they were not given access to the database company, and they dump some files or provide you with them. In one case, they were providing files. In another case, they provided some APIs where you need to call in a batch of thousands of records multiple times. It works very well with Azure Data Factory to pull the records, parse them quickly and post them in the database and data warehouse.

We have found the bulk load feature very valuable.

The only challenge with Azure Data Factory is its exception-handling mechanism. When the record fails, it's tough to identify and log.

We have been using the solution for a year and a half and are currently using the latest version.

The solution is scalable and we intend to further increase its usage in the future.

I rate customer service and support an eight out of ten.

Positive

We previously used different solutions.

The initial setup is straightforward.

The implementation was done in-house.

I cannot comment on licensing costs because I was not involved.

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.