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. 

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. 

My main use case for Azure Data Factory is to pull data from on-premises systems. Most data transformation is done through Databricks, but Data Factory mainly pulls data into different services.

The solution handles large volumes of data very well. One of its best features is its ability to integrate data end-to-end, from pulling data from the source to accessing Databricks. This makes it quite useful for our needs.

The main challenge with implementing Azure Data Factory is that it processes data in batches, not near real-time. To achieve near real-time processing, we need to schedule updates more frequently, which can be an issue. Its interface needs to be lighter. 

One specific issue is with parallel executions. When running parallel executions for multiple tables, I noticed a performance slowdown.

I have been working with the product for five years. 

We haven't faced any issues with the tool's stability. 

The solution can handle large datasets. 

I am satisfied with Microsoft's support. They provide solutions to our challenges. 

Positive

The solution is cheap. 

I rate the overall product an eight out of ten. 

We use Azure Data Factory for data integration.

The most valuable features of the solution are its ease of use and the readily available adapters for connecting with various sources.

Some known bugs and issues with Azure Data Factory could be rectified.

I have been using Azure Data Factory for about two years.

I rate the solution an eight out of ten for stability.

Azure Data Factory is a scalable solution. A team of 16 people from the data analytics team use the solution in our organization.

I rate the solution an eight out of ten for scalability.

On a scale from one to ten, where one is difficult and ten is easy, I rate the solution's initial setup a seven out of ten.

A team of three people deployed Azure Data Factory in three to four days.

The solution's pricing is competitive.

We build data pipelines primarily for integration. Few of them are real-time data transfers, and few of them would be a batch-free file. These would direct the data from various sources to our data warehouse. Azure Data Factory helps build the data pipelines and adaptors.

The solution has built-in features and a control center for us to monitor the status of the pipelines. The solution's email notification also helps us in monitoring. We didn't face any challenges to set up the data pipelines. We know there are some controls, but governance is customized for the organization's requirements. We have our own policies.

Azure Data Factory is deployed on the cloud in our organization. I would recommend Azure Data Factory to other users.

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

We use the solution for migration. We collect data from SAP and various other sources, including multiple ERP systems. These ERP systems encompassed different versions of SAP, Dynamics, Navision, and Oracle, presenting a considerable challenge for data integration. The objective was to consolidate all data into Azure Data Factory and Data Warehouse, establishing a structured framework for reporting and analytics. The main hurdle encountered was data ingestion, particularly with SAP data, due to its significant volume. Alternative tools such as PolyBase were utilized to expedite the process, as standard SAP APIs were insufficient for loading data into Azure Data Services. Collaboration with an Azure data engineer facilitated the exploration of alternative ingestion methods. 

The most important feature is the Data Catalog. We need to define all the data fields we test. It has technical information in the Data Catalog. The main feature is data ingestion in ADF. We also extended it to PurView because PurView is an extension of the Azure data catalog. It can scan metadata. There is a limitation in ADF when setting up the data catalog.

Integration with other tools, such as SAP, could be enhanced. It still has challenges when we talk about different types of structured and non-structured datages. Azure Data Factory has data ingestion issues. There are no delays out of the box. We needed a lot of tools to make the ingestion happen because of the data structure and size of the data.

The transformation we needed to do on data was also not so easy. It was also a long process. We had a bit more capabilities for setting up the Data Catalog, but it still didn't solve the problem from the data ingestion.

I have been using Azure Data Factory as a consultant for five years.

Sometimes, we experienced some instability, mainly on injection.

I rate the solution’s stability as seven out of ten.

I rate the solution’s scalability an eight out of ten.

The support is very good.

Positive

You need to be experienced in deploying the solution. It's not so easy for a business user. Depending on the use case, it takes around six months to get a proof of concept done.

I rate the initial setup a seven out of ten, where one is easy and ten is difficult.

The pricing is visible because you pay for what you do.

The product looks quite expensive because it charges based on the size of the data. If you're not aware, your cost can be very high. If you are experienced, you know that.

I rate the product’s pricing a seven out of ten, where one is cheap and ten is expensive.

I was mainly focusing on ingestion and cataloging. Data engineers were handling data orchestration.

The tool’s maintenance is easy.

There could be a bit more clarity in the pricing structure. It should be understandable for business users. The cost is is becoming too high because users are unaware of the pricing structure. Secondly, the tool should integrate better with other tools like ERP systems.

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

We are currently migrating from on-prem to the cloud, and our on-prem tables are getting data from upstream. We used ADF to build a pipeline to facilitate this migration. A team of 15-20 people currently uses ADF, and more will join once it goes live.

ADF is another ETL tool similar to Informatica that can transform data or copy it from on-prem to the cloud or vice versa. Once we have the data, we can apply various transformations to it and schedule our pipeline according to our business needs. ADF integrates with Databricks. We can call our Databricks notebooks and schedule them via ADF. 

I have used Azure Data Factory for about six months.

I rate Azure Data Factory six out of 10 for stability. ADF is stable now, but we had problems recently with indexing on an SQL database. It's slow when dealing with a huge volume of data. It depends on whether the database is configured as general purpose or hyperscale. 

I rate Azure Data Factory eight out of 10 for ease of setup. The deployment time depends on the data volume. Four million records will take longer than four thousand. Migrating our full load from on-prem to the cloud took around 16-18 hours because the volume was 17 million. 

I rate ADF six out of 10 for affordability. The cost depends on the services we use. It's usage-based. 

I rate Azure Data Factory seven out of 10. Companies that want to migrate from on-prem to the cloud have lots of options. I haven't explored them all, but Azure, GCP, and AWS are essentially all the same.

Azure Data Factory is primarily used to orchestrate workflows and move data between various sources. It supports both ETL and ELT  processes. For instance, if you have an ERP system and want to make the data available for reporting in a data lake or data warehouse, you can use Data Factory to extract data from the ERP system as well as from other sources, like CRM systems.

Data Factory allows you to pull data from multiple systems, transform it according to your business needs, and load it into a data warehouse or data lake. It also supports complex data transformations and aggregations, enabling you to generate summary and aggregate reports from the combined data. Data Factory helps you ingest data from diverse sources, perform necessary transformations, and prepare it for reporting and analysis.

I have extensive experience building things independently, with over twenty years of experience in SQL, ETL, and data-related projects. Recently, I have been using Azure Data Factory for the past two years. It has proven to be quite effective in handling large volumes of data and performing complex calculations. It allows for the creation of intricate data workflows and processes faster. Azure Data Factory is particularly useful for enterprise-level data integration activities, where you might deal with millions of records, such as in SAP environments. For example, SAP tables can contain tens or hundreds of millions of records. Managing and maintaining the quality of this data can be challenging, but Azure Data Factory simplifies these tasks significantly.

It is a powerful tool and is considered one of the leading solutions in the market, especially for handling large volumes of data. It is popular among large enterprises.

While it has a range of connectors for various systems, such as ERP systems, the support for these connectors can be lacking. Take the SAP connector, for example. When issues arise, it can be challenging to determine whether the problem is on Microsoft's side or SAP's side. This often requires working with both teams individually, which can lead to coordination issues and delays. It would be beneficial if Azure Data Factory provided better support and troubleshooting resources for these connectors, ensuring a smoother resolution of such issues.

I have been using Azure Data Factory for two years.

I rate the solution's stability a nine out of ten.

It's pretty good. There are no issues with scalability.

The support has been good.

Positive

It is straightforward to set up. However, ensuring its security requires careful configuration, which can vary depending on the organization's requirements. While the basic setup is user-friendly and doesn’t necessarily require advanced technical skills, securing the environment involves additional steps to prevent unauthorized access and ensure that data is only accessible from permitted locations. This can be more complex depending on the specific setup and organizational needs.

Setting up the infrastructure typically takes about two to three weeks and usually requires the effort of two people.

Azure Data Factory serves several important purposes. One key reason for using it is to build an enterprise data warehouse. This is crucial for centralizing data from various sources. Another reason is to gain insights from that data. By consolidating data in a unified location, you enable data scientists and engineers to analyze it and generate valuable insights.

Customers use Azure Data Factory to bring their data together, creating opportunities to understand their data better and extract actionable insights. However, simply consolidating data is not enough; the actual value comes from how you analyze and utilize it. This involves deriving insights, creating opportunities, and understanding customers better, which can significantly benefit the organization.

Pricing is fine. It's a pay-as-you-go option.

It is in the same price range as other major providers. However, costs can vary depending on enterprise agreements and relationships.

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

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.