Azure Data Factory Reviews

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.

Azure Data Factory can be deployed on the cloud and hybrid cloud. There have been very few deployments on private clouds.

The most valuable feature of Azure Data Factory is the core features that help you through the whole Azure pipeline or value chain.

Across the whole field of use, from accepting the ingestion and real-time SaaS ingestion for which we often use other components. These areas have been instrumental across the board.

Azure Data Factory can improve the transformation features. You have to do a lot of transformation activities. This is something that is just not fully covered. Additionally, the integration could improve for other tools, such as Azure Data Catalog.

I have been using Azure Data Factory for approximately four years.

The stability of Azure Data Factory is good.

I rate the scalability of Azure Data Factory a seven out of ten.

Azure Data Factory is scalable. The solution can move up and be aligned to resources or scaled down.

We have a lot of customers using the solution, approximately 100.

The support from Azure Data Factory is very good. There are some improvements needed.

I rate the support from Azure Data Factory a four out of five.

Positive

I have previously used Informatica. When comparing Informatica to Azure Data Factory, Informatica is a bit behind.

The initial setup of Azure Data Factory is not complex if you know what you are doing. If you do not know the technology you will have a problem.

Azure Data Factory gives better value for the price than other solutions such as Informatica.

I recommend this solution to others.

I rate Azure Data Factory an eight 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.

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.

My task involves extracting data from a source, performing necessary transformations, and subsequently loading the data into a target destination, which happens to be Azure SQL Database.

The company is experiencing significant benefits as one of our customers is successfully implementing the solution we provide. We offer support to the customer in utilizing Azure Data Factory, and their satisfaction level is quite high.

The most valuable aspect is the copy capability.

Implementing a standard pricing model at a more affordable rate could make it accessible to a larger number of companies. Currently, smaller businesses face a disadvantage in terms of pricing, and reducing costs could address this issue.

I have been working with it for more than six months.

The stability is quite satisfactory. I would rate it nine out of ten.

It provides impressive scalability. There are a total of eight switches currently in use. I would rate it nine out of ten.

Technical support is available for all products, and you can reach them without any hassle. They offer assistance through various channels and are proficient in addressing technical issues, ensuring the highest level of support. I would rate it nine out of ten.

Positive

In the past, we used Oracle Data Integrator.

The initial setup has been smooth and without any difficulties.

Deployment time is contingent on the volume of data. If there are millions of records to process, it will understandably take a significant amount of time. Conversely, for smaller datasets, the deployment can be relatively quick, often within a matter of minutes to reach the destination. In the cloud deployment process, the initial step involves defining the instance. Subsequently, in the copy activity, we specify both the source and destination. Following this, communication with the destination takes place. This process constitutes the necessary steps for deployment, and we proceed accordingly. Due to the cloud system being provided by Microsoft, they handle the maintenance of the servers.

The routing value is quite impressive, especially considering that we successfully implemented it for one of our clients, and they expressed satisfaction. For our latest projects, we've acquired additional customers who also require this product for their setups. I would rate it eight out of ten.

While I can't specify the actual cost, I believe it is reasonably priced and comparable to similar products.

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.

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.

We use this solution to perform ELTs so that we do not need to keep code within a database.

The overall performance is quite good.

Occasionally, there are problems within Microsoft itself that impact the Data Factory and cause it to fail.

I've worked with this solution for two and a half years.

I wouldn't consider it to be stable since it fails at times.

The solution is scalable.

Support is quite slow and they have bugs that they are unaware of and claim that that is how the system is supposed to work.

My company used Informatica PowerCenter in the past but I was not involved in that.

The initial setup was quick and easy. The whole process took about fifteen minutes. We have about a hundred users at the moment and have plans to increase.

Two of our in-house developers were able to complete the setup.

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