Azure Data Factory Reviews

We use this solution to ingest data from one of the source systems from SAP. From the SAP HANA view, we push data to our data pond and ingest it into our data warehouse.

Azure Data Factory didn't bring a lot of good when we were also using Alteryx. Alteryx is user-friendly, while Azure Data Factory uses many resources and has issues with parallel workflows. Alteryx helps you diagnose issues quicker than Azure Data Factory because it's on the cloud and has a cold start debugger.

Azure Data Factory has to wake up whenever you are trying to do testing, and it takes about four to five minutes. It's not always online to do a quick test. For example, if we want to test an Excel file to see if the formatting is correct or why the data-flow or pipeline is failing, we need to wait four to five minutes to get the cold start debugger to run. Compared to Alteryx, Azure Data Factory could be better. Nevertheless, we are using it because we have to.

Initially, when we started using it, we didn't like it because it needed to be more mature and had data-flows, so we used the traditional pipeline. After that, Azure Data Factory introduced the concept of data-flows, and it started to become more mature and look more like Alteryx. Azure Data Factory became more user-friendly when data-flows were introduced.

They introduced the concept of Flowlets, but it has bugs. Flowlets are a reusable component that allows you to create data-flows. We can configure a Flowlet as a reusable pipeline and plug it inside different data-flows, so we don't have to rewrite our code or visual transformation.

If we make any changes in our data-flow, it reverts all our changes to the original state of the Flowlet. It does not retain changes, and we must reconfigure the Flowlets repeatedly. We had these issues three months ago so things might have changed. It works fine whenever we plug it in and configure it in our data-flow, but if we make minor changes to it, the Flowlet needs to be reconfigured again and loses the configuration.

We have used this solution for about a month and a half. It is a cloud-based tool, so there are no versions. It is all deployed on Azure Cloud.

Everything is computed inside the SQL server if we're working with pipelines, so we have to be very careful when designing our solution in Azure Data Factory. Alteryx spoiled us because we never cared how it looked in the backend because all the operations were happening on the Alteryx server. But in Azure Data Factory, they run on the capacity of our data warehouse. So Azure Data Factory cannot run your queries, and it directly sends the query to the instance in the SQL server or data warehouse. So we have to be very careful about how we perform certain operations.

We need to have knowledge of SQL and how to optimize our queries. If we are calling a stored procedure, it joins one table in Alteryx. It is pretty easy, and we just put a joint tool. Suppose we want to do it with a stored procedure in the Azure Data Factory. In that case, we have to be very careful about how we write our code. So that is a challenge for our team because we were not looking into how to optimize their SQL queries when fighting queries from Azure Data Factory to the data warehouse.

In addition, the workflows were running very slow, the performance was bad, and some queries were getting timed out because we have a threshold. So we faced many challenges and had to reeducate ourselves on SQL and query optimization.

In regards to scaling, when Azure Data Factory was introduced as your Databricks, it worked similarly to Hadoop or Spark, and it had some Spark clusters in the back end that could scale it as much as it could, and speed up the performance. So it is scalable, especially with Databricks, because a lot of data-related transformations can be performed.

On my team, there are approximately 20 people who work with Azure Data Factory.

We do not have experience with customer service and support.

It does not require any installation and is more like software as a service. You need to create an instance of Azure Data Factory in Azure and configure some of the connections to your databases. You can connect to your block storages and some authentication is necessary for Azure Data Factory.

The setup is straightforward. It doesn't take much time, and it's on cloud. It requires a few clicks, and you can quickly set it up and grant access to the developer. Then the developer can go to the link and start developing within their browser.

We have a team that takes care of the cloud infrastructure, so we raise a ticket and request infrastructure, and they just exceed it based on the naming convention with the project name.

We have an entire team that takes care of the cloud infrastructure. So we raise a ticket when we need infrastructure, which is executed based on the naming convention for the project name.

The nature of our solution is not based on ROI because we are building solutions for other functions within the same organization. In addition, due to the large size of our organization and the services we provide, the ROI is not something we consistently track. It's something discussed with the management, so I can't comment on it.

The cost is based on usage and the computing resources consumed. However, since Azure Data Factory connects with so many different functionalities that Azure provides, such as Azure functions, Logic apps and others in the Azure Data Factory pipelines, additional costs can be acquired by using other tools.

We did not evaluate other options because this solution was aligned with out current work environment. 

I rate the solution a seven out of ten. The solution is good and constantly improving, but the concept of Flowlets can be reconfigured to retain the changes we make. I advise users considering this solution to thoroughly understand what Azure Data Factory is and evaluate what's available in the market. Secondly, to assess the nature of the use cases and the kind of products they will be building before deciding to choose a solution.

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.

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.

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.

As a management consultancy company, we help our clients deploy Azure Data Factory or any other cloud-based solution depending on data integration needs. Regarding how we use Azure Data Factory within our company, we are on the Microsoft Stack, so we use the solution primarily for data warehousing and integration.

The feature I found most helpful in Azure Data Factory is the pipeline feature, including being able to connect to different sources.

I also found running Python codes whenever you need to valuable in Azure Data Factory, especially for certain features of the solution, such as data integrations, aggregations, and manipulations.

Azure Data Factory also has built-in security, which is another valuable feature.

I also like that you get access to the whole Azure suite through Azure Data Factory, so the overall architecture design, defining security and access, role-based access management, etc. It's helpful to have the whole suite when designing applications.

Areas for improvement in Azure Data Factory include connectivity and integration.

When you use integration runtime, whenever there's a failure, the backup process in Azure Data Factory takes time, so this is another area for improvement.

Database support in the solution also has room for improvement because Azure Data Factory only currently supports MS SQL and Postgres. I want to see it supporting other databases.

If you want to connect the solution from on-premises to the cloud, you will have to go with a VPN or a pretty expensive route connection. A VPN connection might not work most of the time because you have to download a client and install it, so an interim solution for secure access from on-premise locations to the cloud is what I want to see in Azure Data Factory.

I've been using Azure Data Factory for about a year now.

Azure Data Factory is very stable, so it's a four out of five for me. In some instances, the solution failed, but I wouldn't wholly blame Azure Data Factory because my company connected to some on-premise databases in some cases. Sometimes, you'll get errors from self-hosted integration, faulty connections, or the on-premise server is down, so my rating for stability is a four.

Scalability-wise, Azure Data Factory is a four out of five because Microsoft is still developing certain tiers, which means you can't upgrade an older skill or tier. In contrast, the more modern, newer tiers could be upgraded easily. Rarely will you get stuck in one platform where you have completely destroyed that container and then fit a new container. Most of the time, Azure Data Factory is pretty easy to scale.

We haven't used Microsoft support directly because whenever we have issues with Azure Data Factory, we can find resolutions through their online documentation.

We're using both Azure Data Factory and SSIS.

We had several in-house solutions, but we moved to Azure Data Factory because it was straightforward. From a deployment standpoint, the solution comes with different services, so we didn't have to worry about separate hardware or infrastructure for networking, security, etc.

The initial setup for Azure Data Factory was easy, so I'd rate the setup a four out of five.

The implementation strategy was looking into what my organization needed overall, then planning and direct deployment. My company first did a test, a dummy version, then a UAT with stakeholders before going into production.

It took about two months to complete the deployment for Azure Data Factory.

An in-house team, the digital data engineering team, deployed Azure Data Factory.

We're still computing the ROI from Azure Data Factory. It's too early to comment on that.

My company is on a monthly subscription for Azure Data Factory, but it's more of a pay-as-you-go model where your monthly invoice depends on how many resources you use.

On a scale of one to five, pricing for Azure Data Factory is a four.

It's just the usage fees my company pays monthly. No support fees because my company didn't need support from Microsoft.

If you're not using core Microsoft products, the cost could be slightly higher, for example, when using a Postgres database versus an MS SQL database.

My company uses Azure Data Factory, SSIS, and for a few other instances, Salesforce.

My company currently has about fifty Azure Data Factory users, but not directly exposed to the solution compared to the developers; for example, members of corporate management and other teams apart from the development team are exposed to Azure Data Factory.

Shortly, there could be about two hundred users of Azure Data Factory within the company.

The developer team working directly on Azure Data Factory comprises ten individuals.

For the maintenance of the solution, my company has two to three staff, but it could reach up to eight or ten for the entire product. It's a mix of engineers and business analysts who handle Azure Data Factory maintenance.

I'd rate Azure Data Factory as eight out of ten.

My company is an end user of Azure.

Azure Data Factory allows us to provide BI service. We pull the data and put it into Synapse. From there, we create our dimension fact tables that are being used for reporting. 

The most valuable feature of Azure Data Factory is the improved flexibility compared to SSIS that we previously used for our ETL transformation. I also enjoy the ease of use for the backend JSON generator, the deployment solution, and the template management. All we need to do is create ARM templates.

Microsoft is constantly upgrading its product. Changes can happen every week. Every time you open Data Factory you see something new and need to study what it is. I would like to be informed about the changes ahead of time, so we are aware of what's coming.

In future releases, I would like to see Azure Data Factory simplify how the information of logs is presented. Currently, you need to do a lot of clicks and go through steps to find out what happened. It takes too much time. The log needs to be more user-friendly.

I have been using Azure Data Factory for two years.

Scalability depends on the use case. 

As far as customer service and support with Azure Data Factory, we are not always satisfied with the response time. However, once they attend to the issue, everything is good.

Positive

All we needed to do was create ARM templates and deployment is easy.

We deployed in-house. For deployment, we use ARM templates that are a part of Azure's deployment strategy. It's not only available for Data Factory, it is built in. It links with DevOps, then the ICD integration.

Azure Data Factory is a good tool. Given that the data platform ecosystem is provided by Microsoft, you know it is good.

I would rate the solution an eight out of ten overall.

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 primarily use Data Factory to ingest data. For example, if we need to pull data into our data warehouse from somewhere like Azure Event Hub or salesforce.com.

We have telemetry that streams into an Azure Event Hub, and Data Factory allowed us to move that data from the Event Hub into our data lake and reduce the cost of that compared to the other tooling we were using.

Data Factory's best features are simplicity and flexibility. It's been very easy to set up connections to different types of data sources to pull data into our warehouse.

Data Factory would be improved if it were a little more configuration-oriented and not so code-oriented and if it had more automated features.

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

I would rate Data Factory's stability eight out of ten.

I would rate Data Factory's scalability eight out of ten.

The initial setup was straightforward, and only one person was required for deployment.

I would rate Data Factory's pricing nine out of ten.

I think Data Factory is a good fit when you need a light, inexpensive way to ingest data. I would rate it eight out of ten.