Azure Data Factory Reviews

The primary use case of this solution is for data integration.

It has built-in connectors for more than 100 sources and onboarding data from many different sources to the cloud environment.

It has everything we needed.

Being able to post your feedback and queries is a very good feature that is offered by Azure Data Factory.

Understanding the pricing model for Data Factory is quite complex. It needs to be simplified, and easier to understand.

We have experienced some issues with the integration. This is an area that needs improvement.

I have been using this solution for almost two years.

So far it's been stable. We have not experienced any issues.

I haven't put much thought into scalability.

I have contacted technical support.

If there are any queries, they have the provision to post them in the Data Factory GUI and we can contact the team directly. This is a very nice feature.

Any feedback or improvements that we post is acknowledged and taken care of.

The initial setup was straightforward. It was not complex.

Pricing is quite complex.

I have had feedback from many people and many of my team members, and they say that it is difficult to understand. 

Understanding the pricing of Data Factory is quite complex. 

It is not presented in a straightforward way.

I would definitely recommend this solution to anyone who is interested in using it.

I would rate Azure Data Factory an eight out of ten.

I primarily use Data Factory for data ingestion and B2B transformation.

Data Factory's best features are connectivity with different tools and focusing data ingestion using pipeline copy data.

Data Factory's monitorability could be better. In the next release, Data Factory should include integrations with open-source tools like Air Flow.

I've been working with Data Factory for about a year.

Data Factory is stable.

Data Factory is scalable.

Microsoft's technical support is good, so long as your company has a good relationship with them.

I previously worked with Talend, Matillion, and Fivetran.

Data Factory is expensive.

I would rate Data Factory seven out of ten.

We generally implement this product for data transformation for our clients. We create the pipelines and provide training before handing it over to them. We generally deal with large-scale organizations. I'm a senior solutions architect. 

I think the main benefit of this solution is the ease of use, especially for companies that have come from an SSIS type of background where they are used to Microsoft tools. 

If you have a very simple pipeline you can use Data Factory for transformations, but it's really for serious analytics. This is an excellent tool for pipeline orchestration; connecting the different components and activities as well as gathering data. It's an orchestration tool, not a transformation tool. The monitoring feature has drastically improved.

Data Factory is embedded in the new Synapse Analytics. The problem is if you're using the core Data Factory, you can't call a notebook within Synapse. It's possible to call Databricks from Data Factory, but not the Spark notebook and I don't understand the reason for that restriction. To my mind, the solution needs to be more connectable to its own services.

There is a list of features I'd like to see in the next release, most of them related to oversight and security. AWS has a lake builder, which basically enforces the whole oversight concept from the start of your pipeline but unfortunately Microsoft hasn't yet implemented a similar feature.

I've been using this solution for five years. 

From what I've seen this is a stable solution. 

The solution is easy to scale keeping in mind that Data Factory doesn't do any computations. We use it mainly to push the computations to Databricks or Synapse. Projects with our clients generally last a few months and only until they go into production. I believe the ability to increase is always there.

We typically do not use customer support, but there were a few cases several years ago as the product was moving to the cloud that things were not so stable and we contacted support services - they were very good. 

When I first started in this field, everything was basically Hadoop on-premise and Hadoop infrastructure. With the increase in cloud integrations, things have changed. Once the big data services got introduced, we were probably one of the few companies in North America that were actually into analytics and big data and we were the first to implement related Microsoft products in Canada.

The initial setup is straightforward. I'm a huge fan and user of CI/CD pipelines and never do deployments manually. It's all automated and deployment takes a few minutes.

Licensing costs of Data Factory are reasonable. The cost is mainly on the Synapse and Databricks side of things because they are the tools where the computations are done and where you need more nodes and servers.

It's important to study the solution before purchasing it. The problem in this market is that because most users are generally not very knowledgeable, they typically fall for services that are not compatible with their use case. Data Factory comes with all the transformations but that doesn't work for serious analytics customers who generally need to resort to Databricks or Synapse which involves training and education. Since it's a new field and everything has just blasted off, it's very hard for people to catch on.

In my opinion, Airflow still ranks as number one but I would rate Data Factory an eight out of 10. 

Our primary use case is data loading.

The most valuable features are data transformations.

The speed and performance can be further improved.

This solution should be able to connect with custom APIs.

We have been using this solution for one or two months.

The stability of the Azure Data Factory is very good.

The scalability is good. We have about five users for this solution and they are all developers.

The technical support for this solution is good.

We did not use another solution prior to this one.

The initial setup of the Azure Data Factory was simple. It took us one week to deploy it.

We deployed this solution with our in-house team.

Good

Our licensing fees are approximately 15,000 ($150 USD) per month. There are no additional fees.

We evaluated a solution by Talend before selecting the Azure Data Factory.

My advice for anybody who is implementing this solution is to first get in touch with the Microsoft team to get their support so that you don't spend too much on research.

I would rate this solution a eight out of ten.

We use the solution to move data from on-premises to the cloud.

The tool’s workflow is not user-friendly. It should also improve its orchestration monitoring.

I have been using the product since 2019.

The tool is a stable product.

I would rate the solution a seven out of ten.

Our primary use case is mainly for ETL and transforming the data and then using it for power VA. So there we are handling multiple projects. It is not just a single thing, but mainly used at the end is data analytics only.

I would like to see this time travel feature in Snowflake added to Azure Data Factory. In addition to taking care of data internally, how it is instead of using indexing, they have some different mechanisms to quickly execute the query instead of normal indexes. Both of these would be great to see implemented in future upgrades.

I have been working with Azure Data Factory for the past twelve months.

I am one hundred percent happy with the stability.

Technical support is excellent.

Positive

I would rate Azure Data Factory an eight on a scale of one to ten.

We are currently using it as an ETL (Extract, Transform, and Load) tool. We are using it to connect to various information providers or, in general, to various sources, to extract data, and then to insert it to our storage devices, databases, or data warehouses.

The two most valuable features of Azure Data Factory are that it's very scalable and that it's also highly reliable.

One area for improvement is documentation. At present, there isn't enough documentation on how to use Azure Data Factory in certain conditions. It would be good to have documentation on the various use cases.

Sometimes, it's really difficult to find the answers to very technical questions regarding certain conditions.

I've been using Azure Data Factory since 2019.

It has been stable so far.  

Azure Data Factory is a very scalable solution. Including internal developers and external consultants working for us, we have about 10-15 people using this solution.

We had been using various ETL tools during the years before moving to the cloud. We picked Azure Data Factory because we were moving towards the Azure cloud.

The initial setup is very easy.

We used a consultant as it was a big project. We had five to six specialists, including both internal and external employees, working on it. It took about about three to six months to complete.

Azure Data Factory is a very easy to use tool. If you want to extract, manipulate, and load data to any type of Azure repository, I recommend this solution. However, I would not recommend it if the manipulation of data is very deep and complicated.

I would rate this solution at eight on a scale from one to ten.

Used Azure Data Factory, Data Flow (private preview) and Databricks to develop data integration processes from multiple and varied external software sources to an OLTP application Azure SQL database. The tools are impressively well-integrated, allowing quick development of ETL, big data, data warehousing and machine learning solutions with the flexibility to grow and adapt to changing or enhanced requirements. I can't recommend it highly enough.

This solution will allow the organisation to improve its existing data offerings over time by adding predictive analytics, data sharing via APIs and other enhancements readily. It's as simple as extending the data pipelines with new modules and components. The solution is improving the organisation by offering something the organisation can grow with.

Data Flow and Databricks are going to be extremely valuable services, allowing data solutions to scale as the business grows and new data sources are added.

Data Flow is in the early stages — currently public preview — and it is growing into a tool that will offer everything other ETL tools offer. There are a few features still to come. The thing we missed most was data update, but this is now available as of two weeks ago. A feature that is confirmed as coming soon is the ability to pass in a parameter and filter, etc.