Azure Data Factory Reviews

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.

Our company uses the solution for data ingestion. 

It is beneficial that the solution is written with Spark as the back end. 

The solution is cloud-based and integrates well with other Azure products such as Synapse Analytics.  

There are limitations when processing more than one GD file. 

Data ingestion pipelines sometimes fail because of transient issues that have to do with the cloud network. It takes more than six hours to process or ingest 300,000 records and that is a long time. 

I have been using the solution for two years. 

The solution is new in the market and pretty stable because ADF is a little more codified than AWS. Synapse Analytics adds another tool for data. 

Stability is not quite at the level of Informatica or DataStage. 

The solution is scalable. 

For multi-tenant applications connected to multiple databases, Microsoft recommends a share box and a cell post integration run time. But a run time connecting to multiple sources has limitations and requires multiple shares connecting to your data if you are ingesting it from on-premises. 

Technical support is okay. Support is contracted or partnered with various companies but is fine as a first level. 

Most of the time, technical support has to connect with product engineers who troubleshoot issues. 

The setup is not very complex but requires intake, setting up integration services, and connecting to databases like Oracle before you push it to service.

We implemented the solution in-house. 

The pricing is pay-as-you-go or reserve instance. Of the two options, reserve instance is much cheaper. 

In the cloud, everything is service based and expensive. Users should be knowledgeable enough to maximize the solution. 

For example, it makes no sense to run integration services all day if you are not ingesting data because you pay for that usage. It is important to understand how the product works to manage it accordingly and keep costs down. 

I rate the solution a six 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.

I primarily use Data Factory for creating pipelines on cloud in terms of integrating multiple cloud services.

Data Factory's best feature is the ease of setting up pipelines for data and cloud integrations. It also has good integration with other Azure services.

Data Factory could be improved in terms of data transformations by adding more metadata extractions.

I've been using Data Factory for five years.

Data Factory's stability has improved following some initial issues.

Data Factory's scalability is good.

The initial setup was easy as it's a SaaS offering.

Data Factory is affordable.

I would give Data Factory a rating of eight out of 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.