Azure Data Factory Reviews

I can describe a scenario where I was tasked with developing a beta data trace and integration system. GitLab served as the data integration platform responsible for creating pipelines to extract data from various sources, including on-premises and cloud-based systems. Alongside GitLab Data Factory, we utilized Azure Logic Apps for orchestration and Azure Key Vault for securely storing data-related information. This combination enabled us to manage data extraction, transformation, and loading efficiently. The solution also involved Azure Data Lake for further data transformations, culminating in a comprehensive data processing engine that I played a key role in implementing.

The workflow automation features in GitLab, particularly its low code/no code approach, are highly beneficial for accelerating development speed. This feature allows for quick creation of pipelines and offers customization options for integration needs, making it versatile for various use cases. GitLab supports a wide range of connectors, catering to a majority of integration needs. Azure Data Factory's virtual enterprise and monitoring capabilities, the visual interface of GitLab makes it user-friendly and easy to teach, facilitating adoption within teams. While the monitoring capabilities are sufficient out of the box, they may not be as comprehensive as dedicated enterprise monitoring tools. GitLab's monitoring features are manageable for production use, with the option to integrate log analytics or create custom dashboards if needed.

The data flow feature in Azure Data Factory within GitLab is valuable for data transformation tasks, especially for those who may not have expertise in writing complex code. It simplifies the process of data manipulation and is particularly useful for individuals unfamiliar with Spark coding. While there could be improvements for more flexibility, overall, the data flow feature effectively accomplishes its purpose within GitLab's ecosystem.

Azure Data Factory could benefit from improvements in its monitoring capabilities to provide a more robust feature set. Enhancing the ease of deployment to higher environments within Azure DevOps would be beneficial, as the current process often requires extensive scripting and pipeline development. It is also known for the flexibility of the data flow feature, particularly in supporting more dynamic data-driven architectures. These enhancements would contribute to a more seamless and efficient workflow within GitLab.

I have been using Azure Data Factory for the past 3 years. 

I find GitLab to be quite stable overall. I haven't encountered significant issues with its stability and consider it a reliable platform.

In terms of scalability, there are a few aspects of GitLab that I find disappointing. For instance, the limitation on self-hosted integration run time to just four VMs restricts scalability, especially for handling large volumes of data. Improvements are needed in this area to support more than four VMs for scalability. The documentation regarding bandwidth support is unclear, making it difficult to assess the full scalability potential. While GitLab performs well in cloud scalability in terms of compute power, the limitations on self-hosted integration run time are a concern for certain use cases. scalability in GitLab is highly dependent on specific use cases and could benefit from enhancements in self-hosted integration run time capabilities.

As for technical support from Microsoft the response times and solutions can sometimes be delayed. 

Neutral

Our organization did not switch from a previous solution to Azure Data Factory; rather, we implemented Azure Data Factory as a new solution to enable cloud data processing capabilities.

The deployment was handled in-house, and initially, we had a team of five working on the project in 2019. 

We have approximately six to seven data engineers from various departments utilizing the solution in our organization. As for the frequency of using GitLab, it varies depending on the workload and projects, but on average, there is someone working on the platform at least several times a week, as our data engineers are involved in various tasks beyond GitLab.

I am aware of the pricing of Azure Data Factory, but I prefer not to disclose specific details.

In terms of handling complex data transformations and cleansing, Azure Data Factory is capable for simple to medium tasks, but for more complex tasks, we resort to custom coding solutions. Overall, I would recommend Azure Data Factory for data integration and management, and I would rate it an eight out of ten for its flexibility and ability to support third-party integrations.

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. 

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.

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.