Microsoft's Azure Data Factory is a service built for all data integration needs and skill levels. It is designed to allow the user to easily construct ETL and ELT processes code-free within the intuitive visual environment, or write one's own code. Visually integrate data sources using more than 80 natively built and maintenance-free connectors at no added cost. Focus on data—the serverless integration service does the rest.
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dbt
Score 9.0 out of 10
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dbt is an SQL development environment, developed by Fishtown Analytics, now known as dbt Labs. The vendor states that with dbt, analysts take ownership of the entire analytics engineering workflow, from writing data transformation code to deployment and documentation. dbt Core is distributed under the Apache 2.0 license, and paid Teams and Enterprise editions are available.
$0
per month per seat
Pricing
Azure Data Factory
dbt
Editions & Modules
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No answers on this topic
Offerings
Pricing Offerings
Azure Data Factory
dbt
Free Trial
No
Yes
Free/Freemium Version
No
Yes
Premium Consulting/Integration Services
No
Yes
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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More Pricing Information
Community Pulse
Azure Data Factory
dbt
Features
Azure Data Factory
dbt
Data Source Connection
Comparison of Data Source Connection features of Product A and Product B
Azure Data Factory
8.0
8 Ratings
2% below category average
dbt
-
Ratings
Connect to traditional data sources
9.08 Ratings
00 Ratings
Connecto to Big Data and NoSQL
7.18 Ratings
00 Ratings
Data Transformations
Comparison of Data Transformations features of Product A and Product B
Azure Data Factory
8.0
8 Ratings
0% above category average
dbt
9.7
8 Ratings
19% above category average
Simple transformations
9.08 Ratings
10.08 Ratings
Complex transformations
7.08 Ratings
9.48 Ratings
Data Modeling
Comparison of Data Modeling features of Product A and Product B
Azure Data Factory
7.2
8 Ratings
8% below category average
dbt
9.1
8 Ratings
15% above category average
Data model creation
7.06 Ratings
9.78 Ratings
Metadata management
7.07 Ratings
8.78 Ratings
Business rules and workflow
7.08 Ratings
9.08 Ratings
Collaboration
7.97 Ratings
10.06 Ratings
Testing and debugging
6.08 Ratings
8.08 Ratings
Data Governance
Comparison of Data Governance features of Product A and Product B
Well-suited Scenarios for Azure Data Factory (ADF): When an organization has data sources spread across on-premises databases and cloud storage solutions, I think Azure Data Factory is excellent for integrating these sources. Azure Data Factory's integration with Azure Databricks allows it to handle large-scale data transformations effectively, leveraging the power of distributed processing. For regular ETL or ELT processes that need to run at specific intervals (daily, weekly, etc.), I think Azure Data Factory's scheduling capabilities are very handy. Less Appropriate Scenarios for Azure Data Factory: Real-time Data Streaming - Azure Data Factory is primarily batch-oriented. Simple Data Copy Tasks - For straightforward data copy tasks without the need for transformation or complex workflows, in my opinion, using Azure Data Factory might be overkill; simpler tools or scripts could suffice. Advanced Data Science Workflows: While Azure Data Factory can handle data prep and transformation, in my experience, it's not designed for in-depth data science tasks. I think for advanced analytics, machine learning, or statistical modeling, integration with specialized tools would be necessary.
The prerequisite is that you have a supported database/data warehouse and have already found a way to ingest your raw data. Then dbt is very well suited to manage your transformation logic if the people using it are familiar with SQL. If you want to benefit from bringing engineering practices to data, dbt is a great fit. It can bring CI/CD practices, version control, automated testing, documentation generation, etc. It is not so well suited if the people managing the transformation logic do not like to code (in SQL) but prefer graphical user interfaces.
It allows copying data from various types of data sources like on-premise files, Azure Database, Excel, JSON, Azure Synapse, API, etc. to the desired destination.
We can use linked service in multiple pipeline/data load.
It also allows the running of SSIS & SSMS packages which makes it an easy-to-use ETL & ELT tool.
So far product has performed as expected. We were noticing some performance issues, but they were largely Synapse related. This has led to a shift from Synapse to Databricks. Overall this has delayed our analytic platform. Once databricks becomes fully operational, Azure Data Factory will be critical to our environment and future success.
dbt is very easy to use. Basically if you can write SQL, you will be able to use dbt to get what you need done. Of course more advanced users with more technical skills can do more things.
We have not had need to engage with Microsoft much on Azure Data Factory, but they have been responsive and helpful when needed. This being said, we have not had a major emergency or outage requiring their intervention. The score of seven is a representation that they have done well for now, but have not proved out their support for a significant issue
The easy integration with other Microsoft software as well as high processing speed, very flexible cost, and high level of security of Microsoft Azure products and services stack up against other similar products.
I actually don't know what the alternative to dbt is. I'm sure one must exist other than more 'roll your own' options like Apache Airflow, say, bu tin terms of super easy managed/cloud data transforms, dbt really does seem to be THE tool to use. It's $50/month per dev, BUT there's a FREE version for 1 dev seat with no read-only access for anyone else, so you can always start with that and then buy yourself a seat later.
