dbt vs. WSO2 Enterprise Service Bus

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.

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WSO2 ESB is an awesome product for companies looking to venture into the world of SOA with an ESB. They have a lot of other products too that can work really well with their carbon infrastructure. The interface is simple for deploying and managing proxy services. You can also write custom modules within the ESB using Java with IDE like Eclipse

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• dbt supports version control through GIT, this allows teams to collaborate and track the data transformation logic.
• dbt allows us to build data models which helps to break complex transformation logic into simple and smaller logic.
• dbt is completely based on SQL which allows data analyst and data engineers to build the transformation logic.
• dbt can be easily integrated with snowflake.

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• One of the basic requirement of an ESB product is that it should be able to support transformation. WSO2 ESB provides support of XSLT, so you can transform your request to whatever format. Moreover, transformations like converting your xml payload into JSON and JSON payload to XML are out of the box available.
• WSO2 ESB provides a scheduler feature, by which you can configure your own scheduler to call a proxy service at a particular time of day or or initiate sequence.
• WSO2 ESB provides excellent error handling techniques, WSO2 ESB provides detailed error handling scenarios to tackle all the situations. WSO2 ESB also provides custom error handling by which you can make your own custom error message before sending it back to client.

• Field-level lineage (currently at table level)
• Documentation inheritance - if a field is documented the downstream field of the same name could inherit the doc info
• Adding python model support (in beta now)

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• While it's easy to configure for a quick start, it is not so easy to deploy by yourself in a complex production scenario.
• Not very stable for production usage, we encountered several trivial bugs that make us believe that this product is still not widely adopted.
• Lack of a built in mechanism for auto-restart in case of an application server crash.

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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.

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Compared to competitors the overall experience has been fine

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Lack of auto-restart built-in capabilities. In case of running out of memory there are no built-in methods to recover from a crash, just for example, Oracle WebLogic Node Manager.

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The product is performing well and consuming few resources

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Our experience with the WSO2 support has beent satisfactory

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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.

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It's the only one truly open source and free.

Adding a server node is really straightforward, there are just few point in the configuration files.

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• Simplified our BI layer for faster load times
• Increased the quality of data reaching our end users
• Makes complex transformations manageable

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• Very well documented tutorials and case studies makes it easy to learn.
• It has a really supportive community
• It is fast and it can easily handle 300 tps of average use on a VM with 4Gig RAM