dbt vs. Oracle Autonomous Database

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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Pro - Stability. Does everything anyone could need. If it's not there it will be on the next update. There is plenty of support for it. It's been around for a long time and it's reliable. The support is well documented and has a great reputation. Cons - Errors have been found in the documentation provided by Oracle with guidelines, etc. Oracles salespeople have a reputation of being obnoxious and condescending.

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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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• Robust - this product doesn't have a lot of downtime. It's less prone to errors than some other tools I've worked with.
• Scalable - we can keep adding more things to it. We haven't hit any roadblocks when we've tried to do more with our database.

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• 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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• There is no access to the physical host of the DB. This is expected from a managed DB. Everything must be done through the console or via API calls. This is a new learning curve for the DBAs.
• Due to the lack of physical host access, certain features are not supported, such as Transportable tablespaces and Oracle LogMiner.
• Certain special data types, (such as XMLType) are not allowed; be sure the app vendor certifies their product on this platform.

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Because it does exactly what we need: it enables us to manage our development and testing database environments in a quick and simple way without requiring support from a database administrators team.

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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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The product is continuously evolving and new features are added frequently. Management options through the OCI (Oracle Cloud Infrastructure) console and through the command line and API are being enhanced frequently.

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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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I found Oracle Autonomous Database very secure to store data and private information.I always feel secure with Oracle Autonomous Databases disaster recovery features.It is very effective to build applications for mobile and desktop devices lesser code using a low code development framework namely Oracle Application Express (ApEx).

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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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• Oracle Autonomous Database has a wide range of warehouses, which is competent and of high performance.
• The transactional processing power that Oracle Autonomous Database outlines are completely important and digital.
• The efficiency of Oracle Autonomous Database data encryption fosters security measures, a form that demands more threat detectors.

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