CData Sync vs. dbt

Evidently, CData Sync is an excellent middleware tool that is perfect for syncing data between systems. It is especially suitable and works well on SQL servers, DB2, MySQL, and Snowflake, and some of their brothering domains. However, it is a limitation in working with Sage 50 API. But if it is extracted with ODBC, it works well.

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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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• Effortless use of no-code interfaces on data pipelining on.
• High demand numbers of sources and destinations.
• Works with even snowflake, with no error occurrence.
• It uses ETL - (Extract / Transfer / Load).
• It enhances data extraction and database support.

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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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• Although efficient for SQL servers and MySQL, as well as Snowflake. It is not strong for other database engines, and an upgrade on this would do a lot.
• The installation process is manual as opposed to the cloud installation it should be.
• A feature of syncing auto increment ID key, that will help in existing data management.

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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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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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Tableau is another similar software tool, unlike CData Sync, rather transforms data into actionable insights. While CData Sync works with automation, Tableau uses a drag box on its AVA, which in turn slows the work speed on the syncing of data. Over the years, I enjoyed the friendly and customizable option of CData Sync over Tableau's.

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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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• Reserves resources by covering human power via its automated sync method.
• Saves time: on launching data collectively into a compatible domain.

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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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