dbt vs. Reltio Connected Data Platform

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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The data model is really flexible, so it should function well across many industries. Reltio Connected Data Platform tends to add new features first to its industrial vertical customers, though, so it may be especially well suited if your use case is specifically supported.

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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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• integration with SFDC, D&B
• Reltio API allows us to build interfaces with ease.
• The beauty of Reltio is Consolidation at any time we can review data at field level from different sources (crosswalk) and configure match merge and survivorship rules to define operational value and approved data can be redistributed to the upstream or downstream systems.
• With Central data governance, enrichment with D&B allows creating trusted data and distribute to various systems.

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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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• UI does not support creating different profile views for an entity based upon customer types as example however we can achieve this by using sub entities ( sub classes). This feature is useful if we are creating profiles in reltio manually.
• Manual export every time we need to pick entities, fields etc does not allow to save templates. workaround is using postman creating predefined API with selected fields, filters and trigger export.
• UI does not support data masking which needed while we maintain PII information
• Potential Matches doesnt auto sort on confidence code so users need to manually select deselect relevant entities which is time consuming and painful process however this issue is when we have more than 4 entities as potential matches.
• It would be beneficial if UI displays data related errors (if any) between Reltio and SFDC.

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Good Feedback

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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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Love the UI

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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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We felt Reltio had the best user experience, with flexible graph database technology and a customizable UI. Also important was the ability to look at historical snapshots to see relationships as they existed as of a previous date.

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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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• unifying multiple sources of data = 10+
• Data quality on customer and vendors moved from 65% to 95%
• Reporting quality on operational, finance and sales improved from 70% to 95%

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