dbt vs. Geckoboard

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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Great value for the money. Excellent for smaller agencies with multiple projects and teams in a smaller space. We can quickly roll out mobile displays to help with a particular deployment push or monitoring a clients website engagement. It's also useful for showing live data without requiring analytics to run reports from a CRM, etc.

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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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• User self-help - the video tutorials and a robust publicly accessible knowledgebase.
• Geckoboard can aggregate data from a number of other apps into a singular dashboard.
• Sleek, dark UI.

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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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• A bit more flexibility in the number of columns would be nice.
• Ability to scale to mobile device (web).
• App capabilities are missing on Android.

My company did not renew due to changes in the organization.

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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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With a simple interface and available templates, creating basic dashboards is easy. Obviously depending on the data you want to visualize, there may be higher learning curves. That being said, they have a huge amount of integrations and extensible frameworks. If you are using anything made in the past ten years there is an API function or integration that can get it talking to the platform. As such, it's pretty easy to hit the main data points you want and get it on a cheap display in front of your team.

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The support levels vary based on the level of plan that you have but that's to be expected. Virtually everything except the Enterprise plan has basic chat/email support. While they are responsive they are not going to be much assistance in helping you figure out API calls or implementing 3rd party integrations. That is to be expected and the support community can pretty much get you in the right direction if you look.

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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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Domo is more robust. We do use both because Geckoboard is really easy to show the rest of our team without the need for additional licenses.

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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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• While we originally used this as an internal IS tool, we eventually have expanded it to be used by nearly every department.
• Because pricing is monthly, we can grow or decrease our usage based on our current client needs.
• Because it is low cost and easy to deploy, we can utilize it in place of considerable resources in analytics and reporting by delivering snapshots of data without pulling reports.

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