dbt vs. Sigma Computing

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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We were able to set up client-facing embedded reports with ease and security. The interface is not difficult to learn, although we may not be aware of or lack the necessary expertise to utilize more advanced features that would likely benefit us.

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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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• Allows end users to easily dive into the data without having direct access to the table in our database management software.
• Can easily turnaround dashboards that are detailed and visually pleasing.
• Sigma is intuitive and as new features are rolled out it is easy to adopt and incorporate them into new and existing dashboards.

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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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• Sigma Computing does not allow custom ordering of pivot fields in pivot tables easily
• Sigma Computing lacks functionality for creating tables or sections that dynamically adjust to the browser window's height while maintaining a fixed height textbox at the bottom
• Sigma Computing does not provide straightforward options for formatting totals in tables, such as renaming 'Total' to 'Average', 'Team Total', etc
• Sigma Computing does not support searching by individual tab names within a workbook

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Sigma has helped us a lot and has become an integral part of our daily workflow. It would be difficult to switch to another platform and have to rebuild the numerous metrics and performance reports that we have already established

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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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It has a clean and modern interface. However, it is not completely intuitive. I think it would be better and easier to navigate with more Windows style drop down menus and/or tabls. There is a significant learning curve, but that may be due in part to the technical nature of this type of software tool.

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Yes, as long as you don’t conduct user error sigma is always up and running and waiting for you to complete your dashboards

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It depends, it loads quickly for smaller dashboards but when loading larger amounts of data it takes more time to do so

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They are very friendly and informative. They are quick in resolving our queries and help us understand very minute things as well. They are quick in creating feature tickets based on our custom requirements, and they would also create a bug ticket if there is any discrepancy and get that checked on time.

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Was not involved in implementation

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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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With Looker, to be effective, a substantial amount of coding & modeling needs to happen in LookML. Being another language to learn, users have to context switch again from at a minimum either SQL or Python into LookML. The concept of being able to source control, code review, and deploy your models is a plus though.

Tableau is the gold standard for data visualization, no question. Power users will be able to create dazzling content that Sigma won't necessarily be able to easily match. However, since development usually happens via an extract, helping other users troubleshoot is an arduous process. Trying to re-do or un-do all the transformations and calculations that cause a certain number is very difficult.

With Sigma, all the queries happen directly against Snowflake and you can see the query logs. The data modeling happens right in a tabular, spreadsheet-like manner, so within only a few minutes, substantial transformations can happen, with visualizations just a few more clicks away.

It is a cloud service offering that is able to expand based on your usage

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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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• Monitoring health of cloud platform has allowed the company to anticipate issues before they affect customers – Sigma prompted us building a canary monitoring process that provides customer container health.
• Customer success has used an activity report to discover customers running runaway processes that they were unaware of, creating an alert to contact the customer and prevent an embarrassing situation.
• Customer success uses the activity report to prompt conversations regarding increases or declines in behavior that led to increasing contract limits or addressing churn concerns.

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