On the surface, dbt and Datameer are both data transformation tools that aim to simplify the data transformation process, integrate with your cloud data warehouse such as a Snowflake, and apply software engineering best practices such as CI/CD to that process. The similarities end there.
Datameer offers a much easier, more inclusive user experience for all your personas – data engineer, analytics engineer, data analyst, and data scientist. The catalog-like data documentation, collaboration, easy data enrichment, deep data profiling, and Google-like search and discovery make Datameer a superior choice for your data transformation needs.
Dbt, which is short for data built tool, is a data transformation tool that enables data analysts and engineers to transform, test, and document data in their cloud data warehouse. With dbt, data teams work directly within the warehouse to produce trusted datasets for reporting, ML modeling, and operational workflows.
The primary language for dbt is their own SQL dialect. Anyone in the organization – typically data engineers and data analysts – who knows SQL can create SQL-based data models and link them into a pipeline. Dbt’s SQL dialect is designed specifically for data transformation, replacing boilerplate DDL/DML with simple SQL SELECT statements that infer dependencies, build tables and views, and run models in order.
The dbt tool not only lets users define data models but also offers a workflow that lets teams quickly and collaboratively deploy data transformation code following software engineering best practices like modularity, portability, CI/CD, and documentation. Under the covers, the product uses Git for version control, sharing, and collaboration.
First and foremost, organizations use dbt to transform raw data loaded into their cloud data warehouse into a consumable analytics form. This is the “T” in their ELT (Extract, Load, and Transform) process. All models are stored in the cloud data warehouse, and execution of the models is performed using the cloud data warehouse’s compute capabilities.
Organizations also use dbt to add software development best practices to their data transformation processes. As mentioned above, dbt also provides facilities to enable a software-development-like workflow. This includes the typical three phases of the software development lifecycle:
The use of Git under the covers allows organizations to use existing GitHub repositories for their data transformation models and to keep all their software libraries, including data models, all under one roof.
Datameer is a powerful SaaS data transformation platform that runs in Snowflake – your modern, scalable cloud data warehouse – that combines to provide a highly scalable and flexible environment to transform your data into meaningful analytics. With Datameer, you can:
Datameer provides a number of key benefits for your modern data stack and cloud analytics, including:
At the surface level, both Datameer and dbt are data transformation tools – the T in your ELT data stack. Both allow you to take raw data loaded from your data sources into your cloud data warehouse and transform the data into an analytics-ready form using the compute and storage of your cloud data warehouse. Both tools also help to add best practices to your data transformation process through capabilities such as data documentation.
However, Datameer offers a number of distinct differences and capabilities that go beyond what dbt offers, including:
Datameer offers a hybrid user experience that has three different user interfaces: a coding UI in SQL, a low-code, formula-driven spreadsheet-like UI, and a no-code, graphical UI. Each offers distinct ways to transform your data using different skills. Dbt only offers a single, SQL-coding and template language (Jinja) programming IDE and articulates that vision in this quote from their website:
At dbt Labs, we have developed strong opinions on how companies should practice analytics. Specifically, we believe that code, not graphical user interfaces, is the best abstraction to express complex analytic logic.
With Datameer, data transformation models can be mixed and matched within a data flow using the various three interfaces. Under the covers, models created by any of the three interfaces are translated into SQL views inside of your cloud data warehouse. And the overall data flow chain is maintained by Datameer by linking the views together.
Because Datameer offers three different UIs, any persona – data engineer, analytics engineer, data analyst, data scientist – can use the Datameer toolset with their existing skill set. Datameer also fosters collaboration between the various personas. With dbt, you need to have SQL skills and some simple programming skills, which really only target the data engineer persona.
Datameer’s spreadsheet-like UI and its ability to easily add file-based data make for a much easier path to enrich data, especially for data analysts who may not be highly SQL-savvy. Adding new, enriched columns is led by an easy, wizard-driven formula builder. With dbt, any data enrichment must be done with SQL formulas and coded in SQL.
Datameer maintains a rich set of both auto-generated and user-created data documentation that allows teams to easily discover and share knowledge about data models. The solution automatically documents system-level metadata and properties. Users can further enrich the information with wiki-style descriptions, custom properties and attributes, tags, and comments. Dbt only offers simple, auto-generated documentation that is taken from comments within their SQL code.
Teams can use shared workspaces to share, reuse, and collaborate around models to speed projects, divide up the workload, and ensure models are designed properly the first time. Different model types can be mixed-and-matched into larger dataflows for maximum flexibility and reuse. Catalog-like features such as comments, custom properties, tags, and others also allow teams to easily share information about the data and transformation. Dbt only offers limited collaboration via model sharing and reuse via references.
Datameer maintains a deep data profile that is expressed visually to users so they can see the full shape and contents of the data as they transform it. This easily allows users to identify invalid, missing, or outlying fields and values, as well as the overall shape of the data. Dbt only offers a snapshot of the data and does not maintain detailed data profiles, with users having to blindly write SQL with limited visibility into the data.
Datameer offers a Google-like faceted search that allows users to discover data models and datasets. The search covers all the information captured on the data, including system-level metadata and properties, descriptions, custom properties and attributes, tags, and comments. Dbt offers no search and discovery capabilities having just a simple project browser and IDE metaphor.
At the most basic level, Datameer and dbt share common characteristics around data transformation and helping teams apply engineering best practices for data transformation and engineering. From there, the two products deviate, with Datameer offering a much more inclusive and easier user experience that supports multiple personas, collaboration among team members, and a much deeper set of searchable, catalog-like data documentation.
Are you interested in seeing Datameer in action? Contact our team to request a personalized product demonstration.
|Data transformation||Data transformation|
|In cloud data warehouse||In cloud data warehouse|
|Three distinct UIs for code (SQL), low-code (spreadsheet-like), and no-code (graphical)||Single, SQL-coding UI/UX|
|UI/UX that supports all your personas: data engineer, analytics engineer, data analyst, and data scientist||Only supports personas with SQL skills – typically data engineers|
|Easy, no-code data enrichment via a wizard-driven formula builder in the spreadsheet UI||Data enrichment by coding SQL formulas|
|Shared workspaces, model reuse, mix-and-match of model types, and shared catalog-like data documentation facilitate collaboration||Only supports shared projects via GitHub integration|
|Maintains a deep, visual data profile that easily allows users to identify invalid, missing, or outlying fields and values, as well as the overall shape of the data||Simple views of the data, no data profiling|
|A rich set of catalog-like auto-generated and user-created data documentation, including system-level metadata and properties, wiki-style descriptions, custom properties and attributes, tags, and comments||Only simple, auto-generated documentation is derived from comments in SQL code|
|Google-like faceted search across all information captured on the data, including system-level metadata and properties, descriptions, custom properties and attributes, tags, and comments||No search and discovery capabilities|