WhereScape vs. DBT and Keboola: Unleashing Data Automation and Integration Powerhouses

In the realm of data automation and integration, organizations have a wide range of tools to choose from. Two notable players in this space are WhereScape, offering WhereScape 3D and WhereScape RED, and DBT (Data Build Tool) and Keboola. In this article, we will compare these technologies, highlighting their key advantages and benefits. By understanding the unique strengths of WhereScape 3D and WhereScape RED, as well as DBT and Keboola, organizations can make informed decisions to optimize their data automation and integration processes.

WhereScape 3D and WhereScape RED: Streamlining Data Automation and Integration

WhereScape 3D is a comprehensive data modeling and discovery tool that enables organizations to gain insights into their data architecture and accelerate data integration processes.

Key advantages include:

Unified Data Modeling — WhereScape 3D offers a unified view of the data landscape, facilitating efficient data modeling, data modeling automation, metadata management, and impact analysis. This accelerates the design and development of data solutions and promotes collaboration among data teams.

Automated Documentation — WhereScape 3D automates the generation of comprehensive documentation, ensuring that data models and business rules are captured and maintained accurately. This reduces manual effort, enhances data governance, and improves overall data quality and understanding of the data.

WhereScape RED

WhereScape RED is a powerful data automation tool enabling organizations to rapidly design, develop, deploy, and operate data solutions.

Key advantages include:

End-to-End Automation — WhereScape RED automates the entire data lifecycle, from data integration to code generation, testing, and deployment. This accelerates time to market, reduces manual effort, and increases development efficiency.

Code Generation and Deployment — WhereScape RED generates optimized code for popular data platforms, such as cloud-based data warehouses, enabling seamless deployment across environments. Code generation is done via Powershell and Python templates so it is open for customization. This ensures consistency, reduces errors, and enhances scalability.

DBT and Keboola: Enabling Data Transformation and Integration

DBT (Data Build Tool)

DBT is an open-source tool designed specifically for data transformation.

Key advantages include:

Transformation Logic — DBT allows users to define and generate complex data transformation logic using SQL (via Python). It provides version control and testing capabilities, ensuring the reliability and reproducibility of data transformations.

Collaboration and Community — DBT benefits from an active and supportive community, offering shared knowledge, resources, and plugins. This fosters collaboration and enables users to leverage a wide range of community-contributed transformations.


Keboola is a cloud-based data integration and transformation platform that enables organizations to orchestrate complex data workflows.

Key advantages include:

Pre-Built Connectors — Keboola offers a vast library of pre-built connectors, enabling seamless integration with various data sources and platforms. This simplifies the data ingestion process and reduces time spent on manual integration tasks.

Scalability and Flexibility — Keboola’s cloud-based architecture provides scalability and flexibility, allowing organizations to process large volumes of data efficiently. It supports both batch and real-time data processing, empowering users to adapt to evolving data needs. Another advantage of Keboola is its quick implementation time and ready learning program.

Keboola and data modeling

SQL-based data modeling tools, such as SQL DBM (Database Modeler), can be a good fit with Keboola. Keboola is a cloud-based data integration and transformation platform that allows organizations to orchestrate complex data workflows. It provides pre-built connectors and a flexible architecture for seamless integration with various data sources and platforms.

When it comes to data modeling, SQL DBM offers a visual and collaborative environment for designing and managing database structures using SQL. It allows users to create entity-relationship diagrams, define tables, columns, relationships, and generate SQL scripts for database creation.

By integrating SQL DBM with Keboola, organizations can leverage the strengths of both tools. SQL DBM enables efficient and collaborative data modeling, while Keboola provides the infrastructure and capabilities to ingest, transform, and orchestrate data workflows seamlessly. This combination allows organizations to design their data models using SQL DBM and then leverage Keboola to integrate, transform, and analyze the data efficiently.

Together, SQL DBM and Keboola offer a powerful solution for organizations looking to streamline their data modeling and integration processes, enabling them to build robust and scalable data solutions. But you still need to keep in mind to evaluate the current state of your analytical platform to find the best fit for your cause.


WhereScape 3D, WhereScape RED, DBT, and Keboola are all powerful tools that address different aspects of data automation and integration. WhereScape 3D and WhereScape RED excel in providing end-to-end automation, streamlining data modeling, discovery, and deployment processes. These tools provide a unified and streamlined approach, enabling faster time to market, improved collaboration, and enhanced data governance. On the other hand, DBT and Keboola focus on data transformation, offering flexible SQL-based transformations and comprehensive data. Ultimately, the choice depends on the specific needs and objectives of an organization.

In conclusion, WhereScape 3D, WhereScape RED, DBT, and Keboola all bring valuable capabilities to the table, enabling organizations to automate and integrate their data workflows effectively. By evaluating a project’s specific requirements, organizations can choose the tool that best aligns with their goals, accelerates their data initiatives, and maximizes the value derived from their data assets.

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