Data Vault Automation Tools Supporting DataOps Practices

In the era of big data, implementing efficient Data Vault architectures is critical for organizations seeking scalable and agile data warehousing solutions. DataOps—a set of practices aimed at improving collaboration and automation across data teams—plays a vital role in modern data management. Data Vault automation tools that align with DataOps principles help streamline data integration, ensure data quality, and enhance agility.

Below are some of the leading Data Vault automation tools that support DataOps practices, including their strengths and weaknesses.

dbt core and dbt cloud

dbt is a transformation workflow tool that supports data modeling and automation, widely adopted in DataOps environments. While not specifically designed for Data Vault, dbt is often used to automate the transformation layer in Data Vault implementations with its coding approach, particularly in cloud environments.

Key Features: SQL-based transformation is its main focus, data lineage tracking, modular code organization, and popularity. Easy to start, make a POC with free dbt core.

Weaknesses:

  • Not Dedicated for Data Vault as it requires customization and external plugins (such as AutomateDV, datavault4dbt) be used effectively for Data Vault modeling.
  • Coding approach and limited GUI - lack of a graphical user interface can make it less accessible to non-technical users. Requires discipline and architectural guidance in order to avoid creating technical debt in the long run.
  • External integrations needed for version control, CI/CD and workflow orchestration (DAG, Airflow). This results in a need to maintain and administer multiple tools, might become challenging in enterprise setups. dbt focuses on transformations.

Agile Data Engine

Agile Data Engine (ADE) is an all-in-one SaaS data platform that supports Data Vault 2.0 modeling and DataOps practices. It provides end-to-end automation for data pipelines, including automated database schema changes, standardized transformations, built-in CI/CD and workflow management. Supports multiple Cloud DW vendors.

Key Features: Fully automated and built-in Data Vault 2.0 modeling, strong data modeling focus (also Kimball and other modeling methodologies), standardization, multiple load patterns and architecture templates. Automated schema changes, integrated workflow orchestration, DataOps practices observability. Support for multiple cloud data warehouses, makes it relatively easy to migrate between data warehouses between clouds which is beneficial for regulated institutions (DORA regulations).

Weaknesses:

  • Cloud native - primarily designed for cloud environments, limited support for on-premises setups.
  • Low-code approach might be not suitable for all users, especially for data engineers with software development background.
  • Small DWH teams - Agile Data Engine is designed especially for Enterprise Data Warehouses where multiple engineers or teams are developing data products concurrently

VaultSpeed

VaultSpeed is a specialized Data Vault automation tool automating the design, generation, and deployment of Data Vault models. It supports various cloud and on-premises platforms and integrates well with popular ETL tools.

Key Features: Automated Data Vault modeling, integration with ETL tools, multi-platform support, metadata management, and model versioning.

Weaknesses:

  • Vaultspeed doesn't provide a runtime environment - it generates scripts (DDL and DML) that must be executed by other tools, with DDL and DML processes being separate. This can lead to significant administrative and integration challenges, especially when coordinating across multiple tools. Vaultspeed offers valuable capabilities in data vault modeling (not supporting other methodologies), it is not a comprehensive DataOps platform as it lacks true CI/CD capabilities, and scheduling integration with other tools must be set up manually. A typical production setup often involves using Vaultspeed alongside additional tools like dbt, Snowflake, git, and Airflow, which complicates the process.
  • Cost - the licensing model is based on code generation (releases), where each release consumes Vaultspeed Allocation Units (VAU’s), making it quite costly overall.
  • Complexity: Initial setup can be complex and time-consuming, requiring significant configuration steps.

Datavault Builder

Datavault Builder is designed specifically for Data Vault automation, providing a fully integrated environment for Data Vault 2.0. It includes features like automated ETL/ELT generation, model visualization, and built-in testing, enabling teams to develop, test, and deploy data vault models.

Key Features: Real-time data integration, automated model generation, ETL/ELT automation, built-in testing and validation, and collaboration features. Robust automation features for the Business and Publish layers, streamlining the creation of business logic
DataVault Builder provides a data preview feature which helps in identifying business keys for hubs and includes checks like key uniqueness validation, ensuring the integrity of the model.
The tool uses Domains or Subject Areas to organize parts of the data model, visually represented as colored clouds around objects in the data model view.

Weaknesses:

  • Model Metadata Storage - DV Builder stores its model metadata repository in the same database as the actual data, which can complicate maintenance and pose risks in terms of performance and data management. Additionally, the Publish layer is implemented as SQL views, with staging and Data Vault materialized, which may not be optimal in all scenarios.
  • Installation Complexity - the installation process is dockerized and tied to a specific database instance, which can increase the effort required for maintenance, configuration, and administration. This can be a barrier for teams without strong DevOps expertise.
  • Portability Limitations between different database systems, as the Data Vault Builder’s development work is tightly coupled to a specific database instance.

Wherescape

Wherescape is a data automation tool that supports Data Vault 2.0 methodology and aligns well with DataOps practices. It provides automated data modeling, ETL/ELT generation, and metadata management, helping teams automate repetitive tasks and focus on higher-value activities.

Key Features: Automated ETL/ELT generation, metadata-driven development, integration with various data platforms, support for agile development.

Weaknesses:

  • Learning Curve: The interface can be challenging for new users, requiring training and practice. The interface looks pretty awful by modern standards.
  • Cost: Licensing can be expensive, making it less accessible for smaller organizations.
  • Flexibility: Limited flexibility for custom automation outside the provided templates.

Coalesce

Coalesce is a data transformation tool designed to support automated Data Vault modeling. It focuses on streamlining data integration and transformation processes, enabling automation of Data Vault structures and provides column lineage for the loads.

Key Features: Automated data transformation, data modeling and lineage capabilities (including column level lineage), cloud-native architecture, collaborative development environment.

Weaknesses:

  • Limited to Snowflake Cloud
  • Unstable - fewer features and community support compared to more established tools.
  • Resource Intensive Can be resource-intensive, requiring substantial cloud resources for optimal performance.