How AI accelerates and improves data modeling

The data modeling bottleneck

Raw data is never ready for analytics or AI workloads out of the box. It requires transformation, cleaning, testing, and documentation to shape it into formats suitable for driving business decisions. This work, the heart of data engineering, frequently becomes a chokepoint for creating new production-ready datasets.

The pressure on data teams has only intensified with the emergence of generative AI use cases, which demand large volumes of high-quality data to produce useful results. Data engineers struggle to meet this demand using traditional methods alone. The good news is that generative AI itself can help address this challenge.

What AI data engineering means for modeling

AI data engineering leverages large language models (LLMs) trained on massive amounts of data, combining them with information from existing pipelines (database schemas, data models, tests, documentation, and metrics) to produce first drafts of artifacts based on natural language descriptions. Engineers can then refine, test, and deploy these outputs.

This approach cuts workload by automating the creation of assets that constitute a data pipeline. Rather than replacing data engineers, AI augments them. The results mirror what's happening in software engineering more broadly, where developers using AI assistance report significantly faster task completion and reduced time-to-deployment.

Accelerating the modeling workflow

Generating transformation code

Data transformations require selecting data from multiple sources and reshaping it into formats suitable for specific business use cases. This involves writing SQL or Python code that integrates data from multiple tables while correcting underlying issues like malformed fields or missing values.

AI can generate the base SQL statements data engineers need for their transformations, from simple queries to complex regular expressions and bulk edits on existing code. This assistance proves particularly valuable for junior engineers, though even senior engineers benefit when performing complex queries. By issuing instructions in natural language and allowing AI to write the code, engineers avoid wasting time looking up SQL syntax peculiarities.

When working with dbt, engineers can use AI to create transformation files that implement broader data modeling designs. These individual files serve as building blocks for the complete architecture, and AI can accelerate their creation while maintaining consistency with established patterns.

Automating documentation

Documentation is essential for data discoverability and usability, yet it often gets short shrift when deadlines loom. Good documentation tells downstream consumers where data comes from, how to use it, and how calculations were derived. This increases data confidence and makes datasets more accessible.

If you use dbt for transformations, you already get automated data lineage generation. AI data engineering goes further, creating descriptions for tables and fields based on their names, context, and similar assets in your projects. When you have hundreds of fields to document, this capability becomes extremely valuable.

AI-generated documentation provides an initial cut of descriptions for all tables and fields. Engineers can check these into source control, where they and team members can gradually improve the documentation over time. This approach eliminates the psychological barrier of starting from a blank page while ensuring documentation exists from day one.

Building comprehensive tests

Code doesn't always work as intended, and it may encounter issues when dealing with edge cases like values outside expected ranges or malformed data. Building tests for data transformation code provides confidence that transformations work as expected under various circumstances.

These tests can take several forms: unit tests that validate small portions of data model logic, data tests that ensure generated data is sound, and integration tests that verify the entire project end-to-end. Testing is one area that often gets deprioritized during crunch time, despite everyone knowing its importance.

AI data engineering can generate basic tests for new or revised data models, eliminating much of the upfront coding overhead. This reduces psychological barriers to creating adequate test coverage and frees engineers to focus on refining tests in ways that bring true value to dataset quality. Rather than spending time writing boilerplate test code, engineers can concentrate on edge cases and business logic validation that requires domain expertise.

Defining metrics and semantic models

AI can assist in defining consistent metrics that provide global availability across your organization for key values. A semantic layer framework defines common representations of data using standard business terminology, translating SQL or Python into common business language. This ensures consistency and democratizes data access by making key values available to all stakeholders.

Defining a semantic layer requires tools for creating and exposing new metrics globally. With AI data engineering, you can generate these models automatically and even ask the AI engine to recommend useful metrics based on your data transformation definitions. This capability helps teams identify valuable metrics they might not have considered and ensures metrics align with the underlying data structures.

Optimizing model design and performance

Converting between platforms

In data migration projects, adapting functions from one data warehouse to another (such as moving from Redshift to Snowflake or BigQuery to Databricks) can be time-consuming. Each platform has its own syntax, operators, and native functions requiring manual adjustments for compatibility.

AI can automate much of this process by converting code between platforms while preserving the original logic. This speeds up migrations and helps maintain consistency across environments. For data modeling work, this means existing models can be adapted to new platforms without complete rewrites, reducing the risk of introducing errors during migration.

Improving performance and readability

Beyond syntax conversion, AI can suggest improvements for performance and readability in data models. It can refactor subqueries into common table expressions (CTEs), reduce duplicated logic, and simplify joins. These optimizations reduce execution time, improve maintainability, and make models easier to understand without changing underlying logic.

For larger projects with complex transformations, small optimizations scale into meaningful gains in both performance and team collaboration. When models are easier to read and understand, new team members can contribute more quickly, and debugging becomes faster. Performance improvements translate directly to lower compute costs and faster query results for end users.

Designing dimensional models

Designing scalable data models is challenging, especially when working with multiple tables. AI can suggest optimized structures for fact and dimension tables, helping build more efficient and scalable models. Based on source system diagrams or descriptions, AI can propose dimensional model designs that align with best practices.

Once the structure is defined, AI can create mapping tables between transactional and dimensional models, generate corresponding dbt models using transactional tables as sources, and suggest naming conventions and folder structures to organize projects. This assistance is particularly valuable when establishing new subject areas or onboarding team members who are less familiar with dimensional modeling techniques.

Navigating the challenges

AI-generated code isn't always accurate. Sometimes it's incorrect or produces outputs that don't align with business requirements. Studies have shown that AI assistance can help enforce best practices in some areas while potentially encouraging shortcuts in others, such as security considerations.

The key is treating AI as an assistant within a mature analytics workflow process, such as the Analytics Development Lifecycle. Processes like this ensure alignment with business objectives and establish checkpoints to verify code quality and conformance to best practices.

Data teams should always validate AI output carefully, reviewing generated code and documentation for accuracy. Testing before deployment is essential. Running tests on any generated or modified code prevents issues from reaching production. Teams should never input confidential data, credentials, or proprietary business logic into AI tools, maintaining appropriate security boundaries.

Using AI output as a learning opportunity helps teams understand new techniques and patterns rather than simply copying code without comprehension. The quality of AI output often depends on prompt clarity and specificity, so teams should iterate and refine their prompts to improve results.

Building with AI-assisted modeling

dbt provides a consistent approach to developing, testing, deploying, and documenting data through SQL and YAML code, with rigorous testing and verification processes. When integrated with AI capabilities through tools like dbt Copilot, teams can automate routine tasks while maintaining the governance and quality standards that production data products require.

AI assistance can enforce code consistency using custom style guides, ensuring that generated models align with team conventions. This consistency becomes increasingly important as data warehouses grow and more team members contribute to the codebase. Clear patterns make projects immediately comprehensible to anyone navigating the code.

For data engineering leaders, the strategic question isn't whether to adopt AI assistance for data modeling, but how to implement it effectively. The teams that succeed will be those that establish clear conventions early, maintain discipline as systems grow, and treat AI as a tool that amplifies human expertise rather than replaces it.

The goal remains unchanged: creating data warehouses that serve as reliable foundations for analytics, where business users find data intuitive to work with, where data teams can efficiently build and maintain transformations, and where architecture can evolve alongside changing business needs. AI assistance makes achieving this goal faster and more sustainable, but it still requires thoughtful data modeling as a first-class concern.

Learn more about data modeling fundamentals and explore how AI is transforming data engineering workflows.

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