Cracking Power BI Lineage: How We Built an AI-Powered Solution

In today's data-driven enterprise landscape, Microsoft Power BI has emerged as the dominant force in business intelligence, commanding over 30% market share and being actively used by 97% of Fortune 500 companies. For enterprise data teams, Power BI isn't just a reporting tool—it's the window through which business leaders view their organization's performance, make critical decisions, and drive key initiatives.

But here's the challenge that keeps data professionals awake at night: when those beautifully crafted Power BI dashboards display unexpected numbers or trends, how do you trace the data back to its source? How do you answer the inevitable question from the C-suite: "Where did this number come from, and can we trust it?"

This is where data lineage becomes mission-critical. Data lineage—the complete trail showing how data flows from source systems through transformations to final reports—is the foundation of data trust and governance. Without it, organizations are essentially flying blind, unable to validate their insights or troubleshoot data quality issues effectively.

At Alation, we discovered just how challenging this problem could be when our Power BI connector started hitting a wall that traditional engineering approaches couldn't scale past. In this blog, I’ll reveal how we cracked it.

The challenge: When traditional parsing hits its limits

Power BI connects to external databases through expressions written in MQuery (Microsoft Query Language)—a powerful but highly flexible language that allows users to define data connections in countless different ways. Think of MQuery as the "plumbing" that connects Power BI reports to their underlying data sources.

The challenge? Every organization (and often every user) writes these MQuery expressions differently. Some use simple, straightforward syntax, while others create complex, nested expressions with variables, custom functions, and non-standard formatting. This variability made it nearly impossible to create comprehensive parsing rules.

Our initial approach relied on handwritten regular expressions (regex)—essentially pattern-matching rules that could recognize and extract connection details like database servers, schemas, and table names from MQuery text. While this worked for common patterns, it quickly became a maintenance nightmare, leading to:

• Incomplete lineage: When our regex couldn't parse an MQuery expression, we simply couldn't generate lineage for that Power BI report

• Constant escalations: Customers would report missing lineage, requiring our engineering team to manually analyze new MQuery patterns

• Engineering bottleneck: Each new pattern required writing new regex rules, testing, and releasing updated connectors

• Competitive disadvantage: With Power BI adoption growing rapidly, our inability to provide complete lineage was becoming a critical gap

The traditional regex approach was fundamentally unscalable; we were playing an endless game of whack-a-mole against the creativity of thousands of Power BI users.

Why the Power BI lineage challenge demands innovation

The stakes for solving this challenge extended far beyond technical elegance. Incomplete Power BI lineage was harming:

Customer trust and business impact

Incomplete data lineage directly undermines confidence in data governance platforms. When business analysts can't trace their Power BI metrics back to source systems, they lose trust in both their reports and the tools meant to govern their data. 

Operational efficiency

Every parsing failure triggered a support cycle: customer escalation → engineering analysis → regex development → testing → release → customer coordination. This reactive model consumed significant engineering resources that could have been devoted to innovation rather than maintenance.

Scalability reality

The manual approach simply couldn't keep pace with the variety of MQuery expressions in production environments. We needed a solution that could automatically adapt to new patterns without engineering intervention.

With Power BI's rapid growth and evolving feature set, our ability to provide comprehensive, adaptive lineage parsing could become a competitive differentiator. Organizations evaluating data governance solutions specifically test Power BI integration capabilities—and for them, incomplete lineage is a deal-breaker.

The innovation: AI-powered hybrid parsing

Rather than continuing to fight the complexity of MQuery with increasingly sophisticated regex patterns, we took a fundamentally different approach: we enlisted artificial intelligence to understand and interpret these expressions the way a human data engineer would.

The core architecture

Our solution combines the best of both worlds through a hybrid parsing system:

1. Regex-first approach: For known, common MQuery patterns, we continue using fast, deterministic regex parsing.

2. AI fallback: When regex fails to parse an expression, we automatically send it to an AI model (Claude Sonnet via AWS Bedrock).

3. Intelligent caching: AI responses are cached to ensure the same MQuery expression doesn't require repeated AI calls.

This hybrid approach ensures optimal performance and cost-efficiency while dramatically expanding our parsing coverage.

How the AI component works

When the AI parser receives an unparseable MQuery expression, it doesn't just pattern-match—it actually understands the intent and structure of the query. The AI model analyzes the expression and returns structured metadata including:

• Database host and port information

• Database and schema names

• Table and column references

• Connection parameter details

What makes this particularly powerful is the AI's ability to handle variations that would be impossible to anticipate with regex:

• Non-standard formatting and spacing

• Nested expressions and variable substitutions

• Custom functions and complex transformations

• Parameterized connections with dynamic values

This AI-powered understanding transforms MQuery parsing from a brittle pattern-matching exercise into an intelligent interpretation process. Instead of trying to anticipate every possible way users might write their data connections, we now have a system that can adapt to new patterns automatically, effectively giving us a data engineer's understanding at machine scale.

Engineering for production scale

Integrating AI into a production data connector required solving several complex engineering challenges:

Performance optimization: AI models are inherently slower than regex, so we implemented sophisticated caching mechanisms that store AI results and reuse them across multiple parsing requests.

Cost management: To control costs, we designed intelligent batching logic that groups multiple MQuery expressions into single AI requests when possible, and comprehensive caching that ensures we never pay to parse the same expression twice.

Reliability: We built retry mechanisms, timeout handling, and graceful degradation so that AI parsing failures don't break the overall lineage extraction process.

Observability: We instrumented the system with detailed metrics tracking success rates, cache hit ratios, latency distributions, and cost per parsing operation, enabling data-driven optimization.

The results: From maintenance burden to innovation that scales

The impact of our AI-powered solution exceeded our expectations:

Immediate customer impact

• Dramatically improved lineage completeness: Thousands of previously unparseable MQuery expressions now generate accurate lineage

• Reduced support escalations: The adaptive nature of AI parsing means fewer "missing lineage" tickets

• Enhanced customer confidence: Complete Power BI lineage strengthens trust in the thousands of enterprise dashboards that display these details

Engineering efficiency

• Maintenance reduction: No more emergency regex updates for new MQuery patterns

• Future-proof scalability: The system improves automatically as AI models advance, without connector updates

• Resource reallocation: Engineering time previously spent on reactive regex fixes can now be focused on proactive feature development

Competitive advantage

• Comprehensive Power BI support: Our connector now handles the full spectrum of MQuery expressions, not just common patterns

• Adaptive capabilities: As Power BI evolves and users create new expression patterns, our system adapts automatically

• Market differentiation: The combination of deterministic and AI parsing provides both speed and comprehensive coverage

These results demonstrate that strategic AI integration can transform operational challenges into competitive advantages. What’s more, the journey from concept to production taught us several critical lessons that other engineering teams can apply to their own AI initiatives.

Key insights and lessons learned

AI excels where rules fall short

Many MQuery expressions that we had categorized as "impossible to parse" were successfully interpreted by the AI model with high accuracy. The AI's ability to understand context and intent, rather than just pattern-match, proved invaluable for semi-structured, variable formats.

Hybrid approaches optimize for reality

Rather than replacing regex entirely, our hybrid model recognizes that different parsing challenges require different solutions. Deterministic parsing for known patterns provides speed and predictability, while AI handles the edge cases to provide comprehensive coverage.

Caching is key for AI integration

Implementing aggressive caching didn't just reduce costs—it actually improved system performance by eliminating redundant AI calls. In production environments where similar MQuery patterns appear repeatedly, cache hit rates can exceed 80%.

Observability enables optimization

Building comprehensive metrics from day one allowed us to identify optimization opportunities, detect regressions early, and make data-driven decisions about model selection and prompt engineering.

Advice for engineering teams facing similar challenges

Embrace AI for complex, variable inputs

When traditional rule-based systems hit scalability walls due to input variability, AI can provide the adaptability needed. However, use AI strategically—not as a replacement for all logic, but as a complement to deterministic approaches where appropriate.

Design hybrid systems with fallback logic

Don't assume AI is always the answer. Build systems that use lightweight, fast, deterministic logic as the first line of processing, invoking AI only when needed. This approach optimizes for both performance and cost.

Optimize for production realities

AI integration requires careful attention to latency, cost, and reliability. Implement batching, prompt optimization, aggressive caching, and comprehensive retry logic. These operational concerns are as important as the core AI functionality.

Build observability first

Instrument your AI-powered systems with detailed metrics from the beginning. Track success rates, cache performance, latency distributions, and cost per operation. This data is essential for optimization and helps justify the investment in AI capabilities.

Cache aggressively and strategically

AI-generated outputs are often reusable, especially in enterprise environments where similar patterns appear repeatedly. Design caching strategies that balance memory usage with performance gains and cost reduction.

Looking forward: The future of intelligent data connectors

This Power BI lineage solution represents more than just a technical fix—it's a proof of concept for how AI can transform data integration challenges that were previously considered intractable. The hybrid parsing approach we developed is now being applied to other parsing workflows within Alation, including SQL query analysis and metadata extraction from various data sources.

As AI models continue to advance, we expect our parsing accuracy and efficiency to improve automatically, without requiring engineering intervention. This creates a self-improving system that becomes more valuable over time—a significant departure from traditional software that requires constant maintenance and updates.

For organizations evaluating their data governance strategies, this project demonstrates a critical principle: the best solutions often combine the reliability of traditional approaches with the adaptability of modern AI. The future of enterprise data tools lies not in replacing proven methods with AI, but in thoughtfully combining them to create systems that are both dependable and innovative.