Mastering Data Quality at Scale: How We Combine SQL, DuckDB, and Polars

In the rapidly evolving landscape of 2026, ensuring data quality at scale remains one of the most significant challenges in enterprise data management.
At DQC.ai, we’ve developed a sophisticated platform that moves beyond simple validation to actually identify, improve, and prevent data errors using a powerful combination of machine learning and generative AI.

Our latest whitepaper, "How We Leverage SQL, DuckDB, and Polars for Efficient Data Processing," dives deep into the technical architecture that makes this possible. Here is a summary of how we’ve built a flexible, high-performance layer to handle today's complex data ecosystems.

The Power Trio: SQL, DuckDB, and Polars

To achieve universal connectivity and high performance, we’ve unified three core technologies into a single data processing layer:

• SQL & Data Virtualization: We use a unified interface that allows us to write data expressions once and execute them across 15+ different data sources, including Snowflake, Databricks, and SAP HANA. Our system handles dialect transpilation and lazy evaluation to optimize queries before they even hit the source

• DuckDB for In-Memory Processing: When we need to process static files (Parquet, CSV, JSON) or API data, DuckDB is our workhorse. Its vectorized execution engine and zero-copy processing allow for complex analytical queries without the overhead of traditional database loading

• Polars for High-Performance Dataframes: For intensive in-memory manipulation, we leverage Polars. Written in Rust, it is often 5-10x faster than pandas and provides superior memory efficiency through parallel execution

Data in an Agentic AI Environment

One of the most innovative sections of our whitepaper explores how this stack supports AI agents. Modern agents are often limited by context windows and token costs. We solve this through:

• Embedding-Based Context Retrieval: Using local models like Qwen3-Embedding to find relevant data without passing entire datasets to an LLM

• Intelligent Subset Selection: Using DuckDB and Polars to feed agents only the specific data slices they need for a task

• Batch Processing: A proprietary framework that handles dozens of tools and hundreds of intermediate steps to enrich millions of rows efficiently

Proven Performance

In production, this architecture allows us to perform data quality checks on millions of rows per minute and support thousands of concurrent agent calls with context lookup.
By minimizing data transfer and maximizing local execution, we provide a system that is both cost-efficient and blazingly fast.