Modern analytics warehouse stack

Snowflake Cortex provides comprehensive AI/ML capabilities directly within the Snowflake Data Cloud, enabling LLM operations without data movement.

Used here for: AI and LLM capabilities close to the warehouse data for analytics and application workflows.

dbt (data build tool) has become the standard for analytics engineering, providing SQL-based data transformation with software engineering practices. dbt Core is free and open-source (Apache 2.0).

Used here for: Transformation and semantic modeling layer used by analytics engineering teams.

Monte Carlo pioneered "data observability", applying infrastructure monitoring concepts to data quality with ML-powered anomaly detection. Pricing is enterprise-only (estimates $50,000-200,000+/year based on data volume).

Used here for: Data observability layer for freshness, volume, schema, and quality incidents.

Datafold provides data quality testing with automated diff capabilities for validating data migrations, transformations, and pipeline changes. Data Diff (open-source) compares tables across databases highlighting row-level differences.

Used here for: Data diff and CI checks before model changes break downstream dashboards.

Looker (Google Cloud) provides enterprise BI with a strong semantic layer (LookML) and emerging Gemini AI integration.

Used here for: Governed BI and semantic modeling for business users.

Sigma Computing provides a spreadsheet-like interface for cloud data warehouse analytics, targeting users comfortable with Excel but needing enterprise scale. Pricing is consumption-based through compute credits.

Used here for: Spreadsheet-like warehouse analytics for teams that need business-friendly exploration.

Great Expectations is the leading open-source framework for data validation and testing with Python-first approach. Core library (Apache 2.0) is free.

Used here for: Explicit data tests and expectations for pipelines and critical tables.