dbt, Snowflake, Airflow, and the tools powering modern analytics engineering.
Learn how modern companies build scalable data systems using specialized tools that work together. From data sources to dashboards, understand each layer and choose the right tools for your organization.
Master advanced dbt patterns including incremental models, sophisticated testing strategies, and production deployment workflows. Transform your warehouse into a mature software development environment.
Learn the architectural differences, pricing models, and performance characteristics of the three major cloud data warehouses. Practical guidance for choosing the right platform for your analytics workload.
Learn when to use dimensional modeling's star schemas versus modern One Big Table approaches for analytical data. Compare both methods with hands-on examples using real e-commerce and SaaS data.
Learn to sync clean warehouse data back to Salesforce, Intercom, and other business tools. Build production-ready pipelines with incremental syncing, error handling, and comprehensive monitoring.
Master the fundamental choice between streaming and batch processing. Learn when each approach works best and how to build hybrid systems that combine both for optimal results.
When a customer moves cities or an employee gets promoted, does your data warehouse know what was true at the time of each transaction? This lesson teaches you exactly how to design, implement, and query SCD Type 1, 2, and 3 tables so your historical reports are always accurate.
Most dbt projects have tests. Few have a data quality strategy. This lesson takes you from basic generic tests to custom macros, statistical checks, and a tiered execution strategy that scales to production without grinding your pipeline to a halt.
Metric sprawl kills data trust. Learn how to implement a production-grade semantic layer using dbt Metrics (MetricFlow) and Cube.js — defining metrics once, serving them consistently everywhere, and never explaining why your revenue numbers don't match again.
ETL dominated data engineering for decades — but modern cloud data warehouses changed the rules. Learn why today's data teams load raw data first, transform it inside the warehouse, and how tools like dbt make it all work.
Running dbt in production means far more than a cron job. Learn how to build production-grade Airflow DAGs that handle upstream dependencies, partial failures, retries, and observability for your dbt transformation pipeline.
Most dbt incremental models work fine in development and silently destroy performance at production scale. This deep-dive lesson teaches you how partition pruning actually works in BigQuery, Snowflake, and Databricks, how to design configurable lookback windows for late-arriving data, and which incremental strategy — merge, insert_overwrite, or append — to use and when.
Stop manually stitching together CSVs and spreadsheets. This hands-on lesson walks you through setting up a real cloud data warehouse in Snowflake or BigQuery — for free — loading actual e-commerce data, and writing your first analytical SQL queries from scratch.
Silent data failures are more dangerous than loud ones — your pipeline runs green while bad data flows to your dashboards. Learn how to instrument dbt with Elementary and re_data for anomaly detection, Slack alerting, and systematic root cause analysis in production environments.
When your revenue dashboard shows unexpected numbers at 9 AM, can you trace the problem to its source in minutes or hours? This deep-dive lesson teaches you to build production-grade multi-hop lineage tracking across Airflow, dbt, and Spark using OpenLineage and Marquez — including programmatic graph traversal for automated impact analysis and root-cause debugging.
Shipping dbt changes without Git is like editing a financial spreadsheet with no undo button. This lesson teaches you the complete workflow: feature branches, automated CI checks with GitHub Actions, and controlled production deployments — with real code you can use today.
Data pipelines break silently when producers and consumers operate without formal agreements. This lesson walks through designing, enforcing, and evolving data contracts across the full ingestion-to-transformation boundary — with working Python validators, dbt integration, and a CI-enforced review workflow for managing schema changes.
Learn how to build a production-grade dbt promotion pipeline that uses Slim CI to run only modified models, deferred state to resolve dependencies against production, and webhook-based promotion gates to prevent bad code from ever reaching your business-critical data. This is the CI/CD architecture lesson that dbt's documentation glosses over.
Misconfigured data warehouse permissions are one of the most expensive and common mistakes in modern data engineering. This lesson teaches you how to design and implement a complete RBAC system in both Snowflake and BigQuery — covering virtual warehouse access, schema isolation, and dynamic row-level security policies that filter data based on who's asking.
Slow dbt models are almost never a dbt problem — they're SQL problems hiding behind dbt abstractions. This lesson teaches you how to read Snowflake Query Profiles and BigQuery Execution Details, diagnose contention with resource monitors, and make smart materialization decisions that actually reduce warehouse cost and latency.
Most dbt projects start clean and become tangled fast — business logic in staging models, 200-line mart files, copy-pasted CTEs everywhere. This lesson teaches you the staging, intermediate, and mart architecture from first principles, giving you the design intuition to structure a dbt project that stays maintainable as it grows to hundreds of models.
Full refresh syncs are quietly one of the biggest cost drivers in modern data stacks — and most teams don't realize it until the bills arrive. This lesson teaches you exactly how to configure incremental sync in both Fivetran and Airbyte, including how to choose cursor fields, when to use CDC, and how to avoid the silent data quality failures that trip up experienced engineers.
Medallion Architecture gives your data a clear, auditable path from raw to refined — but the implementation details are where most teams go wrong. This hands-on lesson shows you how to design and build production-grade Bronze, Silver, and Gold layers using dbt, Delta Lake, and cloud warehouses, with realistic code, common failure modes, and the discipline to make it stick.
Most self-serve analytics platforms either lock users out entirely or let them connect to anything and break everything. This lesson shows you the architecture that does neither — using dbt Exposures as governance contracts, a MetricFlow or Cube.dev semantic layer for consistent metric definitions, and properly governed Tableau and Looker access with row-level security baked in.
Learn how Airflow's interval-based scheduling model really works — and how to safely backfill months of historical data without duplicating records. This hands-on lesson covers catchup behavior, the backfill CLI, and the idempotent design patterns every data pipeline needs.