dbt learning path: everything to study, topic by topic

dbt_project.yml configuration, directory layout (models/, seeds/, macros/, tests/, snapshots/, analyses/), profiles.yml, and project-level settings

SQL and Python model files, model configurations, config blocks, model naming conventions, and the model compilation process

ref() function for model dependencies, DAG construction and visualization, model selection syntax, upstream and downstream dependencies

source() function, source definitions in YAML, source freshness checks, loaded_at_field configuration, warn_after and error_after thresholds

CSV seed files for static reference data, seed configuration, analyses for ad-hoc SQL queries, and when to use seeds vs sources

Table, view, incremental, and ephemeral materializations; custom materializations; choosing the right materialization for different use cases

Staging, intermediate, and mart layers; naming conventions (stg_, int_, fct_, dim_); one-source-per-staging-model rule; model organization patterns

Schema YAML files (_schema.yml), model properties, column descriptions, config inheritance (project > directory > model), meta properties

Model selection syntax (+ and @ operators), tags, paths, graph operators, dbt ls, state-based selection, intersection syntax

Built-in generic tests (unique, not_null, accepted_values, relationships), test configuration, test severity, where clause for tests

Singular tests in tests/ directory, custom generic tests as macros, test macros with arguments, dbt_utils test helpers

doc() blocks, markdown descriptions, dbt docs generate, dbt docs serve, auto-generated documentation site, persist_docs config

Exposures for downstream dependencies (dashboards, ML models), metrics definitions, semantic layer, exposure owners, and maturity levels

dbt unit tests in YAML, expected output definitions, mocking refs with input fixtures, test execution, and differences from data tests and schema tests

Designing comprehensive dbt testing: custom generic tests, unit tests, contract tests, and test coverage strategies

dbt-expectations package for statistical and distributional tests, custom generic tests at scale, test coverage strategies, parameterized tests, and test orchestration patterns

Freshness SLAs beyond source freshness, anomaly detection in transformation outputs, automated alerting pipelines, Elementary integration, and observability dashboards

Jinja syntax in dbt (variables, control structures, filters), set and do blocks, whitespace control, compile-time vs run-time context

Macro creation and invocation, macro arguments, return values, dispatch pattern for cross-database compatibility, adapter macros

packages.yml, dbt Hub, installing packages, dbt_utils, dbt_expectations, codegen, private packages from git, package version pinning

Incremental strategies (append, merge, delete+insert, insert_overwrite), is_incremental() macro, unique_key, lookback windows, full_refresh

Type-2 slowly changing dimensions with dbt snapshots, timestamp and check strategies, snapshot configuration, invalidate_hard_deletes, snapshot freshness

Pre-hook and post-hook configurations, on-run-start and on-run-end hooks, operations (dbt run-operation), grant macros, warehouse-level operations

dbt Python models using Snowpark or PySpark, DataFrame API usage, when to use Python vs SQL models, limitations, and environment dependencies

Migrating from stored procedures and legacy ETL to dbt, phased adoption strategies, side-by-side validation, audit comparisons, and organizational change management for dbt adoption

Micro-batch incremental models, streaming integration patterns with Kafka/Kinesis, near-real-time transformation approaches, materialized views, and hybrid batch-streaming architectures

Model contracts for enforcing column types and constraints, model versioning for backward compatibility, contract enforcement, latest_version config

Model groups for ownership boundaries, access modifiers (private, protected, public), cross-group ref restrictions, model governance at scale

dbt debug command, compiled SQL inspection, log analysis, common error patterns, run_results.json analysis, node status interpretation

dbt Semantic Layer architecture, metrics definitions, semantic models, entities, dimensions, measures, and metric consistency across BI tools

dbt Mesh architecture for multi-project setups, cross-project references, model contracts for public interfaces, access modifiers, and groups for team boundaries

MetricFlow engine, derived metrics, cumulative metrics, conversion metrics, metric queries, Semantic Layer API, and BI tool integration

Building a semantic layer with dbt's MetricFlow and integrating with BI tools for consistent business metric definitions

Managing data lineage with dbt docs, column-level lineage, access policies, and integrating with data catalogs

Enforcing contracts between teams, versioned models for backward compatibility, breaking change detection, contract enforcement policies, and API-style model interfaces

dbt Mesh cross-project references, dependency management between projects, shared model libraries, project boundaries, and multi-project governance at scale

PII tagging and classification, column-level lineage for compliance, audit logging of transformations, access controls for sensitive data, and regulatory reporting

dbt Cloud CI jobs, slim CI with state comparison, GitHub Actions integration, environment promotion (dev, staging, prod), dbt build command

dbt artifacts (manifest.json, catalog.json, run_results.json, sources.json), state comparison, artifact-based selection, deferred runs

dbt Cloud job orchestration, Airflow/Dagster integration, model parallelism, warehouse optimization, thread configuration, performance profiling

dbt Cloud IDE, job scheduler, CI/CD jobs, slim CI, dbt Explorer, environment management, and Cloud-specific features vs. dbt Core CLI

Structuring large dbt projects: modularization, project splitting, dbt-core packages, and performance optimization

Orchestrating dbt Cloud jobs: job dependencies, CI/CD integration, slim CI, and monitoring with dbt Cloud APIs

Model optimization techniques, incremental strategy selection for large datasets, warehouse-specific tuning (Snowflake clustering, BigQuery partitioning, Redshift sort keys), thread configuration, and build profiling

Slim CI with state comparison, deferred runs against production artifacts, PR-based ephemeral environments, state:modified selectors, and multi-environment promotion pipelines

Warehouse cost attribution per model, query cost optimization strategies, build efficiency metrics, cost-aware materialization choices, and budget alerting