dbt Implementation, Analytics Engineering & Data Transformation
Wolk Inc designs and implements dbt projects for Snowflake, BigQuery, and Redshift: three-layer modelling architecture, test coverage, auto-generated documentation, CI/CD pipelines, and dbt Cloud or Airflow scheduling. Engineering rigour applied to SQL transformation.
3-Layer
Staging · Intermediate · Mart
Slim CI
dbt Cloud PR Checks
100%
Model Documentation Target
Snowflake · BQ
Primary Warehouse Targets
dbt Consulting Deliverables
dbt Project Architecture & Data Modelling
Three-layer modelling architecture: staging models (source fidelity, light casting), intermediate models (business logic, joins), and mart models (aggregated, business-unit-specific outputs). Source YAML declarations for all upstream tables, consistent naming conventions, and model materialisation strategy (tables, views, incremental, snapshots) aligned to warehouse performance and cost goals.
Testing & Data Quality Framework
Built-in dbt tests (unique, not_null, accepted_values, relationships) on all key columns, custom generic tests for business rules, and dbt-expectations or dbt-utils for advanced assertions. Severity levels configured per test so warnings surface without blocking deployment. Test coverage report included in dbt documentation site.
Documentation & Data Lineage
Column-level descriptions in YAML for all mart models, model-level descriptions with business context, auto-generated dbt documentation site deployed to an internal URL or dbt Cloud. Data lineage graph (DAG) walkthrough with your analytics engineering team. Source freshness tests configured for critical upstream tables.
CI/CD & dbt Cloud / Airflow Integration
dbt Cloud job scheduling for production runs with failure alerting. Slim CI job in GitHub Actions or GitLab CI running only changed models and their downstream dependencies on pull requests. Airflow or Dagster operator integration for dbt runs within larger pipeline DAGs. dbt artifacts (manifest.json, run_results.json) parsed for model performance tracking.
dbt Stack Coverage
Three-Layer Architecture. Tested. Documented.
dbt Consulting Questions
What is dbt and why do analytics-focused teams use it?▾
dbt (data build tool) is a transformation framework that applies software engineering practices — version control, testing, documentation, modularity — to SQL-based data transformations in a warehouse. Teams use dbt because it makes transformations auditable (Git history), tested (built-in and custom tests), documented (auto-generated lineage and column descriptions), and collaborative (analysts and engineers work in the same codebase). It is now the standard tool for analytics engineering on Snowflake, BigQuery, and Redshift.
Should we use dbt Core or dbt Cloud?▾
dbt Core is the open-source CLI — free, self-managed, and flexible. dbt Cloud is the managed platform: hosted IDE, job scheduler, Slim CI, environment management, and the Explorer lineage UI. dbt Cloud is the right choice for teams that want managed scheduling and CI without operating Airflow or building their own GitHub Actions pipeline. dbt Core is preferred for teams already using Airflow or Dagster as their orchestrator, or for organisations with strict data sovereignty requirements. Wolk Inc implements either and can migrate from Core to Cloud (or the reverse) as part of the engagement.
What is the three-layer dbt modelling architecture and why does it matter?▾
The three-layer approach separates concerns cleanly: (1) Staging — one model per source table, light casting and renaming, no business logic, views; (2) Intermediate — business logic, joins between staging models, still derived from source grain; (3) Marts — aggregated, audience-specific (finance mart, product mart), materialised as tables or incremental models. This structure means business logic changes stay in intermediate models without touching staging, and mart rebuilds are cheaper because they join intermediate views rather than raw source tables.
How does Wolk Inc handle incremental dbt models in Snowflake or BigQuery?▾
Wolk Inc implements incremental models using the `unique_key` + `merge` strategy for Snowflake and BigQuery, with a configurable lookback window to catch late-arriving data. For event tables with high append rates (logs, transactions), insert-overwrite on a date partition is more cost-efficient than full-table merge. We configure `on_schema_change = sync_all_columns` so column additions in the source don't require a full refresh. Incremental strategy decisions are documented in model-level descriptions for future maintainability.
How long does a dbt implementation engagement take?▾
A greenfield dbt implementation for a warehouse with 5–15 source systems and 50–150 target models typically takes 6–10 weeks: 2 weeks for source discovery and project architecture, 4–6 weeks for model development and testing, 1 week for CI/CD and deployment pipeline setup. A migration from an existing SQL-in-stored-procedures or ETL tool (Informatica, Talend) to dbt takes longer depending on existing model complexity and business logic consolidation required. Wolk Inc provides a scope estimate after a discovery session.