Start with the right foundation.
- SQL analysts moving into analytics engineering
- Data warehouse developers
- BI engineers
- Snowflake practitioners who need production dbt workflows
SNOWFLAKE + DBT PRACTICAL PATH
Snowflake Data Engineering with dbt
Build maintainable analytics pipelines with Snowflake loading, warehouses, streams, tasks, dynamic tables and dbt transformation workflows.
Learn how to create an analytics platform that remains understandable as data and teams grow. The program connects Snowflake architecture to dbt modeling, testing, documentation, deployment, security and cost controls.
Recommended prerequisites.
- Intermediate SQL
- Basic dimensional-modeling concepts
- Comfort using Git from a guided workflow
ROLE-BASED OUTCOMES
Learn to make and defend real implementation decisions.
The goal is not to memorize product menus. Each outcome connects architecture, implementation and operational evidence.
Select appropriate Snowflake loading and transformation patterns
Build modular dbt models with tests, sources and documentation
Use incremental processing without silently losing or duplicating data
Operate secure, cost-aware pipelines with deployment and recovery evidence
Tools and platformsSnowflakeSnowpipeStreams and TasksDynamic TablesdbtGitSnowsightQuery Profile
DETAILED SYLLABUS
One connected path from concepts to production evidence.
Every module includes a practical deliverable so you can explain what you built, why you chose it and how you verified it.
Snowflake architecture and workload design
Understand storage, compute and cloud-services separation before choosing warehouse and data-flow patterns.
- Virtual warehouses
- Micro-partitions
- Caching
- Workload isolation
Hands-on lab
Design warehouse boundaries and auto-suspend rules for ingestion, transformation and BI workloads.
Loading and change capture
Compare bulk COPY, Snowpipe, streams and triggered processing using latency and operations requirements.
- Stages and file formats
- COPY validation
- Snowpipe
- Streams and triggered tasks
Hands-on lab
Build a restart-safe file load with rejected-row handling and an incremental change pipeline.
dbt project structure
Turn SQL transformations into named, versioned and reviewable models.
- Sources and staging
- ref and lineage
- Materializations
- Macros and packages
Hands-on lab
Create staging, intermediate and mart layers with consistent naming and model contracts.
Quality and documentation
Make assumptions executable so failures surface before dashboards become misleading.
- Generic and singular tests
- Freshness
- Documentation
- Data contracts
Hands-on lab
Add source freshness, relationship tests and a business-facing documentation site for a sales mart.
Incremental and scheduled processing
Choose between dbt incremental models, tasks and dynamic tables without duplicating orchestration.
- Unique keys
- Late-arriving data
- Dynamic table targets
- Backfills
Hands-on lab
Implement an incremental fact model, simulate late data and prove the backfill procedure.
Security, performance and deployment
Use role design, query evidence and controlled releases to operate the platform responsibly.
- RBAC and future grants
- Query Profile
- Clustering decisions
- CI checks and environments
Hands-on lab
Deploy through a pull-request workflow with tests, least-privilege roles and a cost investigation checklist.
CAPSTONE WORK
Projects designed for explanation, review and improvement.
A useful portfolio shows decisions and evidence, not screenshots alone.
Commerce analytics warehouse
Load orders, model dimensions and facts, and publish tested revenue metrics.
Evidence to retain- dbt DAG
- Test results
- Metric definitions
- Deployment record
Change-data pipeline
Capture account changes and maintain a historical customer model.
Evidence to retain- CDC design
- Incremental logic
- Late-data test
- Backfill runbook
Cost and performance review
Investigate an expensive workload and propose evidence-based warehouse and SQL changes.
Evidence to retain- Query Profile findings
- Warehouse policy
- Before-and-after metrics
- Risk notes
CONTINUE WITH PRIMARY SOURCES
Use current official documentation alongside guided practice.
Technology changes. These primary references help you verify current features and continue learning after the course. ITCertPath is an independent training brand and is not endorsed by the listed vendors.
COURSE QUESTIONS
What to confirm before you enrol.
Ask for a written syllabus, delivery plan, trainer profile, lab arrangement, fee breakdown and cancellation terms.
Is dbt part of Snowflake?
dbt is a separate transformation framework that can execute modeled SQL in Snowflake. This course teaches how the tools work together while keeping orchestration, testing and ownership boundaries clear.
Does this replace SnowPro certification preparation?
No. It develops practical Snowflake and dbt skills. Certification candidates should separately compare the current official SnowPro objectives with their chosen exam and use targeted preparation where gaps remain.
Will the course cover Snowflake cost control?
Yes. The outline includes warehouse isolation, auto-suspend, query investigation and responsible materialization choices. Actual cost depends on your account, workload and cloud region.
PLAN YOUR NEXT SKILL
Discuss Snowflake + dbt training with an advisor.
Tell us your experience, target role, time zone and preferred learning format. We will share the current options without promising a batch that has not been confirmed.