Snowflake Data Warehouse
Go beyond writing SELECT statements. Build, govern and cost-optimise the cloud data platform your whole organisation runs on. Most Snowflake training stops at the SQL warehouse — this course treats it as a data platform: pipelines, Python, open table formats, governance, credit cost control and AI on governed data. Built for fresh IT graduates and working professionals alike.
The Snowflake Data Warehouse program is a live, lab-heavy course that treats Snowflake as what enterprises actually use it as: a data platform, not just a SQL warehouse. Most Snowflake training stops at queries. Here you build the whole platform layer — ingestion pipelines, declarative transformations, Snowpark Python jobs, open table formats, governance controls, and AI functions running directly against governed data — because that is what the job now is.
Running through every module is one theme almost no course teaches properly: cost is a design decision. File sizing, warehouse sizing, clustering, auto-suspend, workload isolation — every choice shows up on the credit bill, and engineers who can control consumption as well as write SQL are the ones who get noticed internally. You will profile a wasteful workload, fix it, and report the credits saved with evidence.
It is designed for two audiences at once. If you are a fresh IT graduate, working SQL is the only hard prerequisite, and you graduate with a portfolio of production-style pipelines. If you are a working professional — an analyst, BI developer, legacy warehouse or ETL specialist, or an engineer joining a data platform team — you have the shortest on-ramp in our catalogue. The program runs 9 weeks (~70 hours), live online, closing with a governed, cost-monitored platform capstone you design, build and defend.
Where This Fits in Our Catalogue
- Snowflake Data Warehouse (this course) — the governed cloud data platform most enterprises standardise on.
- Apache Spark & PySpark — the processing engine; the natural pairing. Spark computes, Snowflake governs and serves.
- Generative AI & LLM Engineering and AI Agent Development — where enterprise AI increasingly runs against the governed data you build here.
Course at a Glance
- Duration: 9 weeks | ~70 hours | live + hands-on
- Format: Live online · lab-heavy · portfolio of production-style pipelines
- Prerequisite: Working SQL. Python helpful for the Snowpark modules but taught from first principles. No prior cloud or warehousing experience required.
- Curriculum: 9 modules, from platform architecture to AI on governed data
- Certification: Aligned to the SnowPro Core certification path
- Curriculum reviewed: July 2026 — Snowflake ships fast; this syllabus tracks the current platform
Course Highlights
- Every module leads with what you build and ship, not what you "learn about"
- Platform, not just warehouse: pipelines, Snowpark Python, Iceberg, governance, AI
- Cost is a design decision: credit consumption engineered, measured and reported — a dedicated cost-rescue project
- Modern pipelines: Snowpipe, Streams & Tasks, and Dynamic Tables compared head-to-head
- Governance done properly: RBAC, masking, row access policies, lineage, secure sharing
- Cortex AI functions over governed structured and unstructured data
- dbt, Kafka, Airflow and Terraform where real teams actually use them
Hands-on Projects & Portfolio
Staged files → Snowpipe → Streams and Tasks → curated medallion layers.
The same pipeline rebuilt declaratively, compared on complexity and cost.
Python transformation job plus a Streamlit app served from inside Snowflake.
Role hierarchy, masking, row access policies and an audit trail for a simulated regulated dataset.
Profile a wasteful workload, fix it, and report credits saved with before/after evidence.
Share governed data to an external consumer with no data movement.
AI over unstructured text sitting alongside governed structured data.
Tools & Stack
Snowflake SnowSQL Snowsight Snowpark (Python) Snowpipe Streams & Tasks Dynamic Tables Apache Iceberg Cortex AI Streamlit dbt Apache Kafka Apache Airflow AWS S3 Azure Blob GCS Terraform Git
Certification Alignment
- Syllabus mapped to the SnowPro Core certification path
- Builds toward SnowPro Advanced: Data Engineer and the Snowpark specialty track
- A lighter platform-level associate credential exists as an entry point
- Presented as preparation and alignment — no official partnership or accreditation implied, and no exam outcome guaranteed
Career Outcomes
Snowflake is one of the platforms enterprises standardise on rather than experiment with, which makes the skill unusually durable — and engineers who can control credit consumption as well as write SQL are the ones who get noticed. SQL-strong analysts and legacy warehouse developers have the shortest on-ramp. This course targets:
- Data Engineer
- Snowflake Developer
- Analytics Engineer
- Data Warehouse Engineer
- Data Platform Engineer
- BI Developer moving to the cloud
- Data Architect
- FinOps / platform cost analyst for data
Who This Course Is For
- Final-year students and fresh IT graduates targeting data engineering or analytics roles
- SQL-strong data analysts and BI developers moving into engineering
- Oracle, Teradata, SQL Server and legacy warehouse professionals migrating to the cloud
- ETL developers modernising onto ELT and the lakehouse
- Python and backend engineers joining data platform teams
- Data leads accountable for governance, compliance or a Snowflake bill
Material Includes
- Class recordings
- Lab guides & pipeline starter repos
- Cost-review checklists & governance templates
- Certificate
Prerequisites
- Working SQL
- Python helpful for Snowpark, taught from first principles
- No prior cloud or warehousing experience required
Estimated Time: 7 Hours
You ship:- A working account layout — warehouses sized and suspended deliberately — plus an annotated architecture brief explaining why concurrency and cost behave differently here than in a traditional warehouse
- Separation of storage, compute and cloud services; virtual warehouses and multi-cluster scaling
- Micro-partitions and clustering; result and metadata caching
- Editions and cloud/region choices
Estimated Time: 8 Hours
You ship:- A dimensional model with medallion layering inside Snowflake — including JSON flattened straight from VARIANT with no pre-parsing, and a zero-copy clone used as a safe dev environment
- Snowflake SQL beyond the basics; window functions; the VARIANT type; querying and flattening JSON/Avro/Parquet
- Zero-copy cloning; Time Travel and Fail-safe
- Dimensional modelling and medallion layering inside Snowflake
Estimated Time: 8 Hours
You ship:- A continuous ingestion pipeline — staged files through Snowpipe auto-ingest — with idempotent reloads, error handling, and file sizing chosen deliberately because it quietly determines your bill
- Stages (internal, external, named); COPY INTO; bulk vs continuous loading
- Snowpipe and auto-ingest; file sizing and format choices; streaming ingestion patterns and Kafka connectivity
- Error handling and idempotent reloads
Estimated Time: 8 Hours
You ship:- The same incremental pipeline built twice — Streams + Tasks, then Dynamic Tables — compared honestly on complexity and cost, plus a dbt transformation layer
- Streams for change data capture; Tasks and task graphs
- Dynamic Tables for declarative incremental pipelines; stored procedures and UDFs; scheduling and dependency management
- dbt on Snowflake as the transformation layer
Estimated Time: 9 Hours
You ship:- A Snowpark Python transformation job that pushes logic to the data instead of exporting it, plus a Streamlit app served from inside Snowflake
- The Snowpark DataFrame API; Python UDFs and UDTFs; stored procedures in Python
- Snowpark-optimised warehouses; Streamlit apps inside Snowflake
- When Snowpark is the right choice versus pulling data out to Spark
Estimated Time: 7 Hours
You ship:- An Iceberg table layer readable from outside Snowflake, with a written trade-off decision: open lakehouse vs native tables for a given workload
- Apache Iceberg tables in Snowflake; managed vs externally managed; catalog interoperability
- External tables over object storage
- Architectural trade-offs of an open lakehouse versus native Snowflake tables
Estimated Time: 8 Hours
You ship:- A full governance build-out for a simulated regulated dataset — role hierarchy, masking, row access policies, lineage and audit trail — plus a secure share to an external consumer with no data movement
- RBAC done properly; role hierarchies and least privilege; dynamic data masking; row access policies
- Tagging and classification; object lineage; access history and auditing; secure views; network policies
- Secure data sharing, the Marketplace and clean rooms
Estimated Time: 8 Hours
You ship:- A cost optimisation case study: profile a wasteful workload, fix it, and report credits saved with before/after evidence — the artifact that gets Snowflake engineers noticed internally
- Reading the Query Profile; spillage; pruning and clustering decisions; search optimisation and materialised views
- Warehouse sizing; auto-suspend and auto-resume; workload isolation; resource monitors and budgets; credit attribution by team
- Newer workload-aware automatic compute options — when to hand sizing to the platform and when to control it yourself
Estimated Time: 7 Hours
You ship:- One end-to-end governed, cost-monitored Snowflake platform — designed, built and defended — including Cortex-powered analysis over unstructured text alongside governed structured data
- Cortex AI functions for summarisation, classification and sentiment directly in SQL
- Semantic views and natural-language querying over structured data; search over unstructured documents
- Governed data as the foundation for enterprise AI agents; capstone design, build and defense
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