Course Syllabus

  Module 1: Introduction & Snowflake Fundamentals (4 hours) – Overview of Snowflake’s cloud data platform. Topics include the basics of cloud data warehousing, Snowflake editions, and key benefits. Learners set up a Snowflake trial account and explore the Web UI and SnowSQL command-line tool. The module covers Snowflake’s architecture (separation of storage and compute, micro-partitioning, multi-cluster virtual warehouses) and role-based access controls (users, roles, privileges). Hands-on lab: configure a Snowflake account, create a database and virtual warehouse, and execute simple SQL queries. This foundation ensures everyone understands that “Snowflake is a cloud-based data warehousing platform”

  Module 2: Database and Schema Design (4 hours) – Deep dive into organizing data in Snowflake. Topics include creating databases, schemas, tables and views, and choosing data types for structured and semi-structured data. Students learn best practices for schema design in a data warehouse. The module also introduces Snowflake’s micro-partitioning and clustering concepts: how to define clustering keys to optimize performance and query pruning. Learners practice loading data into tables and using the INFORMATION_SCHEMA to examine metadata. Hands-on lab: design a star or snowflake schema in Snowflake, load dimension/fact tables, and test clustering performance. (Cites: course materials note database/schema design, partitioning, and clustering as key topics.)

  Module 3: Snowflake SQL & Query Optimization (4 hours) – Mastering SQL in Snowflake. Coverage includes writing SELECT, JOIN and aggregation queries, window functions, and subqueries. Advanced SQL features are introduced: implementing stored procedures and user-defined functions (UDFs) with Snowflake SQL or Snowpark. The instructor also teaches query optimization techniques: how Snowflake caches results and metadata, and how to read the query profile. Students practice tuning queries by adding filters or clustering keys to improve performance. Hands-on lab: write and optimize complex SQL queries on a sample dataset (including use of window functions and stored procedures). This module ensures learners can efficiently query Snowflake and leverage its in-built optimization.

  Module 4: Data Loading and ETL (4 hours) – Loading data into Snowflake and ETL/ELT patterns. Topics include using COPY INTO for bulk loads from staged files, setting up internal and external stages (e.g. AWS S3 or Azure Blob storage), and unloading data back out. The instructor covers continuous ingestion using Snowpipe. Structured and semi-structured data handling is demonstrated (loading JSON, Parquet, Avro). The module also discusses integration with ETL/ELT tools – for example, how Snowflake can work with tools like Talend or dbt for data transformations. Hands-on lab: load a large dataset from cloud storage into Snowflake, handle JSON fields, and use Snowpipe to ingest new data automatically. (For instance, training docs highlight using COPY, BULK INSERT and Snowpipe for loading and note support for formats like JSON/Parquet.)

  Module 5: Data Pipelines with dbt and Airflow (4 hours) – Building automated data pipelines. This module introduces dbt (Data Build Tool) and Apache Airflow as they relate to Snowflake. First, students learn dbt fundamentals: writing modular SQL “models”, creating data transformations and tests, and generating documentation. (As one source notes, “DBT and Snowflake can be integrated seamlessly, with DBT providing ... data transformation and modeling capabilities”.) Next, Apache Airflow is introduced for workflow orchestration. Learners see how to deploy Airflow on a cloud VM (e.g. AWS EC2), connect it to Snowflake, and define DAGs to schedule tasks. A step-by-step lab guides students through setting up an Airflow DAG that runs dbt transformations on a Snowflake table on a schedule. (For example, project guidelines emphasize implementing a scheduled pipeline using Airflow DAGs to orchestrate loading and modeling tasks. By the end, participants can build an end-to-end ETL/ELT pipeline: data is ingested into Snowflake, transformed by dbt, and the workflow is automated with Airflow

  Module 6: Performance Tuning & Advanced Features (4 hours) – Optimizing Snowflake and leveraging advanced capabilities. Topics include performance tuning (using result caching, pruning with clustering keys, and scaling warehouses for concurrency) and cost management (auto-suspend/wake, resource monitors). The second half covers advanced Snowflake features: Streams & Tasks for change-data capture and scheduling, Time Travel and Fail-safe for data recovery, zero-copy cloning of tables, and introduction to Snowpark (if time permits). Hands-on lab: create a Snowflake stream on a table, use a Task to load incremental data, and query historical data with Time Travel. Also practice setting up resource monitors to control credit usage. (This module mirrors advanced topics in the curriculum: e.g. it covers Streams, Tasks, Stored Procedures and versioning features.)

  Module 7: Security and Governance (4 hours) – Ensuring data security and compliance in Snowflake. Key topics are role-based access control (RBAC), user/role management, and multi-factor authentication. Students learn about data protection features: automatic end-to-end encryption, dynamic data masking, and external tokenization. The course covers Snowflake’s data governance tools: using Data Access Governance, tagging sensitive columns, and audit logging. It also addresses compliance standards (e.g. HIPAA, SOC 2) that Snowflake supports. Hands-on lab: implement RBAC by creating roles and granting privileges on database objects, then set up a secure share (or reader account) to demonstrate governed data sharing. (As sources note, security training includes RBAC and data masking, audit logging and encryption.)

  Module 8: Cloud Integration (AWS & Azure) (4 hours) – Snowflake on public clouds. This module explores how Snowflake operates on AWS and Azure infrastructures. Learners compare features (e.g. AWS IAM vs Azure AD integration), and set up cloud storage integration: configure Snowflake stages to access AWS S3 buckets and Azure Blob containers. The training highlights multi-cloud data sharing and replication. Students also see how to use cloud-managed services with Snowflake (for example, using AWS SNS for alerts or Azure Data Factory for orchestration). Hands-on lab: load data into Snowflake via both an S3 stage and an Azure stage in the same account, and query it. The curriculum emphasizes building “real-time data pipelines using Snowflake and AWS/Azure”, so this module includes connecting Snowflake to each cloud’s ecosystem.

  Module 9: Real-World Projects (4 hours) – Capstone project work. In this module, students apply what they’ve learned to one or more end-to-end projects. Examples include migrating a legacy data warehouse to Snowflake, or building a streaming analytics pipeline. For instance, a project might involve ingesting event data into Snowflake (with Snowpipe), transforming it via dbt models, and visualizing the results in AWS QuickSight or Power BI. Another project could be a data “lakehouse” pattern: combining Snowflake tables with external tables on cloud storage. These projects integrate multiple components (storage, ELT tools, warehouses) to simulate business scenarios. Throughout, instructors provide guidance and check progress. (The training’s syllabus explicitly includes “real-time pipeline architecture” and “data warehouse migration” projects, so this module ensures hands-on practice.)

  Module 10: Certification Preparation (4 hours) – Snowflake exam readiness. The final module reviews Snowflake certification topics and test strategies. Instructors go over sample SnowPro Core exam questions, clarify key concepts, and address any knowledge gaps. Students learn how certification validates their skills and how to register for the exam. Practice quizzes and discussion of exam scenarios help solidify understanding. This preparation builds confidence: as one training outline notes, learners are guided through “potential exam questions and scenarios” so they are fully equipped to pass. By the end of this session, participants have both the technical skills and the exam know-how to earn their Snowflake certification