Snowflake Course

About the Course

The Snowflake Training course at Besant Technologies introduces the tools and techniques used to manage data in a cloud environment. You will learn to work with databases, schemas, tables, views, and virtual warehouses, along with writing SQL queries and loading data from different sources. The training covers ETL and ELT workflows, data transformation, data modelling, incremental data loading, and data quality checks. You will also explore structured and semi-structured data, Snowflake security and administration, and the use of tools such as Informatica, Azure Data Factory, Python, and dbt in data engineering projects.

The syllabus moves from basic database operations to the features used in day-to-day Snowflake development. You will work with databases, schemas, tables, views, virtual warehouses, and SQL queries before exploring bulk data loading and automated ingestion with Snowpipe. The advanced sessions cover Streams and Tasks, Dynamic Tables, Snowpark, Time Travel, Zero-Copy Cloning, and query performance analysis.

You will also learn to handle semi-structured data using JSON and VARIANT, understand data sharing and access permissions, and explore how Change Data Capture (CDC) supports incremental data processing. ETL and ELT concepts are connected to tools such as Informatica, Azure Data Factory, dbt, and Apache Airflow, depending on the course module.

What You Will Learn in the Snowflake Course

• Loading Data: Import CSV, JSON, and other supported data files into Snowflake tables.
• Writing SQL: Create queries, joins, views, and transformations for reporting requirements.
• Building Data Workflows: Understand how data moves from source systems through ETL or ELT pipelines.
• Automating Updates: Explore Snowpipe, Streams and Tasks, and Dynamic Tables for data ingestion and processing.
• Working with Python: Use Snowpark concepts to process data with Python in Snowflake.
• Checking Performance: Examine query execution and learn how warehouse configuration affects performance.

Snowflake Syllabus

The Snowflake training syllabus is designed for working professionals, SQL developers, ETL developers, data analysts and aspiring data engineers. The recommended duration is 90–120 hours, depending on the selected modules, practical exercises and project work.

Module 1: Cloud Data Warehousing Fundamentals

• Introduction to data warehousing, data lakes and lakehouse concepts
• OLTP vs. OLAP and analytical workloads
• Cloud data platforms and Snowflake use cases
• Data warehouse architecture
• ETL and ELT fundamentals
• Overview of AWS, Microsoft Azure and Google Cloud integration

Module 2: Snowflake Architecture and Fundamentals

• Snowflake architecture: storage, compute and cloud services
• Virtual warehouses and multi-cluster warehouses
• Databases, schemas, tables, views and stages
• Micro-partitions and automatic data pruning
• Snowflake account structure and worksheets
• Compute scaling and separation of storage and compute

Module 3: Snowflake SQL and Advanced SQL

• DDL, DML and data manipulation commands
• Joins, subqueries, CTEs and set operators
• Window functions and ranking
• Aggregations, conditional logic and date functions
• INSERT, UPDATE, DELETE and MERGE statements
• NULL handling and metadata queries
• SQL scripting, stored procedures and error handling
• Advanced SQL practice using business datasets

Module 4: Snowflake Objects and Warehouse Management

• Permanent, transient and temporary tables
• Standard views and materialized views
• Virtual warehouse creation and configuration
• Warehouse sizing, auto-suspend and auto-resume
• Resource monitors and warehouse usage
• Sequences, constraints and object dependencies

Module 5: Snowflake Data Loading and Unloading

• Internal and external stages
• CSV, JSON, Parquet and other supported file formats
• COPY INTO commands for bulk loading and unloading
• Data validation and load error handling
• Load history and failed-load troubleshooting
• Loading data from cloud object storage
• Incremental loading and duplicate handling
• Hands-on data loading exercises

Module 6: ETL and ELT Development

• ETL vs. ELT architecture
• Source-to-target mapping and transformation logic
• Full loads, incremental loads and change processing
• Data cleansing, validation and deduplication
• Slowly Changing Dimensions (SCD) Type 1 and Type 2
• Informatica PowerCenter and IICS concepts
• Azure Data Factory pipelines and Snowflake integration
• ETL troubleshooting and data reconciliation

Module 7: Data Transformation and Data Modelling

• Fact and dimension tables
• Star schema and snowflake schema modelling
• Data normalization and denormalization
• Staging, intermediate and presentation layers
• SQL-based transformations and reusable views
• Surrogate keys and slowly changing dimensions
• Data quality checks and reconciliation
• Building reporting-ready datasets

Module 8: Semi-Structured Data Processing

• VARIANT, OBJECT and ARRAY data types
• Processing JSON, Avro and Parquet data
• FLATTEN for nested and repeated structures
• Parsing and extracting nested fields
• Schema evolution and changing source data
• Transforming semi-structured data into relational tables

Module 9: Snowpipe and Automated Data Ingestion

• Snowpipe architecture and continuous data ingestion
• Auto-ingest and cloud event notifications
• Stage configuration and file-arrival workflows
• Error handling and load monitoring
• Snowpipe Streaming concepts
• Choosing between bulk loading and continuous ingestion

Module 10: Snowflake Streams, Tasks and Dynamic Tables

• Change Data Capture (CDC) concepts
• Streams and their use cases
• Task scheduling and task graphs
• Incremental processing and dependency management
• Dynamic Tables and target lag
• Automating data transformation pipelines
• Monitoring and recovering failed pipeline runs

Module 11: Python and Snowpark

• Python fundamentals for data engineering
• Snowpark DataFrames and transformations
• Filtering, joining and aggregating datasets
• User-defined functions and stored procedures
• Python worksheets and package dependencies
• Snowpark-based data processing
• Testing and debugging Python workflows

Module 12: ETL Tools and Data Platform Integration

• Connecting Snowflake with Informatica PowerCenter and IICS
• Integrating Azure Data Factory with Snowflake
• Python connectors and application connectivity
• REST API and external data ingestion concepts
• dbt models, tests and transformations
• Apache Airflow DAGs and workflow orchestration
• Integration design, credentials and failure handling

Module 13: Snowflake Administration and Security

• Role-Based Access Control (RBAC)
• Users, roles, grants and privileges
• Database and schema access management
• Secure views and data sharing
• Masking policies and row access policies
• Network policies and authentication concepts
• Time Travel and data recovery considerations

Module 14: Snowflake Performance Tuning and Cost Optimization

• Query Profile and Query History
• Micro-partition pruning and query execution analysis
• Warehouse sizing and scaling strategies
• Clustering considerations and search optimization
• Caching behaviour and query reuse
• Resource monitoring and compute cost analysis
• Troubleshooting slow queries and inefficient workloads

Module 15: Snowflake Real-Time Project Implementation

• Analysing business requirements and source data
• Designing a Snowflake data warehouse
• Building staging and transformation pipelines
• Loading and validating source data
• Implementing incremental processing and data quality checks
• Tuning SQL queries and reviewing resource usage
• Documenting and presenting project results

Module 16: Snowflake Interview Preparation and Career Support

• Snowflake architecture interview questions
• Advanced SQL coding exercises
• ETL/ELT and data engineering interview questions
• Debugging and performance-tuning scenarios
• Resume preparation and project explanation
• Technical mock interviews

Upcoming Batch Schedule for Snowflake Online Training

Besant Technologies provides flexible timings to all our students. Here is the Snowflake Online Training Class Schedule in our branches. If this schedule doesn’t match please let us know. We will try to arrange appropriate timings based on your flexible timings.

Trainer Profile of Snowflake Online Training

Pradeep Narayanan specialises in Snowflake, ETL development, data engineering, cloud computing, and data analytics. His technical skill set includes Snowflake architecture, advanced SQL, Python, data warehousing, ETL and ELT pipelines, Informatica, Azure Data Factory, AWS, Microsoft Azure, data modelling, data integration, and performance optimisation.

His training covers practical aspects of data engineering, from loading raw data into Snowflake to transforming datasets and preparing them for analytics. Students learn Snowflake features such as Snowpipe, Streams and Tasks, Time Travel, Zero-Copy Cloning, Snowpark, and query optimisation through hands-on exercises and project-based practice.

The training is suitable for freshers, working professionals, career-break candidates, and non-IT candidates looking to build skills in data engineering. Sessions also cover data pipeline development, troubleshooting, technical interview preparation, and common industry use cases.

Snowflake Online training can cater to learners from the USA, UK, UAE, Australia, and Canada, depending on batch availability.

Relevant Certification Areas

• Snowflake SnowPro certifications
• Snowflake data engineering and platform fundamentals
• ETL and data integration tools
• AWS, Microsoft Azure, and Google Cloud data services
• SQL and cloud data warehousing

Snowflake Certification

Students who complete the training receive a course completion certificate from Besant Technologies, subject to the institute’s completion requirements. Those who want an industry-recognised Snowflake credential can also prepare for the official SnowPro certification exams. The SnowPro Core certification covers foundational Snowflake knowledge, while the SnowPro Advanced: Data Engineer certification is intended for more advanced data engineering skills. Exam preparation should include the relevant official exam objectives, hands-on practice, and revision of Snowflake concepts. The institute’s course certificate is separate from an official Snowflake certification.

Key Features of Snowflake Course

• Step-by-step learning: Start with database basics and move towards advanced Snowflake features.
• SQL practice: Work with queries and transformations using sample datasets.
• ETL workflow knowledge: Understand how data moves between source systems and cloud storage.
• Advanced Snowflake features: Explore Snowpipe, Streams and Tasks, Dynamic Tables, and Snowpark.
• Project exercises: Practise common data loading and transformation scenarios.
• Flexible learning modes: Check available online Snowflake training and classroom batches.
• Interview preparation: Revise SQL, data warehousing, and Snowflake technical concepts.
• Resume guidance: Learn to describe your skills and project experience clearly.
• Certification preparation: Review relevant Snowflake certification topics.
• Career guidance: Understand the skills commonly required for Snowflake and data engineering positions.

Hands-On Training and Real-Time Snowflake Projects

Project 1: End-to-End Snowflake Data Warehouse

Build a data warehouse using source files, staging tables, SQL transformations, fact and dimension tables, and reporting-ready datasets. Practise SQL, COPY INTO, data modelling, ETL/ELT and query tuning.

Project 2: Automated Snowflake Data Pipeline

Build a pipeline that loads new files, validates data, processes incremental changes and refreshes target tables using Snowpipe, Streams, Tasks, or Dynamic Tables. Learn data ingestion, CDC, automation, monitoring, and error handling.

Project 3: ETL Integration with Snowflake

Create an integration workflow using Azure Data Factory or Informatica to load source data into Snowflake, transform the data, validate results and troubleshoot failed loads.

Training Courses Reviews

Frequently Asked Questions