• Basic knowledge of SQL or data querying languages will be beneficial. 
• A fundamental understanding of database concepts and cloud computing is also helpful. Basic understanding of computers and networking.

Module 1: Introduction to Snowflake

• Overview of Snowflake

  • What is Snowflake and how does it differ from traditional databases?
  • Snowflake architecture: Database, Virtual Warehouses, and Cloud Services.
  • Benefits of Snowflake: Scalability, elasticity, and zero-maintenance.
  • Snowflake vs traditional data warehouses (e.g., Redshift, BigQuery).

• Setting Up Snowflake

  • Creating a Snowflake account and setting up a free trial.
  • Understanding Snowflake’s regions and cloud platforms (AWS, Azure, Google Cloud).
  • Introduction to Snowflake’s Web Interface (UI), Worksheets, and Databases.

• Basic Snowflake Terminology

  • Snowflake objects: Databases, Schemas, Tables, Views, Stages, and Streams.
  • Snowflake roles and privileges: Managing user access.
  • Understanding Snowflake’s multi-cluster architecture for performance.

Module 2: Snowflake Data Structures

• Databases, Schemas, and Tables

  • Creating and managing databases and schemas.
  • Types of tables: Permanent, Transient, and Temporary.
  • Understanding clustering keys for better performance.

• Working with Stages

  • Snowflake Stages: Internal vs External stages.
  • Loading data from stages into Snowflake tables.
  • Managing data storage in Snowflake.

• File Formats

  • Supported file formats: CSV, JSON, Parquet, Avro, and ORC.
  • Using file formats with Snowflake stages.
  • Defining and working with different file formats.

• Streams and Tasks

  • Introduction to Snowflake streams for change data capture (CDC).
  • Using streams to track changes in data.
  • Automating workflows with Snowflake tasks.

Module 3: Loading and Querying Data

• Loading Data into Snowflake

  • Using COPY command to load data from stages into Snowflake tables.
  • Loading data from flat files (CSV, JSON) and cloud storage (S3, Azure Blob Storage).
  • Handling semi-structured data in Snowflake (JSON, Avro, Parquet).
  • Managing loading errors with Snowflake error handling features.

• Querying Data in Snowflake

  • Basic SQL queries in Snowflake: SELECT, INSERT, UPDATE, DELETE.
  • Complex SQL queries: Joins, Subqueries, Window functions.
  • Using Snowflake’s support for standard SQL for data analysis.
  • Handling time-zone issues with TIMESTAMP data type.

• Working with Semi-Structured Data

  • Loading and querying JSON, XML, and Avro data in Snowflake.
  • Using VARIANT data type for semi-structured data.
  • Flattening and parsing semi-structured data with the FLATTEN() function.

• Data Transformation and Management

  • Using Snowflake’s SQL-based transformations.
  • Working with CTEs (Common Table Expressions) and temporary tables.
  • Writing and executing stored procedures.

Module 4: Performance Optimization in Snowflake

• Query Optimization

  • Snowflake’s automatic query optimization.
  • Using clustering keys and partitioning for performance.
  • Analyzing query performance using query history.
  • Best practices for efficient querying.

• Scaling and Concurrency

  • Understanding Snowflake’s automatic scaling.
  • Configuring virtual warehouses and managing workload concurrency.
  • Using multi-cluster warehouses for handling high concurrency.
  • Managing warehouse sizes for optimal performance.

• Caching and Result Set Caching

  • Using result caching to improve query performance.
  • Understanding Snowflake’s automatic caching mechanism.
  • Managing data storage and cache sizes.

• Materialized Views

  • Introduction to materialized views in Snowflake.
  • Creating and managing materialized views for faster query performance.
  • Best practices for using materialized views.

Module 5: Data Sharing and Security

• Data Sharing in Snowflake

  • Understanding Snowflake’s secure data sharing feature.
  • Sharing data across Snowflake accounts: Shares and Consumer Accounts.
  • Real-time data sharing and use cases.

• Managing Security in Snowflake

  • Understanding Snowflake’s role-based access control (RBAC) model.
  • Creating and managing roles, privileges, and grants.
  • Securing data with access control policies.
  • Snowflake’s support for multi-factor authentication (MFA).
  • Setting up network policies to restrict access to specific IP addresses.

• Data Encryption and Compliance

  • Snowflake’s data encryption mechanisms (at-rest, in-transit).
  • Understanding Snowflake’s compliance certifications (GDPR, HIPAA, SOC 2).
  • Managing encryption keys and roles for secure data access.

Module 6: Snowflake Ecosystem Integration

• Integrating Snowflake with Cloud Services

  • Integrating Snowflake with cloud platforms like AWS, Azure, and GCP.
  • Using Snowflake with cloud storage: Amazon S3, Azure Blob Storage, Google Cloud Storage.
  • Configuring cloud storage integration and permissions.

• Connecting Snowflake with BI Tools

  • Connecting Snowflake with popular BI tools (Tableau, Power BI, Looker).
  • Exporting data from Snowflake to BI tools for reporting and visualization.
  • Using ODBC/JDBC drivers to connect Snowflake to external applications.

• Snowflake with Machine Learning and Data Science

  • Using Snowflake for data science workflows: Python, R, and Snowflake integration.
  • Integrating Snowflake with ML libraries (e.g., Scikit-learn, TensorFlow).
  • Running machine learning models inside Snowflake using Snowpark.

• Third-Party Tool Integrations

  • Connecting Snowflake with ETL tools (Talend, Fivetran, Informatica).
  • Integrating Snowflake with monitoring tools for performance tracking.

Module 7: Advanced Snowflake Features

• Snowflake’s Data Lake and Data Warehouse Architecture

  • Differences between a Data Lake and Data Warehouse.
  • Building a hybrid data architecture with Snowflake.
  • Integrating Snowflake with Data Lakes for complex analytics.

• Snowpark for Data Engineering and Data Science

  • Introduction to Snowpark: What it is and how it works.
  • Using Snowpark for Python, Java, and Scala-based data transformations.
  • Writing custom code for processing data inside Snowflake.

• Snowflake Streams and Tasks for Real-Time Analytics

  • Using Snowflake Streams to capture real-time data changes.
  • Automating workflows with Snowflake Tasks.
  • Real-time data processing and analytics with Snowflake.

• Zero-Copy Cloning and Time Travel

  • Understanding Snowflake’s Zero-Copy Cloning feature.
  • Using Time Travel for querying historical data.
  • Data recovery and auditing using Snowflake’s Time Travel.

Module 8: Snowflake Best Practices and Troubleshooting

• Best Practices for Snowflake Data Modeling

  • Optimizing schema design for performance.
  • Best practices for naming conventions, data types, and indexing.
  • Managing large datasets and partitions.

• Data Loading and Transformation Best Practices

  • Efficient ways to load and transform data into Snowflake.
  • Handling large data volumes and ensuring scalability.

• Troubleshooting and Performance Tuning

  • Using Snowflake’s Query History and Profile to debug and optimize queries.
  • Best practices for handling performance bottlenecks.
  • Managing storage costs and data retention policies.

40 Days (also available fast track course with short term duration)

• Flexible Schedules
• Live Online Training

• Training by highly experienced and certified professionals
• No slideshow (PPT) training, fully Hand-on training
• Interactive session with interview QA’s
• Real-time projects scenarios & Certification Help
• 24 X 7 Support