Snowflake offers a cloud-based data platform that combines data warehousing, data lakes, and data sharing capabilities. Built on a scalable architecture, it enables efficient storage, processing, and analysis of large datasets. Snowflake operates across multiple cloud providers, ensuring high availability and security. Its architecture supports flexible scaling and integrates with various data tools and services.

• 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