Snowflake Training in Chennai in Perungudi

• Cloud Data Warehouse vs. Traditional On-Premise Systems
• Snowflake Architecture: Storage, Compute, and Cloud Services layers
• Snowflake Editions, Pricing, and Cost Optimization Strategies
• Setting up Snowflake Accounts, Roles, and User Management
• Introduction to Snowsight UI and Worksheet Navigation
• Database, Schema, and Table creation (Managed vs. External)

Beginner Deploy a fully configured Snowflake environment with custom RBAC roles and a cost-monitoring dashboard.

• Internal and External Stages (AWS S3, Azure Blob, GCS)
• File Formats: CSV, JSON, Parquet, and Avro configuration
• Bulk Data Loading using the COPY INTO command
• Continuous Data Ingestion with Snowpipe (Auto-ingest and REST API)
• Handling Data Loading Errors, Rejection, and Validation
• Data Unloading and Exporting to External Cloud Storage

Beginner to Intermediate Build an automated, serverless Snowpipe pipeline continuously loading real-time JSON logs from AWS S3.

• Advanced SQL: CTEs, Window Functions, and Pivoting
• Semi-structured Data Querying using FLATTEN and LATERAL joins
• Time Travel: Querying historical data and understanding retention periods
• Zero-Copy Cloning for efficient testing and development environments
• Data Recovery using UNDROP and Time Travel restoration
• Performance Tuning: Query Profile analysis and micro-partition clustering keys

Intermediate Develop an analytical query processing nested JSON, utilizing window functions, and implementing a zero-copy clone.

• Introduction to Snowpark: Architecture and Local Testing
• Snowpark Python DataFrames: Transformations and Actions
• Creating and Deploying User-Defined Functions (UDFs) in Python
• Building Stored Procedures for complex business logic
• Integrating third-party Python libraries (e.g., Pandas, Scikit-learn) via Anaconda
• Optimizing Snowpark workloads for performance and cost

Intermediate to Advanced Build a Snowpark Python app performing advanced feature engineering with Pandas and writing to Snowflake.

• Change Data Capture (CDC) using Snowflake Streams
• Automating workflows with Snowflake Tasks and Cron schedules
• Introduction to Dynamic Tables for declarative data transformations
• Configuring refresh modes and target lag in Dynamic Tables
• Building multi-step ELT pipelines combining Streams, Tasks, and Dynamic Tables
• Monitoring pipeline execution and handling task failures

Intermediate Design an automated ELT pipeline using Streams and Dynamic Tables for incremental data refresh.

• Introduction to Apache Iceberg and the Open Table Format
• Creating and managing Iceberg Tables in Snowflake
• Integrating Snowflake with external Iceberg catalogs (e.g., Polaris, AWS Glue)
• Schema evolution and ACID transactions in Iceberg
• Querying externally managed Iceberg tables via Dynamic Tables
• Governance and security for open lakehouse architectures

Advanced Construct an open lakehouse where Snowflake seamlessly queries externally managed Apache Iceberg tables.

• Introduction to Streamlit and its native integration within Snowflake
• Building interactive UI components (charts, sliders, filters)
• Connecting Streamlit apps to Snowflake data securely
• Deploying and sharing Streamlit apps with role-based access control
• Best practices for performance and resource management in Streamlit
• Embedding AI-powered insights into Streamlit dashboards

Intermediate Deploy an interactive Sales Analytics Dashboard using Streamlit in Snowflake for real-time data visualization.

• Overview of Snowflake Cortex and Managed Machine Learning
• Using Cortex AI functions (e.g., COMPLETE, TRANSLATE, SUMMARIZE)
• Performing sentiment analysis and entity extraction on text data
• Introduction to Vector Search and Cortex Search for RAG applications
• Integrating Cortex AI functions into Dynamic Tables for incremental refresh
• Security, privacy, and data governance when using AI models

Advanced Build an AI-powered customer feedback analyzer using Cortex AI for real-time sentiment analysis.

• Introduction to dbt (Core and Cloud) and the ELT paradigm
• Setting up dbt projects and connecting to Snowflake
• Building modular data models using Jinja templating and macros
• Implementing incremental models and snapshots for historical tracking
• Writing dbt tests (schema and data tests) for data quality assurance
• Generating and publishing dbt documentation and lineage graphs

Intermediate to Advanced Migrate a legacy SQL pipeline into a modular, tested, and documented dbt project.

• Fundamentals of Secure Data Sharing (Provider and Consumer roles)
• Creating and managing Data Shares and Reader Accounts
• Discovering and consuming datasets from the Snowflake Data Marketplace
• Introduction to Snowflake Data Clean Rooms for privacy-safe collaboration
• Monetizing data products and building external data applications
• Best practices for cross-cloud and cross-region data sharing

Intermediate Set up a secure data sharing environment where a Provider shares live, governed datasets.

• Role-Based Access Control (RBAC) and the principle of least privilege
• Dynamic Data Masking and Row-Level Security (RLS)
• Network policies, IP allowlisting, and VPC PrivateLink
• Introduction to Infrastructure as Code (IaC) for Snowflake using Terraform
• Setting up CI/CD pipelines for Snowflake using GitHub Actions
• Using SnowCD for network troubleshooting

Advanced Automate Snowflake environment deployment using Terraform integrated into a GitHub Actions CI/CD pipeline.

• Capstone Project Planning: Architecture design and requirement gathering
• End-to-end implementation: Ingestion, Snowpark transformation, dbt modeling, and Streamlit visualization
• Code review, performance optimization, and best practices enforcement
• Resume building: Highlighting Snowflake projects and keywords for ATS systems
• Mock technical interviews: SQL, Snowpark, and architecture scenarios
• Portfolio presentation: Publishing the project on GitHub and writing a technical blog post

Advanced Deliver a production-ready End-to-End AI Data Platform featuring Snowpark, dbt, Cortex AI, and Streamlit.