• What is Cloud
• Different Cloud Vendors
• Advantages of Cloud over On-Premise

• What is a Data Warehouse, and Why do we need a Data Warehouse?
• Database Vs Data Warehouse.
• Data Warehouse Architecture
• OLTP Vs OLAP
• What is ETL

• How different from traditional DB
• Quick start to the snowflake and accessing trial account
• Creating warehouse, DB, Schema, and tables
• Accessing different roles and using it
• Working with worksheets
• Understanding different type of accounts

• AWS and understanding S3 storage
• Snowflake architecture and caching
• AZURE and understanding blob storage
• GCP and understanding Bucket storage

• File formats
• Internal and external storage
• Internal and external stage
• Copy into usage
• Snowflake internal storage
• Accessing Cloud storage data into Snowflake (GCP, AZURE and AWS)
• Data unloading

• Accessing Snowpipe
• PUT and GET commands
• Bulk loading from cloud storage
• Continuous loading

• Snowflake Connector and use cases Python
• BI connectors use cases
• Other connectors hands-on

• Variant Data Type
• File format options
• Creating stages
• Loading JSON semi-structured data into SF tables
• Accessing JSON with select statement

• Creating Tasks
• Streams
• Accessing procedures with tasks
• Scheduling as per time with Different time zones
• Automate loading process Daily and Weekly

• Usage of sharing data
• Sharing data with different accounts
• Sharing data with non-SF accounts using reader accounts
• Importance of reader accounts
• Privileges in data sharing
• Challenges with cross-region sharing and understanding replication
• Connecting shared objects with BI tools
• Limitations with Data sharing

Access Control Privileges for Cloned Objects

Cloning and Snowflake Objects

Impact of DDL on Cloning

Impact of DML and Data Retention on Cloning

• Introduction to Time Travel
• Querying Historical Data
• Enabling and Disabling Time Travel
• Data Retention Period
• Cloning Using Time Travel (Databases, Schemas, and Tables Only)

• Creating multi-users on large tables
• Performance techniques
• Result set cache
• Metadata cache
• Query data cache
• Best practices of using caching for performance and cost optimization

• Error Handling and Validations
• Snowflake Pricing model and selecting best Edition and Calculation of Credits usage
• Resource Monitoring
• Data Masking
• Partitioning and Clustering in snowflake
• Materialized View and Normal View
• Integration with Python
• Integration with AWS, Azure and Google Cloud
• Best Practices to follow