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Chapter 1: Given a data set, load data into Snowflake.-Outline considerations for data loading
Chapter 2: Given a data set, load data into Snowflake.-Define data loading features and potential impact
Chapter 3: Ingest data of various formats through the mechanics of Snowflake.-Required file formats
Chapter 4: Ingest data of various formats through the mechanics of Snowflake.-Ingestion of structured, semi-structured, and unstructured data
Chapter 5: Ingest data of various formats through the mechanics of Snowflake.-Implementation of stages and file formats
Chapter 8: Design, build, and troubleshoot continuous data pipelines.-Stages
Chapter 9: Design, build, and troubleshoot continuous data pipelines.-Tasks
Chapter 10: Design, build, and troubleshoot continuous data pipelines.-Streams
Chapter 11: Design, build, and troubleshoot continuous data pipelines.-Snowpipe (for example, Auto ingest as compared to Rest API)
Chapter 12: Design, build, and troubleshoot continuous data pipelines.-Snowpipe Streaming
Chapter 13: Analyze and differentiate types of data pipelines.-Create User-Defined Functions (UDFs)
Chapter 14: Analyze and differentiate types of data pipelines.-Design and use the Snowflake SQL API
Chapter 16: Install, configure, and use connectors to connect to Snowflake.-Kafka connectors
Chapter 17: Install, configure, and use connectors to connect to Snowflake.-Spark connectors
Chapter 18: Install, configure, and use connectors to connect to Snowflake.-Python connectors
Chapter 21: Implement row-level filtering
Chapter 24: use external tables and define how they work.-Manage external tables
Chapter 26: use external tables and define how they work.-Perform general table management
Chapter 27: use external tables and define how they work.-Manage schema evolution
Chapter 28: use external tables and define how they work.-Unload data
Chapter 30: Outline telemetry around the operation
Chapter 34: Virtual warehouse properties (for example, size, multi-cluster)
Chapter 35: Query complexity
Chapter 36: Micro-partitions and the impact of clustering
Chapter 38: Search optimization service
Chapter 44: Snowflake objects-Snowpipe Streaming
Chapter 53: Storage & Data Protection-Use Time Travel and cloning to create new development environments.-Clone objects
Chapter 54: Storage & Data Protection-Use Time Travel and cloning to create new development environments.-Validate changes before promoting
Chapter 55: Storage & Data Protection-Use Time Travel and cloning to create new development environments.-Rollback changes
Chapter 56: Monitor data.-Apply object tagging and classifications
Chapter 60: Implement column-level security-Use in conjunction with Dynamic Data Masking
Chapter 61: Implement column-level security-Use in conjunction with external tokenization
Chapter 62: Implement column-level security-Use projection policies
Chapter 63: Use data masking with Role-Based Access Control (RBAC) to secure sensitive data
Chapter 64: Explain the options available to support row-level security using Snowflake row access policies-Use aggregation policies
Chapter 65: Use DDL to manage Dynamic Data Masking and row access policies
Chapter 67: Use Snowflake Data Clean Rooms to share data-Use the web-app
Chapter 69: Snowpark UDFs (for example, Java, Python, Scala)
Chapter 70: Secure UDFs
Chapter 73: User-Defined Table Functions (UDTFs)
Chapter 74: User-Defined Aggregate Functions (UDAFs)
Chapter 77: Snowpark stored procedures (for example, Java, Python, Scala)
Chapter 78: Traverse and transform semi-structured data to structured data
Chapter 79: Transform structured data to semi-structured data
Chapter 83: Use Snowpark for data transformation.-Understand Snowpark architecture
Chapter 84: Use Snowpark for data transformation.-Query and filter data using the Snowpark library
Chapter 85: Use Snowpark for data transformation.-Perform data transformations using Snowpark (for example, aggregations)
Chapter 86: Use Snowpark for data transformation.-Manipulate Snowpark DataFrames
Chapter 87: Use Snowpark for data transformation.-SQL Scripting stored procedures
Chapter 88: Use Snowpark for data transformation.-JavaScript stored procedures
Chapter 89: Use Snowpark for data transformation.-Transaction management

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