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Chapter 3: Use of key objects-User-Defined Functions (UDFs)
Chapter 4: Use of key objects-User-Defined Table Functions (UDTFs)
Chapter 8: Other third-party libraries (not managed by Anaconda repository)
Chapter 15: Development environments-Microsoft Visual Studio Code (VS Code)
Chapter 17: Parameters for the CONNECT function
Chapter 18: Authentication methods-Construct a dictionary
Chapter 19: Authentication methods-Key pair authentication
Chapter 21: Authentication methods-Snowflake CLI or .env parameters-SessionBuilder
Chapter 22: Authentication methods-Snowflake CLI or .env parameters-Session methods
Chapter 23: Authentication methods-Snowflake CLI or .env parameters-Session attributes
Chapter 30: From SQL statements
Chapter 32: From pandas DataFrames
Chapter 34: -Data types (for example,IntegerType, StringType, DateType)
Chapter 35: Create UDFs from files (locally, on a stage)
Chapter 36: Use Python modules (packaged Python code) with UDFs
Chapter 37: Write Python function to create UDFs and UDTFs
Chapter 38: Register UDFs and UDTFs (for example, session.utf(...), functions.utf(...))
Chapter 39: Secure UDFs and UDTFs-Use SQL to alter UDFs and UDTFs created with Snowpark
Chapter 40: Secure UDFs and UDTFs-Grant access to UDFs and UDTFs to share code
Chapter 41: Secure UDFs and UDTFs-Understanding how to grant object permissions so other Snowflake users can see and use the UDFs and UDTFs
Chapter 42: Data types (type hints vs. registration API)-Provide the data types as parameters when creating a UDF or UDTF to return as Python hints/specify them as part of the registration
Chapter 44: Create stored procedures from files (locally, on stage)
Chapter 46: Use Python modules (packaged code, Anaconda) with stored procedures
Chapter 49: Secure stored procedures-Use SQL to alter stored procedures created with Snowpark
Chapter 51: Data types (type hints vs. registration API)-Provide the data types as parameters when creating a stored procedure to return as Python hints/specify them as part of the registration
Chapter 52: Create Directed Acyclic Graphs (tasks) executing stored procedures-Python API
Chapter 53: Bring Python modules (packaged code) to be used with UDFs -Stored procedures to enable reuse of code
Chapter 56: Input/output (parameters)
Chapter 57: Snowpark DataFrames
Chapter 68: DataFrames-UDFs
Chapter 69: DataFrames-Traverse semi-structured data
Chapter 70: DataFrames-Explicitly cast values in semi-structured data
Chapter 71: DataFrames-Flatten an array of objects into rows
Chapter 72: DataFrames-Load semi-structured data into DataFrames
Chapter 74: DataFrames-Save DataFrame results as Snowflake tables
Chapter 80: warehouses-Use cases for Snowpark-optimized virtual warehouses
Chapter 81: warehouses-Modify Snowpark-optimized virtual warehouse properties
Chapter 82: warehouses-Billing for Snowpark-optimized virtual warehouses
Chapter 83: warehouses-When to scale up/down virtual warehouses
Chapter 84: Caching DataFrames (using .cache_result()) and understanding why this is useful
Chapter 88: Snowpark DataFrames versus pandas on Snowflake
Chapter 89: Synchronous versus asynchronous calls-Block parameter
Chapter 90: Event tables
Chapter 93: Query history (SQL equivalency to help identify bottlenecks)
Chapter 124: Implement performance improvements-Use the query acceleration service
Chapter 165: Access the Data Exchange

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