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Chapter 1: Data Ingestion Data Preparation-Use a collection system to retrieve data.-Assess how often data needs to be collected
Chapter 2: Data Ingestion Data Preparation-Use a collection system to retrieve data.-Identify the volume of data to be collected
Chapter 3: Data Ingestion Data Preparation-Use a collection system to retrieve data.-Identify data sources
Chapter 4: Data Ingestion Data Preparation-Use a collection system to retrieve data.-Retrieve data from a source
Chapter 5: Data Ingestion Data Preparation-Perform data discovery to identify what is needed from the available datasets.-Query tables in Snowflake
Chapter 6: Data Ingestion Data Preparation-Perform data discovery to identify what is needed from the available datasets.-Evaluate which transformations are required
Chapter 7: Data Ingestion Data Preparation-Enrich data by identifying and accessing relevant data from the Snowflake Marketplace.-Find external data sets that correlate with available data
Chapter 8: Data Ingestion Data Preparation-Enrich data by identifying and accessing relevant data from the Snowflake Marketplace.-Use data shares to join data with existing data sets
Chapter 9: Data Ingestion Data Preparation-Enrich data by identifying and accessing relevant data from the Snowflake Marketplace.-Create tables and views
Chapter 10: Data Ingestion Data Preparation-Outline and use best practice considerations relating to data integrity structures.-Primary keys for tables
Chapter 11: Data Ingestion Data Preparation-Outline and use best practice considerations relating to data integrity structures.-Perform table joins between parent/child tables
Chapter 12: Data Ingestion Data Preparation-Outline and use best practice considerations relating to data integrity structures.-Constraints
Chapter 13: Data Ingestion Data Preparation-Implement data processing solutions.-Aggregate and enrich data
Chapter 14: Data Ingestion Data Preparation-Implement data processing solutions.-Automate and implement data processing
Chapter 15: Data Ingestion Data Preparation-Implement data processing solutions.-Respond to processing failures
Chapter 16: Data Ingestion Data Preparation-Implement data processing solutions.-Use logging and monitoring solutions
Chapter 17: Data Ingestion Data Preparation-Given a scenario, prepare data and load into Snowflake.-Load files using Snowsight
Chapter 18: Data Ingestion Data Preparation-Given a scenario, prepare data and load into Snowflake.-Load data from external/internal stages into a Snowflake table
Chapter 19: Data Ingestion Data Preparation-Given a scenario, prepare data and load into Snowflake.-Load different types of data
Chapter 20: Data Ingestion Data Preparation-Given a scenario, prepare data and load into Snowflake.-Perform general DML (insert, update, delete)
Chapter 21: Data Ingestion Data Preparation-Given a scenario, prepare data and load into Snowflake.-Identify and resolve data import errors
Chapter 22: Data Ingestion Data Preparation-Given a scenario, use Snowflake functions.-Scalar functions
Chapter 23: Data Ingestion Data Preparation-Given a scenario, use Snowflake functions.-Aggregate functions
Chapter 25: Data Ingestion Data Preparation-Given a scenario, use Snowflake functions.-Table functions
Chapter 26: Data Ingestion Data Preparation-Given a scenario, use Snowflake functions.-System functions
Chapter 27: Data Ingestion Data Preparation-Given a scenario, use Snowflake functions.-Geospatial functions
Chapter 28: Data Transformation Data Modeling-Prepare different data types into a consumable format.-CSV
Chapter 29: Data Transformation Data Modeling-Prepare different data types into a consumable format.-JSON (query and parse)
Chapter 30: Data Transformation Data Modeling-Prepare different data types into a consumable format.-Parquet
Chapter 31: Data Transformation Data Modeling-Given a dataset, clean the data.-Identify and analyze data anomalies
Chapter 32: Data Transformation Data Modeling-Given a dataset, clean the data.-Handle erroneous data
Chapter 33: Data Transformation Data Modeling-Given a dataset, clean the data.-Validate data types
Chapter 34: Data Transformation Data Modeling-Given a dataset, clean the data.-Use clones as required by specific use-cases
Chapter 35: Data Transformation Data Modeling-Given a dataset or scenario, work with and query the data.-Aggregate and validate the data.
Chapter 36: Data Transformation Data Modeling-Given a dataset or scenario, work with and query the data.-Apply analytic functions
Chapter 37: Data Transformation Data Modeling-Given a dataset or scenario, work with and query the data.-Perform pre-math calculations (examples, randomization, ranking, grouping, min/max)
Chapter 38: Data Transformation Data Modeling-Given a dataset or scenario, work with and query the data.-Perform classifications
Chapter 39: Data Transformation Data Modeling-Given a dataset or scenario, work with and query the data.-Perform casting - change data types to ensure data can be presented consistently
Chapter 40: Data Transformation Data Modeling-Given a dataset or scenario, work with and query the data.-Enrich the data
Chapter 41: Data Transformation Data Modeling-Given a dataset or scenario, work with and query the data.-Leverage partition pruning
Chapter 42: Data Transformation Data Modeling-Given a dataset or scenario, work with and query the data.-Use Time Travel and cloning features
Chapter 43: Data Transformation Data Modeling-Given a dataset or scenario, work with and query the data.-Use built-in functions for traversing, flattening, and nesting semi-structured data
Chapter 44: Data Transformation Data Modeling-Given a dataset or scenario, work with and query the data.-Use native data types
Chapter 45: Data Transformation Data Modeling-Use data modeling to manipulate the data to meet BI requirements.-Select and implement an effective data model
Chapter 46: Data Transformation Data Modeling-Use data modeling to manipulate the data to meet BI requirements.-Identify when to use a data model and when to use a flattened data set
Chapter 47: Data Transformation Data Modeling-Use data modeling to manipulate the data to meet BI requirements.-Use different modeling techniques for the consumption layer (for example, dimensional, Data Vault)
Chapter 48: Data Transformation Data Modeling-Optimize query performance.-Understand the attributes of the Query Profile
Chapter 49: Data Transformation Data Modeling-Optimize query performance.-Understand how to view and analyze the query execution plan
Chapter 50: Data Transformation Data Modeling-Optimize query performance.-Troubleshoot query performance
Chapter 51: Data Transformation Data Modeling-Optimize query performance.-Leverage result, metadata, and virtual warehouse caching
Chapter 52: Data Transformation Data Modeling-Optimize query performance.-Use of different types of database objects, such as materialized views
Chapter 53: Data Analysis-Use SQL extensibility features.-User-Defined Functions (UDFs)
Chapter 54: Data Analysis-Use SQL extensibility features.-Stored procedures
Chapter 56: Data Analysis-Perform a descriptive analysis.-Summarize large data sets using Snowsight dashboards
Chapter 58: Data Analysis-Perform a diagnostic analysis.-Find reasons/causes of anomalies or patterns in historical data
Chapter 59: Data Analysis-Perform a diagnostic analysis.-Collect related data
Chapter 60: Data Analysis-Perform a diagnostic analysis.-Identify demographics and relationships
Chapter 61: Data Analysis-Perform a diagnostic analysis.-Analyze statistics and trends
Chapter 62: Data Analysis-Perform forecasting.-Use statistics and built in functions
Chapter 63: Data Analysis-Perform forecasting.-Make predictions based on data
Chapter 64: Data Presentation Data Visualization- create reports and dashboards to meet business requirements.-Evaluate and select the data for building dashboards
Chapter 65: Data Presentation Data Visualization- create reports and dashboards to meet business requirements.-Understand the effects of row access policies and Dynamic Data Masking
Chapter 66: Data Presentation Data Visualization- create reports and dashboards to meet business requirements.-Compare and contrast different chart types (for example, bar charts, scatter plots, heat grids, scorecards)
Chapter 67: Data Presentation Data Visualization- create reports and dashboards to meet business requirements.-Understand what is required to connect BI tools to Snowflake
Chapter 68: Data Presentation Data Visualization- create reports and dashboards to meet business requirements.-Create charts and dashboard in Snowsight
Chapter 69: Data Presentation Data Visualization- maintain reports and dashboards to meet business requirements.-Build automated and repeatable tasks
Chapter 70: Data Presentation Data Visualization- maintain reports and dashboards to meet business requirements.-Operationalize data
Chapter 71: Data Presentation Data Visualization- maintain reports and dashboards to meet business requirements.-Store and update data
Chapter 72: Data Presentation Data Visualization- maintain reports and dashboards to meet business requirements.-Manage and share Snowsight dashboards
Chapter 73: Data Presentation Data Visualization- maintain reports and dashboards to meet business requirements.-Configure subscriptions and updates
Chapter 74: Data Presentation Data Visualization- incorporate visualizations for dashboards and reports.-Present data for business use analyses
Chapter 75: Data Presentation Data Visualization- incorporate visualizations for dashboards and reports.-Identify patterns and trends
Chapter 76: Data Presentation Data Visualization- incorporate visualizations for dashboards and reports.-Identify correlations among variables
Chapter 77: Data Presentation Data Visualization- incorporate visualizations for dashboards and reports.-Customize data presentations using filtering and editing techniques

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