Snowflake Data Analyst
Mastering Data Engineering with Snowflake for Efficient Data Pipelines, Performance Optimization, and Advanced Data Processing
Duration
2 Days (8 hours per day)
Level
Intermediate Level
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This two-day, role-specific course introduces data analysts to how the Snowflake AI Data Cloud enables them to deliver actionable insights and reports to their organization to drive business value. The course is structured around a data analysis lifecycle placing each capability Snowflake provides in the data analyst’s context. The course begins with how to connect to the Snowflake Data Cloud. It then covers how to analyze, ingest, enrich, report, and diagnose data insights with Snowflake. The course consists of lectures, labs, demonstrations, and discussions.
ACQUIRED SKILLS
- Outline the unique and differentiated architecture of the Snowflake AI Data Cloud.
- Exploit the options provided to connect and interact with the Snowflake AI Data Cloud.
- Describe how to use BI tools for data analysis in Snowflake.
- Summarize query constructs, Data Definition Language (DDL), Data Manipulation Language (DML), and Data Query Language (DQL) operations.
- Use Snowflake’s extensive SQL capabilities and the built-in table, scalar, window, and estimation functions to support data analysis.
- Apply Snowflake best practices when working with semi-structured data.
- Load and transform data.
- Visualize data outside of Snowflake.
- Employ Snowflake features in the reporting process and activities performed by the data analyst to create analytic visualizations.
- Explain Snowflake’s unique approach to caching and the benefits to query performance.
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- Data Analysts
- Business Intelligence users
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- Why Snowflake?
- Snowflake Functional Architecture
- Platform Features
- Getting Started with Snowflake
- Snowflake Architecture Layers
- Snowflake Structure
- Information Schema and Account Usage
- Using Parameters to Control Snowflake
- Query Tags
- Sample Data Sets
- Client Interface Overview
- Authentication • Snowsight
- SnowSQL
- Connecting Tools to Snowflake
- Data Definition Language (DDL)
- Data Manipulation Language (DML)
- Data Query Language (DQL)
- Set Operations and Joins
- Subqueries and Common Table Expressions
- Dynamic Pivot Queries
- Constructing Eicient Queries
Copilot
- Function Overview
- Estimation Functions
- User-defined Functions
- Stored Procedures
- Snowflake Scripting
- Collation
- Sampling
- Snowflake Tasks
- Alerts
- Window Functions, Syntax, and Usage
- Group By and Grouping Sets
- Recursive With and Connect By
- Semi-structured Data Overview
- Query Semi-structured Data
- Time Travel
- Cloning
- Data Loading Concepts
- Examples of Data Loading
- Explore Data
- Prepare Data
- Analyze Data
- Visualize Data
- Integrate BI Tools
- Metadata and Caching Overview
- Metadata
- Query Result Cache
- Data Cache
- Query Pruning
- Using Explain
- Using Query Profile
- SQL Performance Tips
- Performance Bottlenecks
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Basic knowledge of SQL is required