Join our intensive 3-day workshop, ‘Data Analysis in Snowflake – Python & DataFrames’, designed specifically for Data Analysts. This hands-on training will immerse you in the essential aspects of Snowflake, with a focus on practical data analysis using Python and DataFrames. Engage in real-world scenarios, enhance your skills, and transform your data operations with the power of Snowflake and Python.
Format
Mostly workshops
Duration
3 days
Prerequisites
Basic understanding of SQL
Basic familiarity with Python programming
Experience with data analysis concepts is a plus but not mandatory
Target Audience
Data Analysts
Data Engineers
IT Professionals involved in data analysis
Anyone interested in enhancing their data analysis skills using Snowflake and Python
Training Program
Snowflake Overview
Introduction to Snowflake
Key features and architecture
Client Interfaces
Using the Snowflake web interface
Connecting through various client tools
Setting up Python environment for Snowflake
Retrieving Data with Python and DataFrames
Loading data into DataFrames
Using Built-in Functions for Processing and Formatting Retrieved Data
String functions in Python
Date and time functions in Python
Type Conversion in DataFrames
Converting between data types
Practical use cases
Sorting DataFrames
Sorting techniques
Filtering Rows in DataFrames
Applying conditions and logical operators
Aggregate Functions with DataFrames
Aggregating data functions like COUNT, SUM, AVG, MIN, MAX
Grouping and Filtering Groups in DataFrames
GROUP BY operations
Filtering groups with HAVING conditions
Joining DataFrames
Merging DataFrames to simulate SQL JOIN operations
Practical examples of data joining
Inner and Outer Joins, Cartesian Product with DataFrames
Differences between join types
When to use each type
Subqueries with DataFrames
Simulating single-row and multi-row subqueries
Set Operations with DataFrames
UNION, INTERSECT, EXCEPT
Window Functions with DataFrames
Windowing operations like ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, FIRST_VALUE, LAST_VALUE
DML Operations with DataFrames
INSERT, UPDATE, and DELETE operations
Working with Semi-Structured Data
Loading and querying JSON, Avro, and other formats
Database Objects
Understanding tables, views, sequences in the context of Snowflake
Managing database objects through Python
DDL – Creating Tables and Data Types in Snowflake
Using Python to execute CREATE TABLE statements
Defining data types and constraints
Time Travel & Cloning in Snowflake
Accessing historical data and performing data recovery
Using cloning for data replication
Views – Creating and Retrieving Data
Creating and managing views in Snowflake through Python
Practical use cases for views
Acquired skills
By the end of this workshop, participants will be well-equipped with the knowledge and hands-on experience needed to leverage Snowflake for data analysis using Python and DataFrames.