Automatic Data Ingestion Using Snowflake Snowpark Python Api

What you'll learn
How to automate data ingestion from named stages to snowflake table using Snowpark Python API
How to use infer-schema to automate data ingestion activities.
How to build automation utilities using Snowpark Python API
How to build end to end data onboarding tool using Snowpark Python API
Requirements
Basic working knowledge with Python Programming Language
Working knowledge with Snowflake Cloud Data Warehouse
Basic working knowledge with Snowpark Python API
Description
The "Infer Schema" feature for CSV files or automatic CSV schema detection in Snowflake is a highly valuable utility for data teams. With this addition, Snowflake empowers data developers to effortlessly create tables without the need for manual intervention. In the past, creating permanent or transient tables required laborious DDL SQL Statements, consuming a significant portion of development efforts, ranging from 5 to 15% in man-hours, particularly for extensive data projects.This innovative feature significantly streamlines the process by automatically scanning CSV and JSON files, extracting column names, and identifying data types using the "Infer Schema" table function. By leveraging this automation, data developers can now create tables without dedicating excessive time and energy.The advantages of the "Infer Schema" functionality are twofold. Firstly, it saves valuable time and effort, freeing up data teams to focus on other critical aspects of their projects. Secondly, it mitigates the risk of schema mismatches, thereby preserving data integrity throughout the data pipeline.By enabling Snowflake to intelligently infer column names and data types, this feature ensures that the data is accurately and seamlessly integrated into the system. This automation eliminates the likelihood of human errors during the manual table creation process, minimizing the chances of inconsistencies or data corruption.Furthermore, this utility is not limited to merely detecting CSV file schemas; it extends its support to JSON files as well, making it even more versatile and indispensable for various data handling scenarios.In conclusion, the "Infer Schema" for CSV Files or automatic CSV schema detection feature in Snowflake is a game-changer for data teams. By simplifying and accelerating the table creation process, it elevates overall productivity, reduces development efforts, and guarantees data integrity, all contributing to a more efficient and reliable data management ecosystem. Snowflake continues to demonstrate its commitment to empowering users with cutting-edge tools that optimize data workflows and ensure a seamless data analytics experience.
Overview
Section 1: Introduction
Lecture 1 Power Of Infer-schema In Snowflake With Snowpark
Lecture 2 Prerequisite To Build Snowpark Python Utility
Lecture 3 Overall Design Approach For Snowpark Python Utility
Section 2: User, Database, Schema, Stage, File Format Creation & Data Loading
Lecture 4 Create Users For Snowpark & Database/Schema/Stage/File-Formats
Lecture 5 Snowpark Session Using Basic Authentication
Section 3: Working With Snowpark Python API
Lecture 6 Snowflake Snowpark Python API Utility
Section 4: Alternative Authentication Approach in Snowpark
Lecture 7 Snowpark Authentication Approaches
Section 5: Key Pair Authentication - Snowflake Snowpark Python API
Lecture 8 Key Pair (RSA) Based Authentication - Snowpark Python API
Section 6: SSO Based Authentication - Snowflake Snowpark Python API
Lecture 9 Create Snowpark Session Using SSO Based Authentication
Snowflake Cloud Data Developer,Snowflake Cloud Data Engineer,Snowflake Cloud Data Architect,Python Data Developer,Snowflake Data Engineer


Homepage

https://www.udemy.com/course/automatic-data-ingestion-using-snowflake-snowpark-python-api/