This training program is specifically designed for Data Engineers seeking to master Snowflake. It covers Snowflake’s architecture, core features, and advanced functionalities, along with its integration with AI to enhance and streamline modern data engineering workflows. Participants will gain in-depth knowledge of data ingestion, efficient data loading, transformation processes, schema evolution, and AI-driven pipeline automation. The program is intended for professionals who will be building and managing scalable data pipelines, as well as designing and maintaining data infrastructure to efficiently load data into the Snowflake platform from various sources, supporting analytics, reporting, and other critical business tasks.
Format
Mostly workshops
Duration
2 days
Prerequisites
Basic SQL knowledge
Understanding of key data engineering concepts and architecture
Basics of software development
Interest in data loading and data transformation
Target Audience
This training is ideal for:
Data Engineers looking to enhance their skills in Snowflake and cloud-based data platforms
ETL/ELT Developers focused on optimizing data ingestion, transformation, and loading processes
Data Architects aiming to design robust data pipelines and infrastructure for Snowflake
Cloud Engineers working with data solutions in cloud environments
Database Administrators interested in managing scalable data infrastructures
Data Scientists looking to streamline data access and integration for AI/ML workflows
Training Program
Snowflake and DWH fundamentals
Overview
Snowflake architecture and layers
Snowsight
Virtual warehouses
Snowflake objects, databases, schemas
Datatypes
Structured and semi-structured data
Snowflake roles and access control
Connection options
Authentication types
Connectors
Snowflake Connector for Python
SnowSQL
Snowflake Native Connectors
Snowflake REST SQL API
Snowflake Connector for Kafka
Snowflake features for Data Loading
Storage
Internal stages
External stages
Streams
Tasks
Serverless Tasks
Data ingestion
ETL vs ELT
Pull vs Push
Bulk vs. Continuous Data Load
Change Data Capture
Snowpipe
Snowpipe Streaming
Snowflake Connector for Kafka
Snowflake Connector for Kafka with Streaming
Data Transformation
Moving data from stage to table
Transformation during load
COPY command
Snowpipe transformation
Dynamic tables
UDF & Stored Procedures
Schema detection and evolution
Schema detection – INFER_SCHEMA
Object creation based on metadata
Automatic schema evolution
AI in Data Engineering
Snowflake Cortex
Copilot
AI enhanced Data Pipelines
AI generated Data Pipelines
Anomaly detection
Data Cleaning
Data Anonymization
Conversion of unstructured data into structured or semi-structured data