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Free Pre-requisite TrainingJoin a free session to assess your readiness for the course. This session will help you understand the course structure and evaluate your current knowledge level to start with confidence.
Assessments (Qubits)Take assessments to measure your progress clearly. Koenig's Qubits assessments identify your strengths and areas for improvement, helping you focus effectively on your learning goals.
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Free Revision Classes Join our free revision classes to reinforce your learning. These classes revisit important topics, clarify doubts, and help solidify your understanding for better training outcomes.
Inclusions in Koenig's Learning Stack may vary as per policies of OEMs
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♱ Excluding VAT/GST
You can request classroom training in any city on any date by Requesting More Information
Inclusions in Koenig's Learning Stack may vary as per policies of OEMs
SNOWFLAKE DATA ENGINEER IN 3 DAYS
This intensive 3-day course is designed for professionals aiming to master the data engineering workflow on Snowflake for efficient data querying, development, and pipeline creation.
Target Audience and Job Roles:
Introduction:
The "Snowflake Data Engineer in 3 Days" course provides comprehensive training on Snowflake's data engineering capabilities, including data ingestion, transformation, performance optimization, and management. Participants will gain practical experience through lectures, demos, and labs.
Learning Objectives and Outcomes:
Understanding Authentication Methods: Learn the various authentication methods supported by Snowflake, including the configuration and management of drivers, clients, and connectors.
Data Storage Proficiency: Master the principles of storing and querying semi-structured data in Snowflake and understand the integration with data lakes.
Data Ingestion Techniques: Acquire skills in bulk and continuous data loading, using Snowpipe and Snowpipe Streaming, and leveraging connectors like Kafka for efficient data ingestion.
Orchestration Best Practices: Develop and manage tasks and streams within Snowflake to automate and streamline data workflows.
Data Transformation Methods: Implement dynamic tables, scripting, stored procedures, and user-defined functions (UDFs and UDTFs) to transform data efficiently within Snowflake.
Performance Optimization Strategies: Learn to enhance data processing performance through natural and explicit clustering, as well as utilizing services like Search Optimization and Automatic Clustering.
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