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The Snowflake with dbt Syllabus focuses on modern cloud data engineering and analytics using Snowflake and dbt (Data Build Tool). It covers data modeling, SQL transformations, ELT workflows, testing, documentation, automation, and analytics engineering practices in cloud data platforms. This training explains how organizations build scalable, reliable, and well-structured data pipelines for reporting and business intelligence. It is important because modern enterprises rely on cloud-based analytics and automated data transformation for faster decision-making and efficient data management. It is ideal for data engineers, analytics engineers, BI developers, SQL professionals, and cloud data specialists working on modern data warehouse solutions.
🎯 Mode: Theory + Hands-on Labs + Real-Time Data Project
Duration of Training : 40–50 Hours
Batch type : Weekdays/Weekends
Mode of Training : Classroom/Online/Corporate Training
Detailed Syllabus • Hands-on Labs • Assignments • Support-Focused • Implementation
Curriculum Designed by Experts
• Data Engineers
• BI Developers
• Analytics Engineers
• Cloud Data Professionals
• ETL Developers transitioning to ELT
This program focuses on modern data stack architecture (Snowflake + dbt + Cloud +
CI/CD).
• OLTP vs OLAP
• Data Warehouse Architecture
• ETL vs ELT
• Star & Snowflake Schema
• Slowly Changing Dimensions (SCD)
• Data Lake vs Data Warehouse
Overview of Snowflake
• Snowflake Architecture
o Storage Layer
o Compute Layer
o Cloud Services Layer
• Virtual Warehouses
• Pricing & Cost Optimization
• Multi-cluster Warehouses
Hands-on Lab:
• Create database, schema, tables
• Load data using COPY INTO
• Manage warehouses
• Advanced SQL
• Window Functions
• CTEs
• Semi-structured Data (JSON, VARIANT)
• Streams & Tasks
• Time Travel & Zero Copy Cloning
• Secure Data Sharing
Lab:
• Build Sales Data Warehouse
• Implement SCD Type 1 & 2
• Query JSON data
• Snowpipe
• Bulk Loading
• External Stages (S3/Azure/GCS)
• File formats
• Performance tuning
Lab:
• Load data from cloud storage
• Automate ingestion
Overview of dbt
• dbt Architecture
• dbt Core vs dbt Cloud
• Installation & Setup
• Profiles & Targets
• Project Structure
Lab:
• Setup dbt with Snowflake
• Configure environment
• Models
• Materializations (View, Table, Incremental)
• Jinja & Macros
• Sources & Seeds
• Tests (Generic & Custom)
• Documentation
Lab:
• Build ELT pipeline
• Create incremental models
• Implement tests
• Snapshots
• Packages
• Refactoring & Modularization
• Performance optimization
• Data lineage
• Deployment environments
Lab:
• Build production-ready model
• Implement snapshots
• Git integration
• CI/CD pipeline setup
• Job scheduling
• Environment promotion (Dev → QA → Prod)
• Monitoring dbt jobs
• Role-based access in Snowflake
• Row-level & Column-level security
• Data masking
• Secure Views
• Auditing & compliance
• Query optimization
• Warehouse sizing
• Auto suspend/resume
• Clustering keys
• Monitoring query history
End-to-End Modern Data Pipeline
Build a complete pipeline:
• Ingest data into Snowflake
• Transform using dbt
• Implement incremental loading
• Apply security controls
• Create documentation
• Deploy using CI/CD
• End-to-End ELT Project
• 100+ Interview Questions
• Resume Template for Data Engineer / Analytics Engineer
• 5 Real-Time Case Studies
• Mock Interviews
• Snowflake Data Engineer
• Analytics Engineer (dbt)
• Cloud Data Engineer
• BI Developer
• ELT Developer
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(Our Team will call you to discuss the Fees)