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CoursesData Engineering
Snowflake Data Engineering
Data Engineering

Snowflake Data Engineering

Learn Snowflake Data Engineering Course through practical online training. Build data engineering skills with Snowflake and prepare for certification. Ideal for working professionals.

5/5(4,890 Reviews)

Level

Advanced

Duration

8 Weeks

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About Snowflake Data Engineering

The Snowflake Data Engineering Course helps learners develop practical skills in building, managing, and optimizing data pipelines using Snowflake. This Snowflake Data Engineering Training covers data ingestion, SQL transformations, cloud data warehousing, and modern data workflow development. Through Data Engineering with Snowflake, learners explore structured and semi-structured data, continuous data loading, pipeline automation, and analytics-ready data preparation. The course introduces Snowflake features used to build reliable data workflows, manage compute resources, and improve query performance. Snowflake Online Training is designed for aspiring data engineers, IT professionals, and working professionals seeking to strengthen their cloud data engineering skills. Learners also explore concepts relevant to Snowflake Data Engineer Certification preparation, including Snowflake architecture, data loading, transformations, and performance considerations. The training focuses on applying Snowflake-based data engineering concepts to practical scenarios and developing skills relevant to modern data platforms.

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Training Plan

01
About trainer

About trainer

Working professional who is carrying more than 10 years of industry experience.

02
Decks & Updated Content

Decks & Updated Content

Access to updated presentation decks shared during live training sessions.

03
e-Book

e-Book

E-book provided by TechPratham. All rights reserved.

04
Assignments & MCQs

Assignments & MCQs

Module-wise assignments and MCQs provided for practice.

05
Video Recording

Video Recording

Daily Session would be recorded and shared to the candidate.

06
Projects

Projects

Live projects will be provided for hands-on practice.

07
Resume Building

Resume Building

Expert-guided resume building with industry-focused content support.

08
Interview Preparation

Interview Preparation

Comprehensive interview preparation with real-time scenario practice.

Snowflake Data Engineering Course Curriculum

Module 1: Introduction to Snowflake Data Engineering

Understand Snowflake’s role in modern cloud data engineering and the fundamentals of data pipelines and analytics-ready datasets.

Introduction to modern data engineering
Data engineer responsibilities and workflows
Snowflake platform overview
Cloud data warehouse fundamentals
Data pipeline architecture
Introduction to Snowflake learning environment

Module 2: Snowflake Architecture and Core Concepts

Explore Snowflake architecture and how storage, compute and cloud services support data processing and pipeline workloads.

Snowflake architecture overview
Storage, compute and cloud services
Virtual warehouses
Databases, schemas and tables
Snowsight interface
Compute scaling and workload separation

Module 3: SQL Fundamentals for Data Engineering

Develop SQL skills for querying, filtering, transforming and manipulating data within Snowflake.

SQL syntax and query execution
SELECT, WHERE, GROUP BY and HAVING
Joins and subqueries
Common table expressions
Window functions
DDL and DML operations

Module 4: Data Ingestion and Loading

Learn to ingest data from files and cloud storage using stages, file formats and loading commands.

Supported data formats
Internal and external stages
File format objects
COPY INTO command
Loading CSV, JSON and Parquet data
Data validation and load error handling

Module 5: Data Transformation and Modeling

Transform raw datasets into structured, analytics-ready tables using SQL and data modeling concepts.

Data transformation workflows
Data cleaning and standardization
Relational data modeling
Fact and dimension tables
Incremental transformation concepts
MERGE statements and deduplication

Module 6: Semi-Structured Data Processing

Extract, flatten and transform nested data in JSON and other semi-structured formats.

VARIANT data type
JSON ingestion and querying
OBJECT and ARRAY data
FLATTEN function
Nested data extraction
Semi-structured data transformation

Module 7: Snowpipe and Continuous Data Ingestion

Explore continuous ingestion and automated loading from supported cloud storage.

Batch and continuous ingestion
Snowpipe architecture
Automated ingestion concepts
Cloud storage integration fundamentals
File arrival and ingestion workflows
Monitoring ingestion status and errors

Module 8: Streams and Tasks for Pipeline Automation

Use change tracking and task scheduling to create automated Snowflake workflows.

Introduction to Snowflake Streams
Change data capture concepts
Stream types and usage
Introduction to Snowflake Tasks
Task scheduling and dependencies
Stream and Task pipeline implementation

Module 9: Snowpark and Python for Data Engineering

Explore Python-based processing with Snowpark and data work near Snowflake compute.

Introduction to Snowpark
Snowpark DataFrame API
DataFrame transformations
Python UDF fundamentals
Stored procedures with Python
Snowpark execution concepts

Module 10: Performance and Cost Optimization

Assess query performance and manage compute resources efficiently.

Query Profile and execution analysis
Virtual warehouse sizing
Warehouse scaling concepts
Micro-partitioning and pruning
Clustering and materialized views
Query and compute cost considerations

Module 11: Data Security, Monitoring and Reliability

Understand access control, monitoring and operational practices for reliable workflows.

Role-based access control
Roles, users and privileges
Secure data access fundamental
Pipeline monitoring and troubleshooting
Data quality and validation checks
Reliability and recovery considerations

Module 12: End-to-End Snowflake Data Engineering Project

Apply ingestion, transformation, automation and data delivery concepts in an end-to-end project.

Project requirements and data architecture
Source data ingestion
Data transformation and modeling
Automated pipeline development
Performance and validation checks
Project documentation and presentation

Data Engineering Courses

No related courses found

Additional Program Highlights

Learning Materials

Comprehensive study materials and resources

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Resume Writing

Professional resume building session

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Interview Preparation

Master your interview skills

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Live Project Demo

Real-world project demonstrations

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Upcoming Batches

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Who Should Take Snowflake Data Engineering Course?

IT Professionals

Non-IT Career Switchers

Fresh Graduates

Career Opportunities After Learning Snowflake Data Engineering

Snowflake Data Engineer

Cloud Data Engineer

Data Pipeline Engineer

Key Projects

Snowflake Data Engineering

Walmart

Walmart – Retail Data Pipeline


Scenario: Build a Snowflake pipeline to consolidate retail sales data and prepare reliable datasets for business reporting.

Live Work:

  • Load sales files into Snowflake stages.
  • Transform sales data using SQL and MERGE.
  • Automate refreshes using Snowflake Tasks.
Outcome: Automated retail reporting data pipeline
Metro Bank

Metro Bank – Banking Data Integration


Scenario: Design a Snowflake workflow to ingest sample banking transactions and produce validated, analytics-ready datasets.

Live Work:

  • Ingest transaction files using COPY INTO.
  • Clean and validate transaction records.
  • Build incremental processing with Streams.
Outcome: Validated banking data workflow
Accenture

Accenture – Enterprise Data Transformation


Scenario: Create a Snowflake transformation workflow that combines sample enterprise datasets into structured reporting tables.

Live Work:

  • Model source data into reporting tables.
  • Transform records using Snowflake SQL.
  • Review query performance and data quality.
Outcome: Analytics-ready enterprise datasets
Infosys

Infosys – Snowflake Data Quality Pipeline


Scenario: An enterprise needs to validate incoming customer and transaction data in Snowflake, identify errors, and maintain reliable datasets for reporting and analytics.

Live Work:

  • Build automated data quality checks using SQL
  • Identify duplicate, null, and invalid records
  • Create validation reports and exception tables
Outcome: Improved data quality and reliable analytics
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Commitment

  • Ensuring quality training every day

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Fulfillment

  • Meeting learning goals with confidence

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Accomplishment

  • Students achieving industry-ready expertise

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Beyond Courses:

Additional Support We Provide

24/7 Support

LinkedIn Profile

Resume Writing

Alumni Sessions

Interview Preparation

Live Projects

What is Snowflake Data Engineering?

What is covered in the Snowflake Data Engineering Course?

Is this course suitable for beginners?

Do I need SQL knowledge before learning Snowflake Data Engineering?

Can working professionals take Snowflake Online Training?

What is Snowflake used for in data engineering?

What is Snowflake, and how is it used in data engineering?

Explain the architecture of Snowflake.

What is a virtual warehouse in Snowflake?

What is the difference between an internal stage and an external stage?

How does the COPY INTO command work?

What is the difference between Snowpipe and bulk loading?

Snowflake Data Engineer Certification & Course Completion Certificate

The SnowPro Advanced: Data Engineer is an official vendor certification offered by Snowflake. It validates advanced knowledge and skills in applying data engineering principles using Snowflake, including data ingestion, transformation, pipeline development, data governance, and performance optimization.


Certification Details


  • Certification Name: SnowPro Advanced: Data Engineer
  • Certification Provider: Snowflake
  • Certification Level: Advanced
  • Exam Code: DEA-C02
  • Exam Format: Multiple-choice, multiple-select, and interactive questions 
  • Exam Duration: 115 minutes 
  • Exam Fee: USD 375 standard list price; regional pricing may vary
  • Exam Delivery: Online proctored or at an authorized test center, subject to available options
  • Prerequisites: Review Snowflake's current certification requirements and recommended experience before registering
  • Recommended Experience: Hands-on production experience in Snowflake data engineering is recommended
  • Certification Validity: Two years, subject to Snowflake's current certification policy
  • Passing Score: Check the current official exam guide for the applicable passing criteria.


Exam Domains and Weightage

Refer to the current official SnowPro Advanced: Data Engineer exam guide for the latest exam domains, objectives, and weightage. Domain percentages can change between exam versions and should not be copied from an outdated exam guide.


TechPratham Course Completion Certificate

Learners who successfully complete the Snowflake Data Engineering training requirements receive a TechPratham course completion certificate, subject to the institute's applicable completion criteria.

The TechPratham certificate is separate from the SnowPro Advanced: Data Engineer credential. The official Snowflake certification is awarded by Snowflake only to candidates who meet its certification requirements and pass the applicable certification examination.

The training provides learning and practical project experience relevant to Snowflake data engineering and the certification objectives. Enrollment or completion of the TechPratham

Industry-Recognized Certification

Certificate
Snowflake Data Engineering Certificate

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