Bootcamp Kỹ thuật Dữ liệu: SQL, ETL, PySpark & AWS (Thực hành)

Master the Core of Modern Data Engineering – Build Real-World Pipelines with Airflow, AWS, Spark, and Python.

Take your first step into data engineering and future-proof your career with this hands-on, project-based bootcamp built on the modern data stack.

Taught by a senior data architect with 12+ years of real-world experience, this course blends theory and practice to help you design, build, and orchestrate scalable data systems like those used at top tech companies.

Whether you’re an aspiring data engineer, software developer, or analyst, this course will guide you through building enterprise-grade data pipelines from scratch, all through a real-life ride-hailing app project that simulates real-world data challenges.

What You’ll Learn

You’ll gain hands-on expertise in the most essential components of data engineering:

Section 1: Context Setup

• Understand the Modern Data Stack and real-world data architectures

• Learn how data flows across systems in data-driven companies

• Set up your foundation using a ride-hailing app scenario

Section 2: Data Lake Essentials

• Build scalable data lakes on AWS S3 with best practices

• Master S3 architecture, partitioning, and schema evolution

• Implement IAM, encryption, and lifecycle management

• Get hands-on with Boto3 S3 APIs for automation

Section 3: Data Modeling

• Design dimensional models (Star Schema) for analytics

• Implement Slowly Changing Dimensions (SCD Type 1 & 2)

• Build ETL pipelines and data marts end-to-end

Section 4: Data Quality Frameworks

• Learn how to ensure data accuracy, completeness, and consistency

• Implement data validation and data contracts

• Use industry best practices to maintain trust in data

Section 5: AWS Athena

• Query massive datasets using AWS Athena (serverless SQL engine)

• Learn DDL, Glue Catalog, workgroups, and automation via Boto3

• Compare Athena, Presto, and Trino

• Apply optimization strategies for performance

Section 6: Apache Spark on AWS EMR

• Build scalable PySpark pipelines with the Write-Audit-Publish (WAP) pattern

• Understand Spark architecture and APIs

• Run production-grade Spark jobs on AWS EMR

• Apply UDFs and data quality checks in transformations

Section 7: Apache Airflow Orchestration

• Master workflow orchestration using Apache Airflow

• Design DAGs, manage dependencies, and schedule jobs

• Automate Spark jobs using a custom AWS EMR plugin

• Build reusable, reliable orchestration solutions

What You’ll Build

By the end of the course, you’ll have built your own production-style data platform for a ride-hailing company, including:

• A Data Lake on AWS S3

• Dimensional Data Model with SCD logic

• PySpark-based ETL pipelines

• Automated orchestration with Airflow

• Query layer powered by Athena

• Data quality framework for validation and monitoring

Who This Course Is For

• Aspiring Data Engineers and ETL Developers

• Analysts or Software Engineers moving into data roles

• Anyone passionate about building scalable data systems on the cloud

Why Learn from Me

I am Andalib Ansari, a Data Architect with 12+ years of experience designing and implementing data platforms and analytics solutions across industries. My goal is to make you confident in real-world data engineering skills, not just theory.

Enroll Now

Use coupon DEBS12025 for special pricing. Take the first step in your data engineering journey and start building your own real-world data pipelines today!