AWS Data Engineering
AWS runs more production data infrastructure than any other cloud, and AWS Data Engineers are among the most in-demand roles in 2026. In two months you'll master the entire AWS data stack the way senior engineers actually use it: Glue + EMR for processing, Redshift + Athena for warehousing, Kinesis for streaming, Lambda + Step Functions for serverless orchestration, and Managed Airflow (MWAA) for production scheduling. Five hands-on projects mirror real systems at AWS-first companies. Prepares you fully for the AWS Certified Data Engineer Associate (DEA-C01) exam.
0 lessons
What you'll learn
- Architect lakehouse pipelines on S3 with Iceberg or Hudi
- Build serverless ETL using Glue + Lambda + Step Functions
- Process streaming data with Kinesis + Lambda end-to-end
- Run production Spark on EMR with auto-scaling and cost controls
- Query S3 directly with Athena — petabyte scale, pay-per-query
- Orchestrate dependencies with MWAA (Managed Airflow)
- Implement IAM least-privilege patterns across data pipelines
- Pass AWS Certified Data Engineer Associate (DEA-C01)
- Land AWS Data Engineer roles paying ₹10-25 LPA
Technologies Taught
AWS Glue — serverless ETL + Data CatalogAmazon EMR — managed Spark/Hadoop clustersAmazon Redshift — cloud data warehousingAmazon S3 + Lake Formation — lakehouse foundationAmazon Athena — serverless SQL on S3Amazon Kinesis — real-time data streamingAWS Lambda + Step Functions for orchestrationAmazon MWAA — Managed Apache AirflowIceberg + Hudi for open table formatsAWS IAM + KMS for security and encryption
Course Unique Features
- Hands-on labs on real AWS subscriptions — no toy datasets
- Build 5 production-grade pipelines you can show in interviews
- DEA-C01 (AWS Certified Data Engineer Associate) exam prep included
- Daily 90-minute live sessions with live AWS console walkthroughs
- Cost optimisation patterns — cut bills by 60-80%
- Serverless-first design philosophy taught throughout
- Lakehouse on S3 with Iceberg + Hudi — the new standard
- Mock interviews covering AWS services + system design
- Direct referrals to AWS-first companies actively hiring
- Lifetime access to course material + project templates
Job Opportunities
Top job positions you can apply for after completing this training.
| Job Role | Experience | Salary Range |
|---|---|---|
| 1. Data Engineer (AWS) | Fresher to 2+ Years | ₹5–9 LPA |
| 2. ETL Developer (AWS Glue/Redshift) | Fresher to 3 Years | ₹6–12 LPA |
| 3. Cloud Data Engineer | 2 to 4 Years | ₹8–14 LPA |
| 4. Big Data Engineer (PySpark/Hadoop/Spark) | 2 to 5 Years | ₹10–16 LPA |
| 5. AWS Solutions Associate (Data Focus) | 3 to 5 Years | ₹12–18 LPA |
| 6. Data Warehouse Engineer (Redshift/Snowflake) | 3 to 6 Years | ₹12–20 LPA |
| 7. AWS Data Engineer / Data Consultant | 4 to 7 Years | ₹15–25 LPA |
| 8. Senior Data Engineer / Lead | 6+ Years | ₹20–35 LPA |
You Can Work As
AWS Data EngineerCloud Data Engineer (AWS)Big Data EngineerData Platform EngineerSenior Data Engineer (AWS)AWS Solutions Architect — Data
Upcoming In-Demand Jobs
AWS Lakehouse EngineerAI/ML Data Engineer (AWS)Real-Time Streaming Engineer
Course Curriculum
Python
33 topics
Python
- •What is Python?
- •Why Python for Data Engineering?
- •Installing Python and Setting Up Environment (IDEs, Jupyter, VSCode)
- •Running Python Scripts and Notebooks
- •Basic Syntax and Indentation
- •Variables and Data Types (int, float, str, bool, None Type)
- •Type Casting and `type()` function
- •Arithmetic, Comparison, Logical Operators
- •Membership (`in`, `not in`) and Identity Operators
- •Operator Precedence and Associativity
- •`if`, `elif`, `else` Statements
- •`while` and `for` Loops
- •Loop Control: `break`, `continue`, `pass`
- •List Comprehensions (important for Glue transformations)
- •Defining and Calling Functions
- •Parameters and Return Values
- •Lambda Functions (used heavily in PySpark)
- •`map()`, `filter()`, `reduce()` (from `functools`)
- •Lists, Tuples, Sets, Dictionaries
- •CRUD operations on each data structure
- •Iterating through collections
- •Common built-in functions (`len`, `sum`, `sorted`, `zip`, etc.)
- •String Manipulation and Formatting
- •`split()`, `join()`, slicing, and regex intro (`re` module)
- •Introduction to `datetime` and `time` modules (for partition/date-based transformations)
- •Try-Except Blocks
- •Catching Specific Exceptions
- •`finally` and `else` in error handling
- •Importance in ETL pipeline robustness
- •Classes and Objects
- •Constructors (`__init__`)
- •`self` keyword
- •Simple inheritance and method overriding
Data Warehouse
24 topics
Data Warehouse
- •What is Data Warehousing?
- •OLTP vs OLAP
- •Data Warehouse Architecture (Single-tier, Two-tier, Three-tier)
- •Components of a Data Warehouse
- •ETL vs ELT in Data Warehousing
- •What is Data Modeling?
- •Conceptual, Logical, and Physical Data Models
- •Key Data Modeling Concepts: Entities, Attributes, Relationships
- •Primary Keys, Foreign Keys, and Constraints
- •Normalization & Denormalization
- •Choosing the Right Model for Analytical Workloads
- •Introduction to Dimensional Modeling
- •Fact Tables vs Dimension Tables
- •Star Schema: Concepts & Design
- •Snowflake Schema: When to Use It?
- •Slowly Changing Dimensions (SCD) (Types 0, 1, 2, 3, 4, 6)
- •Handling Hierarchies & Aggregations
- •Overview of ETL & ELT Processes
- •Common ETL Challenges & Solutions
- •Data Quality & Data Governance in ETL
- •Change Data Capture (CDC) Strategies
- •Traditional Data Warehouses vs Cloud Data Warehouses
- •Introduction to Data Lakes & Data Lakehouses
- •Overview of Modern DW Platforms: Snowflake, BigQuery, Redshift, Synapse
PySpark
23 topics
PySpark
- •What is PySpark?
- •PySpark vs Pandas vs Dask
- •PySpark Architecture & Execution Model
- •Setting up PySpark in Google Colab
- •Introduction to SparkSession & DataFrames
- •Reading & Writing Data (CSV, JSON, Parquet, Avro)
- •Understanding Schema Inference & Defining Schemas
- •Basic Transformations: `select()`, `filter()`, `withColumn()`, `drop()`
- •Handling Nulls & Missing Data (`fillna()`, `dropna()`, `replace()`)
- •Column Operations: `cast()`, `alias()`, `when()`, `case()`
- •Working with Date & Time Functions (`current_date()`, `datediff()`, `date_add()`)
- •Grouping & Aggregations (`groupBy()`, `agg()`, `pivot()`)
- •Joins in PySpark (inner, left, right, full)
- •Window Functions (Row Number, Ranking, Lead/Lag, Running Totals)
- •Exploding & Flattening Nested Data (`explode()`, `array()`, `struct()`)
- •Working with UDFs (User-Defined Functions)
- •Broadcasting & Skew Handling
- •Understanding Spark Execution Plan (`explain()`, `cache()`, `persist()`)
- •Catalyst Optimizer & Tungsten Execution
- •Partitioning & Bucketing Strategies
- •Repartitioning & Coalescing
- •Optimizing Shuffle Operations
- •Performance Tuning Parameters (`spark.conf.set()`)
PySpark Assignment Problem
2 topics
PySpark Assignment Problem
- •Statements 1 – Hands-On Coding PySpark Assignment Problem
- •Statements 2 – Hands-On Coding
Amazon Web Services (AWS)
26 topics
Amazon Web Services (AWS)
- •Setting up AWS Account and Configuring IAM Roles & Policies
- •Creating S3 Buckets, Uploading Data, and Configuring Permissions
- •Implementing IAM Best Practices for Secure Data Access
- •Setting Up AWS Glue Crawler to Discover Metadata
- •Creating and Querying AWS Glue Catalog Tables
- •Schema Evolution & Handling Semi-Structured Data (JSON, Parquet)
- •Integrating Glue Catalog with Athena & Redshift Spectrum
- •Writing SQL Queries on S3 Data Using Athena
- •Optimizing Queries with Partitioning & Bucketing
- •Using Iceberg Tables in Athena for Time-Travel Queries
- •Performance Optimization: Query Federation & Compression Techniques
- •Setting Up AWS Glue Job with PySpark
- •Transforming & Cleaning Raw Data Using PySpark in Glue
- •Handling Schema Drift in Glue ETL Pipelines
- •Writing Processed Data to S3, Redshift, and RDS
- •Configuring AWS Glue Job to Ingest Data from REST API
- •Using AWS Lambda to Trigger Glue Jobs on Event Streams
- •Handling Real-Time Data Streams in PySpark
- •Writing Ingested Data to Iceberg Tables in Athena
- •Setting Up an Amazon Redshift Cluster
- •Loading Data from S3 to Redshift Using COPY Command
- •Performance Tuning with Sort & Distribution Keys
- •Running Complex Analytical Queries in Redshift
- •Creating S3, IAM Roles, Glue Jobs, and Redshift Using CloudFormation
- •Automating Data Pipeline Deployment Using CloudFormation Templates
- •Managing Stack Updates & Rollbacks
Athena Assignment & Problem Statements
3 topics
Athena Assignment & Problem Statements
- •Statements 1 – Hands-On Coding Redshift Assignment Problem
- •Statements 2 – Hands-On Coding Glue PySpark Assignment Problem
- •Statements 3 – Hands-On Coding
Course Instructed By
MA
Mr. Anuj S---
A Data Engineering professional with over 11+ Yrs of experience in building scalable data pipelines, distributed systems & cloud-native architectures. Has extensive expertise in Apache Spark, Hadoop, Hive, Kafka, and programming with Python, Java, SQL. Anuj brings real-world project knowledge into the classroom, helping learners master modern data engineering practices including streaming ETL, Data Lakehouse design, and ML pipeline integration. Approved trainer by Raj Cloud Technologies.
Approved trainer by Raj Cloud Technologies
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