Data Engineer
The Data Engineer Training program is designed to provide learners with practical knowledge and skills required to design, build, manage, and optimize modern data infrastructure and data pipelines.
The course covers essential Data Engineering technologies including Python, SQL, Linux, ETL, data warehousing, Apache Spark, Hadoop, cloud platforms, databases, workflow orchestration, and data pipeline development.
Learners will gain hands-on experience in collecting, processing, transforming, storing, and delivering large volumes of data for analytics and business intelligence.
The Data Engineer Training program is designed to provide learners with practical knowledge and skills required to design, build, manage, and optimize modern data infrastructure and data pipelines.
The course covers essential Data Engineering technologies including Python, SQL, Linux, ETL, data warehousing, Apache Spark, Hadoop, cloud platforms, databases, workflow orchestration, and data pipeline development.
Learners will gain hands-on experience in collecting, processing, transforming, storing, and delivering large volumes of data for analytics and business intelligence.
- Data Engineering Fundamentals
Understand the role of Data Engineering and the complete data lifecycle. - Python for Data Engineering
Develop Python skills for data processing, automation, and pipeline development. - Advanced SQL
Query, transform, and analyze data using advanced SQL techniques. - ETL & Data Pipelines
Build reliable pipelines for extracting, transforming, and loading data. - Data Warehousing
Understand data warehouse architecture, dimensional modeling, and analytical databases. - Big Data Technologies
Learn concepts and tools for processing large-scale datasets. - Apache Spark
Process and transform large datasets using Spark and PySpark. - Cloud Data Engineering
Understand modern cloud-based data engineering architectures and services. - Workflow Orchestration
Automate and schedule data workflows using Apache Airflow. - Data Quality & Monitoring
Implement techniques to maintain reliable, accurate, and consistent data pipelines. - Real-World Projects
Apply Data Engineering concepts through practical projects and business use cases.
By the end of this course, learners will be able to:
- Understand Data Engineering concepts and the data lifecycle.
- Develop data processing applications using Python.
- Write advanced SQL queries for data engineering tasks.
- Extract, transform, and load data from different sources.
- Build automated and scalable data pipelines.
- Design and implement data warehouses.
- Understand dimensional data modeling.
- Work with structured and unstructured data.
- Process large datasets using Apache Spark and PySpark.
- Understand Big Data technologies and distributed processing.
- Automate data workflows using Apache Airflow.
- Work with cloud-based data engineering platforms.
- Implement data quality and validation processes.
- Monitor and troubleshoot data pipelines.
- Build end-to-end Data Engineering projects.
Learners are recommended to have:
- Basic computer knowledge.
- Basic programming knowledge.
- Basic understanding of databases.
- Familiarity with SQL is helpful.
- Basic knowledge of Python is recommended.
- Basic understanding of cloud computing is beneficial but not mandatory.
- Introduction to Data Engineering
- What is Data Engineering?
- Role of a Data Engineer
- Data Engineering Lifecycle
- Data Collection
- Data Processing
- Data Storage
- Data Transformation
- Data Analytics
- Data Engineering vs Data Analytics
- Data Engineering vs Data Science
- Modern Data Engineering Architecture
- Linux for Data Engineers
- Introduction to Linux
- Linux File System
- Linux Commands
- File & Directory Management
- User & Permission Management
- Process Management
- Package Management
- Environment Variables
- Shell Scripting
- Linux Automation
- Working with Servers
- Python for Data Engineering
- Introduction to Python
- Variables & Data Types
- Operators
- Conditional Statements
- Loops
- Functions
- Lists, Tuples & Dictionaries
- Sets
- File Handling
- Exception Handling
- Modules & Packages
- Object-Oriented Programming Basics
- Working with JSON
- Working with APIs
- Python Automation
- SQL for Data Engineering
- Introduction to Databases
- Relational Database Concepts
- Tables & Relationships
- Primary Keys & Foreign Keys
- SELECT Statements
- Filtering & Sorting
- Aggregate Functions
- GROUP BY & HAVING
- SQL Joins
- Subqueries
- CASE Statements
- Common Table Expressions (CTEs)
- Views
- Stored Procedures
- Window Functions
- Indexing
- Query Optimization
- Advanced SQL for Data Engineering
- Database Technologies
- Introduction to Database Systems
- Relational Databases
- MySQL
- PostgreSQL
- Database Design
- Normalization
- Transactions
- Database Performance
- NoSQL Database Concepts
- Introduction to MongoDB
- SQL vs NoSQL
- Data Extraction & Ingestion
- Introduction to Data Ingestion
- Batch Data Processing
- Real-Time Data Processing
- File-Based Data Ingestion
- Database Data Ingestion
- API-Based Data Ingestion
- JSON & CSV Data
- Data Ingestion from Cloud Storage
- Data Validation
- Data Ingestion Best Practices
- ETL & Data Pipelines
- Introduction to ETL
- ETL vs ELT
- Extracting Data
- Data Transformation
- Data Loading
- Batch Processing
- Incremental Data Loading
- Full Load vs Incremental Load
- Data Pipeline Architecture
- Pipeline Automation
- Error Handling
- Logging & Monitoring
- Data Warehousing
- Introduction to Data Warehousing
- Data Warehouse Architecture
- OLTP vs OLAP
- Fact Tables
- Dimension Tables
- Star Schema
- Snowflake Schema
- Slowly Changing Dimensions (SCD)
- Data Warehouse Design
- Data Mart
- Data Lake
- Data Lakehouse
- Modern Data Warehouse Architecture
- Data Modeling
- Introduction to Data Modeling
- Conceptual Data Models
- Logical Data Models
- Physical Data Models
- Entity Relationship Modeling
- Dimensional Modeling
- Fact & Dimension Tables
- Primary & Foreign Keys
- Data Relationships
- Schema Design
- Data Model Optimization
- Apache Hadoop & Big Data
- Introduction to Big Data
- Big Data Characteristics
- Hadoop Overview
- Hadoop Architecture
- HDFS
- Hadoop Ecosystem
- Distributed Storage
- Distributed Processing
- MapReduce Concepts
- Big Data Use Cases
- Apache Spark
- Introduction to Apache Spark
- Spark Architecture
- Spark Components
- Spark Installation
- Spark DataFrames
- Spark SQL
- RDD Concepts
- Transformations
- Actions
- Data Processing with Spark
- Data Aggregation
- Joins in Spark
- Performance Optimization
- Handling Large Datasets
- PySpark
- Introduction to PySpark
- PySpark DataFrames
- Reading Data
- Writing Data
- Data Cleaning
- Data Transformation
- Filtering
- GroupBy
- Aggregations
- Joins
- Window Functions
- PySpark SQL
- Handling Missing Data
- Working with Large Datasets
- PySpark Project Implementation
- Apache Airflow
- Introduction to Workflow Orchestration
- Apache Airflow Architecture
- DAGs
- Tasks
- Operators
- Scheduling
- Dependencies
- Sensors
- Variables
- Connections
- Workflow Automation
- Monitoring DAGs
- Error Handling
- Building Automated Data Pipelines
- Data Processing & Transformation
- Data Cleaning
- Data Standardization
- Data Validation
- Missing Data Handling
- Duplicate Data Removal
- Data Type Conversion
- Data Aggregation
- Data Transformation
- Data Quality Checks
- Data Enrichment
- Data Processing Best Practices
- Cloud Data Engineering
AWS
- AWS Data Engineering Overview
- Amazon S3
- Amazon EC2
- AWS IAM
- AWS Glue
- Amazon Redshift
- Amazon RDS
- AWS Lambda
- Amazon Athena
- Cloud Data Pipeline Architecture
Microsoft Azure
- Azure Data Engineering Overview
- Azure Storage
- Azure Data Lake Storage
- Azure Data Factory
- Azure Synapse Analytics
- Azure SQL Database
- Azure Databricks
- Azure Data Pipeline Architecture
Google Cloud Platform
- GCP Data Engineering Overview
- Google Cloud Storage
- BigQuery
- Cloud SQL
- Dataflow
- Dataproc
- Pub/Sub
- GCP Data Pipeline Architecture
- Data Lake & Lakehouse
- Introduction to Data Lakes
- Data Lake Architecture
- Data Lake vs Data Warehouse
- Data Lake Storage
- Data Lake Zones
- Data Ingestion
- Data Processing
- Data Governance
- Lakehouse Architecture
- Introduction to Delta Lake
- Real-Time Data Engineering
- Batch vs Streaming
- Real-Time Data Processing
- Streaming Architecture
- Apache Kafka Overview
- Kafka Producers
- Kafka Consumers
- Topics & Partitions
- Message Processing
- Real-Time Data Pipelines
- Streaming Data Use Cases
- Data Quality & Governance
- Introduction to Data Quality
- Data Validation
- Data Accuracy
- Data Consistency
- Data Completeness
- Data Lineage
- Metadata Management
- Data Governance
- Data Security
- Access Management
- Data Privacy Fundamentals
- DevOps for Data Engineering
- Introduction to DataOps
- Git & Version Control
- GitHub/GitLab
- Code Repository Management
- CI/CD Fundamentals
- Automated Data Pipeline Deployment
- Environment Management
- Docker Basics
- Containerizing Data Applications
- Monitoring & Logging
- Data Pipeline Monitoring
- Pipeline Monitoring
- Logging
- Error Handling
- Alerts & Notifications
- Pipeline Performance
- Data Quality Monitoring
- Failure Recovery
- Troubleshooting
- Pipeline Optimization
- Real-World Projects
Learners will work on practical Data Engineering projects such as:
Sales Data Pipeline Project
- Data Extraction
- Data Cleaning
- Data Transformation
- Data Warehouse Design
- Automated Data Pipeline
- Business Reporting Data Preparation
Customer Data Engineering Project
- Customer Data Ingestion
- Data Cleaning
- Customer Data Transformation
- Data Modeling
- Data Warehouse Loading
- Automated Pipeline Development
E-Commerce Data Pipeline
- Order Data Processing
- Customer Data Processing
- Product Data Processing
- ETL Pipeline
- Data Warehouse
- Analytics-Ready Dataset
Real-Time Data Pipeline
- Streaming Data Ingestion
- Kafka Integration
- Real-Time Processing
- Spark Processing
- Data Storage
- Pipeline Monitoring
- Capstone Project
Learners will complete an end-to-end Data Engineering Capstone Project covering:
- Business Requirement Analysis
- Data Source Identification
- Data Extraction
- Data Ingestion
- Data Cleaning
- Data Transformation
- Data Modeling
- Data Warehouse Development
- ETL/ELT Pipeline Development
- Apache Spark Processing
- Airflow Workflow Automation
- Cloud Integration
- Data Quality Checks
- Pipeline Monitoring
- Documentation
Final Project Presentation
After completing the course, learners can explore roles such as:
- Data Engineer
- Junior Data Engineer
- Cloud Data Engineer
- Big Data Engineer
- ETL Developer
- Data Pipeline Engineer
- Data Warehouse Developer
- Analytics Engineer
- PySpark Developer
- Data Platform Engineer
- DataOps Engineer
- AWS Data Engineer
- Azure Data Engineer
- GCP Data Engineer
Who Can Join This Course?
This course is suitable for:
- Students & Fresh Graduates
- Software Developers
- IT Professionals
- Database Professionals
- SQL Developers
- Data Analysts
- Business Intelligence Professionals
- Cloud Professionals
- System Administrators
- Data Science Aspirants
- Professionals looking to transition into Data Engineering
- Working Professionals seeking to upgrade their technical skills
Why Choose Our Data Engineering Training?
- Industry-oriented curriculum
- Hands-on practical training
- Real-world datasets and projects
- Python & Advanced SQL
- ETL/ELT pipeline development
- Data Warehousing & Data Modeling
- Apache Spark & PySpark
- Apache Airflow
- Big Data technologies
- AWS, Azure & GCP concepts
- Cloud Data Engineering
- Real-time data pipeline concepts
- Data Quality & Monitoring
- End-to-end capstone project
- Career-focused learning approach
Job Roles / Industry Positions
- Data Engineer
- Junior Data Engineer
- Cloud Data Engineer
- Big Data Engineer
- ETL Developer
- Data Pipeline Engineer
- Data Warehouse Developer
- Data Warehouse Engineer
- Analytics Engineer
- PySpark Developer
- Data Platform Engineer
- DataOps Engineer
- Database Engineer
- AWS Data Engineer
- Azure Data Engineer
- GCP Data Engineer
- Big Data Developer
- ETL Engineer
- Data Integration Engineer
- Data Infrastructure Engineer
