Data Engineering Course: Build Robust Data Pipelines
Become a skilled Data Engineer with BinnBash Academy's comprehensive course. Master SQL, Python, Big Data technologies (Spark, Hadoop), ETL processes, Data Warehousing, Data Lakes, and Cloud platforms (AWS, Azure, GCP). Design, build, and manage scalable data infrastructure!
Engineer Your Data Future!Who Should Enroll in this Data Engineering Course?
This course is ideal for individuals looking to build a career in data infrastructure and big data solutions:
- Aspiring Data Engineers, ETL Developers, and Big Data Engineers.
- Software Developers interested in data architecture and pipeline development.
- Data Analysts or Scientists looking to deepen their understanding of data infrastructure.
- Database Administrators who want to expand into modern data platforms.
- Anyone with a strong analytical and problem-solving mindset interested in data systems.
- Graduates from Computer Science, IT, or related fields.
Data Engineering Course Prerequisites
- Basic understanding of programming concepts (preferably Python or Java).
- Familiarity with relational databases and SQL basics.
- A logical and analytical mindset for problem-solving.
- Basic computer literacy and internet navigation skills.
- Prior experience with data is a plus, but not mandatory.
Key Data Engineering Tools & Technologies Covered
Hands-on practice building scalable, reliable, and efficient data pipelines and infrastructure.
Data Engineering: Comprehensive Syllabus & Practical Contents
Module 1: Data Engineering Fundamentals & SQL
- Introduction to Data Engineering & Data Lifecycle.
- Role of a Data Engineer.
- Relational Databases & Advanced SQL for Data Engineering.
- Database Optimization & Indexing.
- Data Modeling (Dimensional Modeling, Star/Snowflake Schema).
- Lab: Design and query complex databases, optimize SQL queries.
Tools & Concepts:
- SQL, Data Modeling, Database Optimization.
Expected Outcomes:
- Understand DE fundamentals.
- Master advanced SQL.
- Design efficient databases.
Module 2: Python for Data Engineering
- Advanced Python for Data Manipulation (Pandas, NumPy).
- File Handling (CSV, JSON, Parquet).
- Object-Oriented Programming (OOP) in Python.
- Error Handling & Logging.
- Introduction to PySpark for Distributed Computing.
- Lab: Build Python scripts for data processing and automation.
Tools & Concepts:
- Python, Pandas, PySpark.
Expected Outcomes:
- Write robust Python code.
- Process various data formats.
- Understand distributed processing.
Module 3: ETL/ELT & Data Warehousing/Data Lakes
- Understanding ETL vs. ELT Processes.
- Data Ingestion Techniques.
- Data Transformation & Cleansing.
- Data Warehousing Concepts (OLAP, OLTP).
- Building Data Warehouses (e.g., using Snowflake/Redshift concepts).
- Data Lake Architecture & Use Cases.
- Lab: Design an ETL pipeline, implement data transformations.
Tools & Concepts:
- ETL/ELT, Data Warehousing, Data Lakes.
Expected Outcomes:
- Design data ingestion strategies.
- Build data transformation logic.
- Understand data storage architectures.
Module 4: Big Data Technologies (Hadoop & Spark)
- Introduction to Big Data & Hadoop Ecosystem.
- HDFS (Hadoop Distributed File System).
- MapReduce Concepts.
- Apache Spark: Core Concepts, Spark SQL, Spark Streaming.
- Working with Spark DataFrames.
- Real-time Data Processing with Kafka (basics).
- Lab: Process large datasets using Spark, set up a basic Kafka producer/consumer.
Tools & Concepts:
- Hadoop, Spark, Kafka.
Expected Outcomes:
- Process big data.
- Utilize Spark for analytics.
- Understand streaming data.
Module 5: Cloud Data Platforms (AWS/Azure/GCP)
- Introduction to Cloud Computing for Data.
- AWS Data Services (S3, Glue, Redshift, EMR basics).
- Azure Data Services (Blob Storage, Data Factory, Synapse Analytics basics).
- Google Cloud Data Services (Cloud Storage, Dataflow, BigQuery basics).
- Building Cloud-based Data Pipelines.
- Data Security & Governance in Cloud.
- Lab: Deploy a simple data pipeline on a chosen cloud platform.
Tools & Concepts:
- AWS, Azure, GCP (data services).
Expected Outcomes:
- Work with cloud data services.
- Build cloud data pipelines.
- Understand data security in cloud.
Module 6: Data Orchestration, Monitoring & Career
- Introduction to Workflow Orchestration (Airflow concepts).
- Data Quality & Testing.
- Monitoring & Alerting for Data Pipelines.
- Data Governance & Compliance.
- Building a Professional Data Engineering Portfolio.
- Career Guidance: Resume Building, LinkedIn Optimization, Mock Interviews for Data Engineer roles.
- Final Project: Design, build, and deploy an end-to-end data pipeline solution.
Tools & Concepts:
- Airflow, Data Quality.
- Portfolio Building, Career Prep.
Expected Outcomes:
- Orchestrate data workflows.
- Ensure data quality.
- Secure a Data Engineer job.
This course provides hands-on expertise to make you a proficient and job-ready Data Engineer!
Data Engineer Roles and Responsibilities in Real-Time Scenarios & Live Projects
Gain hands-on experience by working on live projects, understanding the real-time responsibilities of a Data Engineer in leading global companies. Our curriculum is designed to align with industry best practices and scalable data architecture.
Data Pipeline Development
Design, build, and maintain scalable and robust ETL/ELT pipelines for ingesting, transforming, and loading data from various sources into data warehouses or data lakes, as done at Google.
Database & Data Storage Management
Manage and optimize relational and NoSQL databases, data warehouses (e.g., Snowflake, Redshift), and data lakes (S3, ADLS) for efficient data storage and retrieval, similar to work at Microsoft.
Programming for Data
Write efficient and maintainable code in Python, SQL, and PySpark to automate data processes, build data quality checks, and develop custom data solutions, common at Amazon.
Cloud Data Platform Utilization
Utilize cloud data services from platforms like AWS, Azure, or GCP to build and deploy data solutions, leveraging services for storage, compute, and orchestration.
Big Data Technologies Implementation
Work with Big Data frameworks such as Apache Spark and Hadoop to process and analyze massive datasets, ensuring high performance and scalability.
Data Architecture Design
Collaborate with data architects and data scientists to design optimal data models, schemas, and overall data infrastructure that supports analytics, machine learning, and business intelligence needs.
Monitoring & Optimization
Implement monitoring tools and practices to ensure the health, performance, and reliability of data pipelines and systems, proactively identifying and resolving data issues.
Data Governance & Security
Ensure data quality, security, and compliance with industry regulations by implementing robust data governance policies and access controls within data platforms.
Our Alumni Works Here!
Ankit Sharma
Data Engineer
Pooja Singh
ETL Developer
Rahul Gupta
Cloud Data Engineer
Sneha Reddy
Big Data Engineer
Vikram Joshi
Data Pipeline Eng.
Divya Kumar
Data Platform Eng.
Karan Desai
Associate DE
Meena Patel
Data Engineer Intern
Siddharth Rao
Data Architect (Junior)
Neha Sharma
Data Warehouse Spec.
Ankit Sharma
Data Engineer
Pooja Singh
ETL Developer
Rahul Gupta
Cloud Data Engineer
Sneha Reddy
Big Data Engineer
Vikram Joshi
Data Pipeline Eng.
Divya Kumar
Data Platform Eng.
Karan Desai
Associate DE
Meena Patel
Data Engineer Intern
Siddharth Rao
Data Architect (Junior)
Neha Sharma
Data Warehouse Spec.
What Our Data Engineering Students Say
"This course transformed my understanding of data infrastructure. Building ETL pipelines with Python and SQL was incredibly practical."
"Learning Big Data technologies like Spark and Hadoop was challenging but rewarding. I now feel confident handling massive datasets."
"The cloud data services module was excellent. I gained hands-on experience with AWS, which is crucial in today's job market."
"BinnBash Academy's focus on real-time projects and industry best practices prepared me perfectly for my role as a Big Data Engineer."
"The instructors are highly experienced and supportive. They made complex topics like data warehousing and data lakes easy to grasp."
"I appreciated the emphasis on building a strong portfolio. My live projects from the course were key to landing my first job."
"This course is comprehensive and covers all essential aspects of data engineering, from data modeling to pipeline orchestration."
"Even as an intern, I was able to contribute meaningfully to data projects thanks to the solid foundation provided by this course."
"Learning about data governance and security was crucial. It's not just about moving data, but moving it responsibly."
"The practical approach to learning, combined with industry-relevant tools, made this course stand out from others."
"This course transformed my understanding of data infrastructure. Building ETL pipelines with Python and SQL was incredibly practical."
"Learning Big Data technologies like Spark and Hadoop was challenging but rewarding. I now feel confident handling massive datasets."
"The cloud data services module was excellent. I gained hands-on experience with AWS, which is crucial in today's job market."
"BinnBash Academy's focus on real-time projects and industry best practices prepared me perfectly for my role as a Big Data Engineer."
"The instructors are highly experienced and supportive. They made complex topics like data warehousing and data lakes easy to grasp."
"I appreciated the emphasis on building a strong portfolio. My live projects from the course were key to landing my first job."
"This course is comprehensive and covers all essential aspects of data engineering, from data modeling to pipeline orchestration."
"Even as an intern, I was able to contribute meaningfully to data projects thanks to the solid foundation provided by this course."
"Learning about data governance and security was crucial. It's not just about moving data, but moving it responsibly."
"The practical approach to learning, combined with industry-relevant tools, made this course stand out from others."
Data Engineer Job Roles After This Course
Data Engineer
ETL Developer
Cloud Data Engineer
Big Data Engineer
Data Pipeline Engineer
Data Warehouse Engineer
Data Platform Engineer
Associate Data Engineer