The Data Engineering course is a comprehensive training program designed to prepare learners for the dynamic field of data management and processing. This course focuses on building scalable data pipelines, mastering data storage and transformation techniques, and leveraging modern tools to support analytics and machine learning initiatives. It is ideal for aspiring data engineers, developers, and IT professionals.

Course Highlights

• Duration: 80 hours (Weekday/Weekend options available)
• Mode: Online/Offline/Hybrid
• Prerequisites: Familiarity with programming (Python or Java preferred) and basic knowledge of databases and SQL.

Key Learning Outcomes

• Understand the fundamentals of data engineering and its role in the data ecosystem.
• Design and implement scalable data pipelines for batch and real-time processing.
• Work with modern databases, data warehouses, and data lakes.
• Gain expertise in ETL (Extract, Transform, Load) processes and tools.
• Use cloud-based data engineering tools and frameworks for storage and processing.

Modules Covered

1. Introduction to Data Engineering

   • Overview of the data engineering lifecycle
   • Key tools, technologies, and methodologies

2. Programming for Data Engineering

   • Python for data processing (Pandas, NumPy)
   • Java/Scala basics for working with big data frameworks

3. Database Essentials

   • Relational databases: SQL, normalization, and indexing
   • NoSQL databases: MongoDB, Cassandra

4. Data Warehousing and Data Lakes

   • Data modeling and schema design
   • Working with tools like Amazon Redshift, Snowflake, and Google BigQuery

5. ETL and Data Pipelines

   • ETL tools: Apache NiFi, Talend, and Informatica
   • Building pipelines with Apache Airflow

6. Big Data Processing

   • Hadoop ecosystem and HDFS
   • Apache Spark for distributed data processing

7. Cloud Data Engineering

   • Using AWS, Azure, or Google Cloud for data engineering tasks
   • Serverless data processing with AWS Lambda and Google Cloud Functions

8. Data Streaming and Real-Time Processing

   • Apache Kafka for data streaming
   • Real-time analytics using Spark Streaming and Flink

9. Data Security and Governance

   • Data privacy, encryption, and access controls
   • Best practices for compliance and governance

10. Capstone Projects

• Designing and implementing end-to-end data pipelines
• Real-world use cases such as recommendation systems, log analysis, and fraud detection

Certification and Placement Assistance

• Certification Prep: Earn a course completion certificate recognized in the data engineering domain.
• Placement Assistance: Job-ready resume building, mock interviews, and access to hiring partners.

Who Should Attend?

• IT professionals transitioning to data engineering roles.
• Developers and database administrators looking to expand their skill sets.
• Students and graduates aiming for careers in data management and analytics.

Why Choose Us?

• Training from experienced data engineers.
• Hands-on projects and case studies with real-world relevance.
• Lifetime access to materials, recorded sessions, and a thriving alumni network.