Description
Description:
You’ll begin with SQL and Big Data fundamentals before exploring modern data technologies like Apache Hive, Confluent Kafka, MongoDB, Cassandra, Apache Spark (PySpark), and Databricks. You’ll then master workflow orchestration with Apache Airflow, and deep dive into data warehousing platforms like Snowflake and BigQuery.
The course also covers AWS Cloud integration for data engineering pipelines and offers industrial projects to apply everything you learn in a practical setting. Additionally, you’ll gain career development skills including resume building, LinkedIn optimization, and interview strategies to boost your job prospects.
✅ What You Will Learn
📊 Data Foundations
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SQL for Data Engineering
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Big Data Fundamentals & Hadoop Architecture
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Apache Hive for Data Processing
⚡ Streaming & NoSQL Databases
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Confluent Kafka for Real-Time Streaming
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MongoDB (NoSQL Database) Basics & Applications
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Cassandra for Distributed Data Management
🔥 Big Data Processing
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Apache Spark (PySpark) for Large-Scale Data Processing
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Databricks for Collaborative Data Workflows
🛠 Data Orchestration & Warehousing
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Apache Airflow for Pipeline Scheduling & Automation
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Data Warehousing Fundamentals
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Snowflake for Cloud Data Storage & Analytics
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Google BigQuery for Scalable Query Processing
☁ AWS Cloud for Data Engineering
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AWS Cloud Fundamentals for Data Projects
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Integrating AWS with Data Engineering Pipelines
🧪 Hands-On Industrial Projects
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End-to-End Data Engineering Projects (Multiple Real-World Scenarios)
🚀 Career Development
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Resume Writing for Data Engineering Roles
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LinkedIn Profile Optimization
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Interview Strategies for Data Engineering Positions
👤 Who Is This Course For?
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Beginners wanting to enter the data engineering field
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Data analysts and developers wanting to transition into data engineering
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Professionals looking to master AWS data workflows
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Students preparing for data engineering interviews
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Anyone interested in end-to-end data engineering projects
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