Course Description
Cloud Data Engineering focuses on designing, building, and managing scalable data infrastructure using cloud platforms. The course covers data pipelines, cloud storage, ETL processes, and big data tools like Spark and Kafka. Students gain hands-on experience with AWS, Azure, or GCP for real-time and batch data processing.
Why should I join?
Cloud Data Engineering course is a smart career investment if you want to become highly skilled in building modern, scalable, and real-time data systems. Cloud Data Engineering course is a smart career investment if you want to become highly skilled in building modern, scalable, and real-time data systems
Course Content
Module 1: Introduction to Tableau
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5 Topics
What is Data Engineering?
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Roles & responsibilities of a Data Engineer
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Overview of cloud platforms: AWS, Azure, GCP
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Core concepts: ETL vs ELT, batch vs streaming, data lakes vs warehouses
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Tools used in cloud data engineering
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Module 2: Cloud Fundamentals
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4 Topics
Cloud computing models (IaaS, PaaS, SaaS)
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Cloud storage basics (object, file, block)
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Compute services (VMs, containers, serverless)
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Networking and security basics
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Module 3: Data Storage Systems in the Cloud
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5 Topics
AWS: S3, RDS, Redshift, DynamoDB
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Azure: Blob Storage, Data Lake Gen2, SQL DB, Cosmos DB
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GCP: Cloud Storage, BigQuery, Firestore
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Data partitioning and clustering
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Data modeling basics
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Module 4: Data Ingestion Tools
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3 Topics
File-based ingestion (CSV, JSON, Parquet)
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Real-time ingestion tools:
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Batch ingestion using:
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Module 5: Data Transformation & Processing
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5 Topics
ETL vs ELT strategies
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Using Apache Spark and PySpark
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Databricks on Azure and AWS
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SQL-based transformation (BigQuery, Snowflake, Redshift)
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Processing data at scale (partitioning, bucketing)
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Module 6: Workflow Orchestration
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3 Topics
Introduction to Apache Airflow
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DAGs, tasks, dependencies
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Cloud-native orchestration:
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Module 7: Real-Time Data Processing
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3 Topics
Concepts: streaming, windowing, watermarking
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Tools:
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Use cases: real-time dashboards, alerting systems
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Module 8: Data Warehousing and Analytics
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4 Topics
Redshift, BigQuery, Snowflake, Synapse
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Star/snowflake schema design
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Writing optimized SQL queries
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BI tool integration (Tableau, Power BI, Looker)
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Module 9: Data Governance, Security & Monitoring
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5 Topics
IAM roles and access control
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Data encryption at rest & in transit
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Logging and monitoring:
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Data quality and validation tools
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Introduction to Data Catalogs and Lineage Tracking (e.g., AWS Glue Data Catalog, Purview, Dataplex)
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Module 10: DevOps and CI/CD for Data Pipelines
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3 Topics
Version control with Git
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CI/CD for data workflows (GitHub Actions, Jenkins, Cloud-native tools)
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Infrastructure as code: Terraform, CloudFormation
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Module 11: Capstone Project
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1 Topic
Build an end-to-end cloud data pipeline:
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Includes
11 Lessons
41 Topics
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