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Data Engineering Courses
Build in-demand data engineering skills, from Python and database fundamentals through ETL pipelines, big data technologies like Hadoop and Spark, real-time streaming with Kafka, and scalable, secure data architecture.
Course curriculum
Four levels, a clear path through
Every learner starts where they are. Move from Beginners through to Expert at your own pace.
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Introduction to Data Engineering
- Overview of Data Engineering and its role in the data ecosystem
- Fundamental concepts in data engineering
- Importance of data engineering in various industries
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Basics of Programming for Data Engineering
- Introduction to a programming language (e.g., Python) for data engineering
- Working with data structures and algorithms
- Writing basic scripts for data manipulation
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Introduction to Databases and Data Storage
- Understanding databases and their types
- Basics of SQL and relational databases
- Overview of NoSQL databases
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Data Processing and Transformation
- Extract, Transform, Load (ETL) processes
- Data pipeline architecture
- Hands-on experience with data processing tools
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Database Management for Data Engineers
- Advanced SQL for data manipulation
- Database optimization techniques
- Data indexing and partitioning
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Data Modeling and Design
- Entity-Relationship Diagrams (ERDs)
- Database normalization
- Schema design for efficient data storage
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Big Data Technologies
- Introduction to big data concepts
- Apache Hadoop and its ecosystem
- Apache Spark for large-scale data processing
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Advanced Data Processing and Optimization
- Parallel processing and distributed computing
- Optimizing data pipelines for performance
- Data caching and in-memory processing
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Real-time Data Streaming
- Understanding real-time data processing
- Streaming frameworks (e.g., Apache Kafka)
- Building real-time data pipelines
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Data Security and Governance
- Principles of data security
- Compliance with data regulations
- Implementing data governance best practices
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Scalable Data Architecture
- Distributed databases and sharding
- Cloud-based data solutions
- Building scalable and resilient data architectures
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Edge Computing and IoT Integration
- Handling data at the edge
- Integration of IoT data in data engineering processes
- Challenges and opportunities in edge computing
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Automation in Data Engineering
- Introduction to automation tools
- Implementing automation in data workflows
- Auto-scaling and self-optimizing data systems
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Data Engineering Project
- Designing and implementing a comprehensive data engineering project
- Integration of various data engineering techniques
- Presentation of the project and its impact
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