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Mastering Data Engineering; A Step-by-Step Guide to Building Scalable Data Pipelines

Mastering Data Engineering; A Step-by-Step Guide to Building Scalable Data Pipelines

$199.00
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What does the Data Engineering course cover?

Data Engineering is covered here in 8 modules: Introduction to Data Engineering, Data Pipeline Fundamentals, Data Ingestion and Processing and 5 more. The outline lists 24 specific topics, opening with what is Data Engineering? : An introduction to the field of data engineering and its importance in modern data management.

How do you approach Data Engineering step by step?

The work is sequenced in 8 stages. It starts with Introduction to Data Engineering, moves through Data Pipeline Fundamentals and Data Ingestion and Processing, and ends at Conclusion and Next Steps. Each stage carries its own topic list, so the sequence is followed rather than summarised.

What is in Module 1 of the Data Engineering course?

Module 1 is Introduction to Data Engineering. It works through what is Data Engineering? : An introduction to the field of data engineering and its importance in modern data management., data Engineering vs. Data Science : A comparison of data engineering and data science, including their roles and responsibilities.

How is the Data Engineering course delivered?

The Data Engineering course is fully self-paced with immediate online access after enrolment. Access does not expire and future updates are included at no cost. It can be taken on any device, and a certificate of completion is issued by The Art of Service when you finish.

How much does the Data Engineering course cost?

The Data Engineering course is $199 as a one time payment. There is no subscription, no per seat licence and no hidden fee. Enrolment carries a 30 day satisfied or refunded guarantee, so it can be assessed in full before you commit.

Closely related courses: Kafka Mastery, Building Scalable Marketing Data Pipelines in enterprise, DataOps, Building Scalable Data Pipelines with Cloud Platforms.

More answers: what you get with every course, refund policy, all help answers.

Mastering Data Engineering: A Step-by-Step Guide to Building Scalable Data Pipelines



Course Overview

This comprehensive course is designed to equip you with the skills and knowledge needed to build scalable data pipelines. With a focus on practical, real-world applications, you'll learn the fundamentals of data engineering and how to apply them in a variety of contexts.



Course Features

  • Interactive and Engaging: Our course is designed to keep you engaged and motivated, with interactive lessons and hands-on projects.
  • Comprehensive and Personalized: Our course covers all aspects of data engineering, with personalized feedback and support to help you succeed.
  • Up-to-date and Practical: Our course is constantly updated to reflect the latest developments in data engineering, with a focus on practical, real-world applications.
  • High-quality Content and Expert Instructors: Our course features high-quality content and expert instructors with years of experience in data engineering.
  • Certification: Upon completion of the course, you'll receive a certificate issued by The Art of Service.
  • Flexible Learning and User-friendly: Our course is designed to be flexible and user-friendly, with bite-sized lessons and lifetime access.
  • Mobile-accessible and Community-driven: Our course is mobile-accessible, with a community-driven approach that allows you to connect with other learners and instructors.
  • Actionable Insights and Hands-on Projects: Our course provides actionable insights and hands-on projects to help you apply your knowledge in real-world contexts.
  • Gamification and Progress Tracking: Our course features gamification and progress tracking to help you stay motivated and engaged.


Course Outline

Module 1: Introduction to Data Engineering

  • What is Data Engineering?: An introduction to the field of data engineering and its importance in modern data management.
  • Data Engineering vs. Data Science: A comparison of data engineering and data science, including their roles and responsibilities.
  • Data Engineering Tools and Technologies: An overview of common data engineering tools and technologies, including Apache Beam, Apache Spark, and Apache Hadoop.

Module 2: Data Pipeline Fundamentals

  • What is a Data Pipeline?: A definition of a data pipeline and its components, including data sources, transformations, and sinks.
  • Data Pipeline Architecture: An overview of data pipeline architecture, including batch and real-time processing.
  • Data Pipeline Design Patterns: A discussion of common data pipeline design patterns, including ETL and ELT.

Module 3: Data Ingestion and Processing

  • Data Ingestion: A discussion of data ingestion techniques, including file-based and message-based ingestion.
  • Data Processing: An overview of data processing techniques, including batch and real-time processing.
  • Data Transformation: A discussion of data transformation techniques, including data mapping and data aggregation.

Module 4: Data Storage and Management

  • Data Storage Options: A discussion of data storage options, including relational databases, NoSQL databases, and data warehouses.
  • Data Management: An overview of data management techniques, including data governance and data quality.
  • Data Security: A discussion of data security techniques, including encryption and access control.

Module 5: Data Pipeline Orchestration

  • Data Pipeline Orchestration: A discussion of data pipeline orchestration techniques, including workflow management and scheduling.
  • Data Pipeline Monitoring: An overview of data pipeline monitoring techniques, including logging and metrics collection.
  • Data Pipeline Troubleshooting: A discussion of data pipeline troubleshooting techniques, including error handling and debugging.

Module 6: Scalability and Performance

  • Scalability: A discussion of scalability techniques, including horizontal scaling and vertical scaling.
  • Performance Optimization: An overview of performance optimization techniques, including caching and parallel processing.
  • Benchmarking and Testing: A discussion of benchmarking and testing techniques, including load testing and stress testing.

Module 7: Case Studies and Real-World Applications

  • Case Study 1: Building a Real-Time Data Pipeline: A case study of building a real-time data pipeline using Apache Kafka and Apache Spark.
  • Case Study 2: Building a Batch Data Pipeline: A case study of building a batch data pipeline using Apache Hadoop and Apache Pig.
  • Real-World Applications: A discussion of real-world applications of data pipelines, including IoT data processing and financial data analysis.

Module 8: Conclusion and Next Steps

  • Conclusion: A summary of the course and its key takeaways.
  • Next Steps: A discussion of next steps, including further learning and career development.
  • Certificate of Completion: Upon completion of the course, you'll receive a certificate issued by The Art of Service.
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