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Data Engineering Workshop

Master the Art of Data Engineering!

Requirements:
  • Experience with a programming language such as Java, R, or Python
  • Familiarity with the non-statistical aspects of the Data Science and Big Data Analytics v2 content
  • Understanding of the data engineer role provided in the Introduction to Data Engineering

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Who is the course for

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Who is this course for?

The Data Engineering Workshop is designed for individuals who are looking to deepen their understanding of data infrastructure and large-scale data processing. Whether you are a beginner aiming to step into the world of data engineering or a seasoned professional seeking to enhance your skills with the latest tools and techniques, this course provides the necessary framework to build robust data solutions.

This course is particularly beneficial for those involved in handling vast amounts of data and who need to manage data flows and storage efficiently within their organizations. It offers practical, hands-on learning and insights into real-world data challenges, preparing participants to tackle projects of any scale.

Target audience:

  • Aspiring data engineers seeking foundational knowledge in data systems.

  • Software developers interested in transitioning to data engineering roles.

  • Data analysts aiming to scale their data-handling capabilities.

  • IT professionals responsible for managing and analyzing large data sets.

  • Project managers and team leaders overseeing data-driven projects.

  • Academic researchers and students in fields related to data science and engineering.

What will you learn

More information
  • Data warehousing techniques
  • ETL process design
  • Cloud data services
  • Real-time data streaming

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Timeline

Agenda

– Data Warehousing with SQL and NoSQL
  • Provide an overview of data warehouses
  • Explain the purposes of databases and their various types
  • Describe various SQL and NoSQL tools
ETL Offload with Hadoop and Spark
  • Identify business challenges with ETL (Extract-Transform-Load)
  • Explain ELT and ETL processes
  • Describe the Hadoop ecosystem as an ETL offload solution
Data Governance, Security and Privacy for Big Data
  • Describe data governance, roles, and responsibilities
  • Discuss data governance models
  • Describe metadata, metadata types and uses
  • Explain master data, framework, and purpose
  • Explain Hadoop security controls
  • Discuss data governance tools Apache Atlas, Ranger and Knox
  • Describe cloud security consideration
  • Explain GDPR and data ethics
Processing Streaming and IoT Data
  • Describe streaming and IoT data environments
  • Explain Kafka messaging system with examples
  • Explain the key features, architecture and various use cases of stream processing tools such as Storm, Spark Streaming, and Flink
  • Explain various IoT related projects such as Project Nautilus, Pravega, and EdgeX Foundry
Building Data Pipelines with Python
  • Write Python scripts to perform key data processing activities
  • Describe data pipelines and tools
  • Build data pipelines using Python
  • Block length
  • Teaching hours
  • Refreshments No
  • Exam No

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