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Course Outline

Foundations of Data Platforms

  • Understanding databases, data platforms, and big data systems.
  • Structured, semi-structured, and unstructured data types.
  • Key business drivers for modern data solutions.
  • Characteristics of big data and essential terminology.

Database Fundamentals

  • Relational database concepts including tables, rows, columns, and keys.
  • Utilising SQL for data retrieval and management.
  • Basic data modelling and simple schema design.
  • Practical aspects of transactions, consistency, and reliability.

Choosing Between Relational and NoSQL Systems

  • Relational databases versus NoSQL databases.
  • High-level overview of document, key-value, column, and graph models.
  • Strengths, limitations, and trade-offs of each approach.
  • Aligning database choices with common business requirements.

Data Warehousing and Big Data Processing

  • Purpose of data warehouses, data lakes, and lakehouse-style architectures.
  • ETL and ELT concepts for data movement and preparation.
  • Batch and stream processing concepts.
  • High-level view of distributed storage and processing.

Governance, Security, and Data Quality

  • Basic governance principles, ownership, and stewardship.
  • Access control, privacy, and security considerations.
  • Common data quality issues and practical improvement methods.
  • Compliance and responsible data use in business environments.

Practical Applications and Course Wrap-Up

  • Typical use cases in reporting, analytics, and operational systems.
  • Reviewing example architectures for different scenarios.
  • Common implementation challenges and strategies to reduce risk.
  • Summary, recommendations, and next steps for further learning.

Requirements

  • A general understanding of data, reports, and common business information flows.
  • Experience using spreadsheets, reports, or business applications that interact with data.
  • Basic technical, analytical, or business systems experience.

Audience

  • Business analysts and reporting professionals.
  • IT staff, developers, and system support personnel.
  • Managers and decision-makers involved in data-related projects.
 14 Hours

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