Cambridge
Academy
In London

Introduction:

 

This course on data management, security, and warehousing is tailored for professionals and companies aiming to harness the potential of Big Data effectively. With the emergence of the Big Data phenomenon, businesses increasingly rely on robust Data Analytics and Data Science to address challenges, innovate their operations, and enhance customer service while optimizing costs and streamlining processes. The widespread adoption of these practices has led to the term "Industrial Revolution 4.0" becoming commonplace. 

Managing Big Data presents the challenge of efficient data management, which goes beyond mere data acquisition. Data Management involves acquiring, validating, storing, protecting, and processing data to ensure its accessibility, reliability, and timeliness for users. 

This course focuses on the crucial aspect of data warehousing, where a multitude of solutions exists. Selecting the right approach poses a significant challenge for companies in the era of Big Data.

Course Objectives

By the end of this course, participants will be able to:

·        Plan the steps in a data warehousing project effectively.

·        Understand the escalating need for strategic information.

·        Grasp the fundamental concepts of data management and warehousing.

·        Implement strategies for data security.

·        Utilize appropriate methods and tactics in the Big Data era.

Target Audience

This course is designed for:

·        Systems Analysts

·        Programmers

·        Data Analysts

·        Database Administrators

·        Project Leaders

·        Software Engineers

·        Managers

·        Professionals involved in Data Analytics

Course Outlines

Day  1: Agile Enterprise Data Warehousing:

·        Agile Manifesto and its principles.

·        The Scrum Method.

·        Extreme Programming Approach.

·        Lean Software Development.

·        Sources for Data Warehousing Standards.

Day  2: Data Security Strategies:

·        Trustworthiness assessment of data.

·        ISO/IEC 17728 Standard.

·        EU General Data Protection Regulation (GDPR).

·        Protecting the Data Warehouse.

·        Dataset Lifecycle.

Day 3: Data Warehouse: The Building Blocks:

·        Data Warehouse features and characteristics.

·        Data Warehouses and Data Marts.

·        Overview of Data Warehouse Components.

·        Dimensional Analysis of Data.

·        Requirements driving Data Warehousing.

Day  4: Data Warehouse: Architecture and Infrastructure:

·        Hardware and Operating Systems.

·        Database Software.

·        Automation of Warehousing Tasks.

·        Data Warehouse Architecture.

·        Business Conceptual, Logical, and Physical Data Models.

Day  5: Data Management, Security, and Warehousing Implementation:

·        Data Extraction, Transformation, and Loading (ETL).

·        Data Design and Preparation - Data Dimensional Modeling.

·        Key Elements of Data Quality.

·        User Information Matching.

·        On-Line Analytical Processing (OLAP).

·        Big Data Processing in Cloud Environments.