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Data Vault Mastery: Modernizing Data Warehousing

Modernizing Data Warehousing with Data Vault 2.0 Methodology

     
  • 4.2
  •  |
  • Reviews ( 23 )
₹1999

This Course Includes

  • iconudemy
  • icon4.2 (23 reviews )
  • icon6 total hours
  • iconenglish
  • iconOnline - Self Paced
  • iconcourse
  • iconUdemy

About Data Vault Mastery: Modernizing Data Warehousing

Course Overview:

Data Vault Mastery: Modernizing Data Warehousing for Advanced Analytics is an in-depth and comprehensive training program designed to equip participants with the skills and knowledge required to leverage the power of data vault methodologies in modern data warehousing environments. This course focuses on the latest advancements in data vault 2.0, providing learners with a solid foundation in data modeling, implementation, and management techniques for supporting advanced analytics.

You also have a chance to open knowledge on other data aspects such as: Master Data Management (MDM), Metadata Management, Multidimensional Databases and Data Warehouse Platform, etc

Course Objectives:

Understand the Fundamentals: Participants will grasp the core concepts of data warehousing, data vault methodologies, and the need for modernization in the era of advanced analytics.

Master Data Vault 2.0 Architecture: Learners will explore the architecture of Data Vault 2.0 and understand how it addresses scalability, flexibility, and adaptability for handling dynamic data environments.

Learn Data Vault Modeling: The course delves into Data Vault 2.0 modeling techniques, covering the design of hubs, links, and satellites to capture historical data and manage changes.

Implement Data Vault Load Patterns: Participants will gain hands-on experience in implementing Data Vault 2.0 load patterns for efficiently loading data from various sources into the data warehouse.

Explore Data Vault Physical ETL Load: The course provides insights into the physical implementation of ETL (Extract, Transform, Load) processes for populating the Data Vault.

Understand Data Vault 2.0 Hash Key: Learners will learn about the significance of hash keys in Data Vault 2.0 for enhancing data performance and managing data integrity.

Discover Dimensional Modeling: Participants will be introduced to dimensional modeling techniques, including star schemas and multi-star schemas, to support reporting and analytics.

Master Data Management: The course covers the architecture and development steps of Master Data Management (MDM) to ensure consistent and accurate master data across the organization.

Unveil Metadata Management: Learners will explore different metadata types and understand how to capture and manage metadata for effective data governance.

Dive into Multidimensional Databases: Participants will gain insights into the world of multidimensional databases and how they cater to complex analytical queries.

Explore Data Warehouse Platforms: The course examines the technology landscape of Data Vault 2.0, IBM's Data & Analytics products, and AWS Data & Analytics services

By the end of the "Data Vault Mastery: Modernizing Data Warehousing for Advanced Analytics" course, participants will be well-equipped to design, implement, and manage robust data vault structures to support advanced analytics and derive valuable insights from their data assets.

What You Will Learn?

  • Modernizing Data Warehousing for Advanced Analytics with the powerful methodology of Data Vault 2.0.
  • Scalable Data Vault 2.0 data warehouse architecture.
  • Data Vault 2.0 methodology in discussing project planning & execution.
  • How to Modelling Data Vault 1.0 & 2.0.
  • The real practical of Data Vault 2.0 with Loading Patterns, ETL Load, HashKey.
  • How to design Dimensional Model.
  • Master Data Management from architecture to implementation steps.
  • Meta data management on each data layers and how to capture metadata.
  • What is Multi-dimensional Database (OLAP CUBE).
  • Update Enterprise Data warehouse (DWH) Platform from IBM, AWS and Data Vault 2.0 technology landscape.
  • Hands-On Lab with loading source to datavautl, to datamart and to OLAP CUBE by using SQL Server, SSIS, SSAS.