Home : Book details : Book description

Description of  Data Vault Mastery

2023 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 3.43 GB | Duration: 7h 0m Modernizing Data Warehousing for Advanced What you'll 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 Requirements Basic Knowledge of Data Warehousing: Familiarity with data warehousing concepts, including the purpose and architecture of data warehouses, data modeling, and ETL processes, will be helpful. Database Fundamentals: Understanding of fundamental database concepts, such as tables, relationships, and SQL queries, is beneficial. Business Intelligence and Analytics: Some knowledge of business intelligence tools and analytics concepts can be advantageous for understanding the application of data vault methodology in advanced analytics. Data Modeling: Familiarity with data modeling techniques, such as entity-relationship diagrams and dimensional modeling, can be beneficial for comprehending the concepts taught in the course. Database Management Systems: Basic knowledge of database management systems (e.g., Oracle, SQL Server, etc.) is recommended, as data vault implementation may involve working with different databases. Data Integration: Awareness of data integration processes and tools, such as ETL (Extract, Transform, Load), is helpful for understanding data vault load patterns. Description Overview Section 1: Introduction Lecture 1 Introduction Lecture 2 Course Outline and Key Learning Outcomes Lecture 3 Get the Matterials Section 2: Data warehouse Introduction Lecture 4 Enterprise data warehouse environment Lecture 5 Introduction to Data Vault Lecture 6 Data warehouse architecture Section 3: Flexible & scalable data warehouse architecture Lecture 7 Struggling of data warehouse with changes Lecture 8 Data vault 2.0 architecture Lecture 9 Business rules application Lecture 10 Staging area layer Lecture 11 Data warehouse layer Lecture 12 Information mart layer Lecture 13 Extension of data vault 2.0 architecture >> Metrics Vault Lecture 14 Business Vault Lecture 15 Operational Vault Section 4: The data vault 2.0 methodology Lecture 16 Project planning Lecture 17 Project planning >> Roles & Duties Lecture 18 Project planning >> Communication Lecture 19 Project planning >> CMMI maturity model Lecture 20 Project planning >> SCRUM Lecture 21 Project planning >> Estimation of the project Lecture 22 Project execution Lecture 23 Project execution >> Implementation steps under agile - Scrum methodology Section 5: The data vault modelling Lecture 24 The data vault modelling Lecture 25 Data vault 1.0 use case, requirement, database diagram & table structure Lecture 26 Data vault 1.0 modelling Lecture 28 Data vault 2.0 definition Lecture 29 Data vault 2.0 application >> hub application Lecture 35 Satellite application >> Overloaded Satellites Lecture 36 Satellite application >> Multi-active Satellites Lecture 37 Satellite application >> Status tracking Satellites Lecture 38 Satellite application >> Effectively Satellites Lecture 39 Satellite application >> Computed Satellites Lecture 40 Advanced data vault modeling >> Point-In-Time tables Lecture 41 Advanced data vault modeling >> Bridge tables Lecture 42 Data vault 2.0 flexibility Section 6: The data vault implementation Lecture 43 Data vault 2.0 introduction & use case implementation Lecture 45 Data vault 2.0 load patterns >> hash key & parallel Section 7: Dimensional modeling Lecture 46 Dimensional modeling: star schemas, multi-dimension schemas, dimension design Section 8: Master data management - MDM Lecture 47 Master data management: MDM architecture & implementation steps Section 9: Meta data management Lecture 48 Meta data type Lecture 49 Metadata capturing >> Source system Lecture 50 Metadata capturing >> Staging Lecture 51 Metadata capturing >> Metadata for loading hub entities Lecture 53 Metadata capturing >> Metadata for loading satellite entities on hubs Lecture 55 Metadata capturing >> Metadata for loading data vault to Datamart Section 10: Multi-dimensional database (MOLAP cube) Lecture 56 Multi-dimensional database Section 11: Data warehouse platform Lecture 57 Data warehouse - data lake platform updates: IBM & AWS data platform Section 12: Hands-on practices Lecture 58 SSIS load: source to Datavault, to Datamart, to OLAP cube Section 13: Summary session Lecture 59 Data Vault Mastery Modernizing Data Warehousing for Advanced Analytics Data Warehouse Architects,Data Engineers,Business Intelligence (BI) Developers,Data Analysts,Data Scientists,Data Managers and Data Governance Professionals,IT Managers and Professionals,Data ModellersPublished 7