Course Outline
Introduction
- The shortcomings of existing data warehouse data modeling architectures
- Benefits of Data Vault modeling
Overview of Data Vault architecture and design principles
- SEI / CMM / Compliance
Data Vault applications
- Dynamic Data Warehousing
- Exploration Warehousing
- In-Database Data Mining
- Rapid Linking of External Information
Data Vault components
- Hubs, Links, Satellites
Building a Data Vault
Modeling Hubs, Links and Satellites
Data Vault reference rules
How components interact with each other
Modeling and populating a Data Vault
Converting 3NF OLTP to a Data Vault Enterprise Data Warehouse (EDW)
Understanding load dates, end-dates, and join operations
Business keys, relationships, link tables and join techniques
Query techniques
Load processing and query processing
Overview of Matrix Methodology
Getting data into data entities
Loading Hub Entities
Loading Link Entities
Loading Satellites
Using SEI/CMM Level 5 templates to obtain repeatable, reliable, and quantifiable results
Developing a consistent and repeatable ETL (Extract, Transform, Load) process
Building and deploying highly scalable and repeatable warehouses
Closing remarks
Requirements
- An understanding of data warehousing concepts
- An understanding of database and data modeling concepts
Audience
- Data modelers
- Data warehousing specialist
- Business Intelligence specialists
- Data engineers
- Database administrators
Testimonials
Example exercises; Practical work experience sharing
澳新银行
Cube and DV
Alan Xie
The teacher's knowledge of the data warehouse is comprehensive, and he praises it!
澳新银行
The teacher explained in detail and discussed the atmosphere
澳新银行
Practical application, help in explaining many different doubts
- SGB-Bank S.A.
The Topic
Accenture Inc.
Related Courses
From Data to Decision with Big Data and Predictive Analytics
21 hoursAudience If you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc...) this course is for you. It is mostly aimed at decision makers and people who need to
Data Mining and Analysis
28 hoursObjective: Delegates be able to analyse big data sets, extract patterns, choose the right variable impacting the results so that a new model is forecasted with predictive
Data Mining
21 hoursCourse can be provided with any tools, including free open-source data mining software and applications
Data Mining with R
14 hoursR is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data
MonetDB
28 hoursMonetDB is an open-source database that pioneered the column-store technology approach. In this instructor-led, live training, participants will learn how to use MonetDB and how to get the most value out of it. By the end of this training,
Oracle SQL Intermediate - Data Extraction
14 hoursThe objective of the course is to enable participants to gain a mastery of how to work with the SQL language in Oracle database for data extraction at intermediate level.
Introductory R for Biologists
28 hoursR is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has also found followers among
Statistics with SPSS Predictive Analytics Software
14 hoursGoal: Learning to work with SPSS at the level of independence The addressees: Analysts, researchers, scientists, students and all those who want to acquire the ability to use SPSS package and learn popular data mining
Data Visualization
28 hoursThis course is intended for engineers and decision makers working in data mining and knoweldge discovery. You will learn how to create effective plots and ways to present and represent your data in a way that will appeal to the decision makers
Foundation R
7 hoursThe objective of the course is to enable participants to gain a mastery of the fundamentals of R and how to work with data.
Data Mining & Machine Learning with R
14 hoursR is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data
Data Science for Big Data Analytics
35 hoursBig data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer,
Knowledge Discovery in Databases (KDD)
21 hoursKnowledge discovery in databases (KDD) is the process of discovering useful knowledge from a collection of data. Real-life applications for this data mining technique include marketing, fraud detection, telecommunication and manufacturing. In
KNIME Analytics Platform for BI
21 hoursKNIME Analytics Platform is a leading open source option for data-driven innovation, helping you discover the potential hidden in your data, mine for fresh insights, or predict new futures. With more than 1000 modules, hundreds of ready-to-run
Process Mining
21 hoursProcess mining, or Automated Business Process Discovery (ABPD), is a technique that applies algorithms to event logs for the purpose of analyzing business processes. Process mining goes beyond data storage and data analysis; it bridges data with