Temporal Data in a Fast Changing World

  This training will focus on methods and techniques for handling bitemporal data in a Data Warehouse. It includes how to populate and afterwards get bitemporal data out of the Data Warehouse’s core layer. Nowadays, most data warehouses already store "some kind of history of" data. But what about events that took place at a different time than what the data warehouse represents to us? Or data that will be valid in the future? For example, future planned prices for products and goods or special prices for discount battles or sales promotions like the “Black Friday” in the United States.

What will You learn?

The Big Business Man smiled. "Time," he said, "is what keeps everything from happening at once.Ray Cummings, The Girl in the Golden Atom, novel (1922) Dirk Lerner is the teacher of Temporal data in a fast-changing world! In this class he will focus on methods and techniques for storing bitemporal data in a Data Warehouse as well as how to get data out of the Data Warehouse’s core layer by merging timelines of bitemporal data. Dirk will show bitemporal basics for a better understanding of loading data as well as the concepts to develop SQL queries to insert and update temporal data within a Data Warehouse. Finally, concepts to provide Star Schema Dimensions as non-, uni- or bitemporal objects will be demonstrated. All modules contain (mostly real world) examples and attendees will do several exercises and SQL-tutorials are provided.

Module 1 – Theory and basic temporal concepts Topic

Overview of bitemporal theory and methods. Introduction to the basic concepts of temporal data and temporal modeling structures in Data Vault but not only. 

• FastChangeCo
• Time concepts
• Non-, uni- and bitemporal terminology and data modeling structures
• Clock ticks and time periods
• Allen Relationship
• Exercises

Outcome

Students will be able to understand bitemporal concepts and terminology, apply bitemporal structures to Data Vault data modeling and handle temporal data.

Audience

Beginners and Advanced in the methods, concepts, and terminology with temporal data.

Module 2 – Deepen and populate temporal data (part I) Topic

Deepen bitemporal theory and methods. Introduction to basic SQL concepts to populate temporal data in Data Vault structures. In small teams exploring step-by-step the first part of the Allen Relationship.

• Using FastChangeCo’s Use Case
• Populate unitemporal and bitemporal data (part I of Allen Relationship)
• Exercises

Outcome

Students will be able to apply bitemporal concepts and terminology to populate first sets of temporal data into a Data Vault data model within FastChangeCo’s use case.

Audience

Already knowing the methods, concepts, and terminology of temporal data (participants of module 1).

Module 3 – Populate temporal data (part II) and temporal interfaces Topic

Further deepen bitemporal theory and methods. Second part of basic SQL concepts to populate temporal data in Data Vault structures. Again, exploring in small teams step-by-step the second part of the Allen Relationship.

• Using FastChangeCo’s Use Case
• Populate bitemporal data (part II of Allen Relationship)
• Real world cases of incoming bitemporal interfaces
• Exercises

Outcome

Students will be able to apply bitemporal concepts and terminology to populate all sets of temporal data into a Data Vault data model within FastChangeCo’s use case. Furthermore, students will be able to understand and handle temporal issues within real world incoming interfaces.

Audience

Already knowing the methods, concepts and terminology of temporal data and knowing how to populate first sets of temporal data (participants of module 1 and 2).

Module 4 – Getting temporal data out Topic

Overview of bitemporal star schema, facts, and dimensions. Introduction to the basic concepts of temporal data and temporal modeling structures in a Star Schema and how to populate it by merging timelines of bitemporal data, e.g. Data Vault Satellites.

• Using FastChangeCo’s Use Case
• Merge and condense/packing temporal data
• Provide and access temporal data through a Star Schema
• Exercises

Outcome

Students will understand how to merge and condense temporal data and to be able to provide temporal data into a Star Schema.

Audience

Already knowing the methods, concepts and terminology of temporal data and knowing how to populate all sets of temporal data (participants of module 1,2 and 3).

Is this Training Right for You?

Do you have to work with time-related data? Would you like to learn a structured approach to deal with this data and build appropriate structures? If so, this training is suitable for you.   Even if you are working with databases that support time-related data, the methods learned in this training will help you to understand the underlying technology and to reproduce the results.  As a Data Architect, Business Analyst, Data Modeler, Data Vault Expert, ETL developer, Data Warehouse Manager, BI Expert or BI Consultant you will get most out of this class.