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Success with bitemporal knowledge

  • Published in Testimonials

The first session at Data Modeling Zone Europe 2018 in Düsseldorf, was a session about bitemporal data by Dirk Lerner. The session was and is an extract from his current training Temporal Data in a Fast-Changing World, which is now available as open and private training [Link].

At work I just started at a new customer and was part of the data warehouse team, who was assigned the task of building the Data Vault Data Warehouse. We were developing the Data Vault generator. At that point we used an end-date for the business time. I remember the complexity for updating the old records and the time for the server it costs to do this. Especially when you want to add rows in between. To load history, for example, from old sources.

The training is led by Dirk in an interactive way.

With very small groups of two or three we did some exercises about timelines and Allen relationships. He told the difference between technical timeline and the business timeline. Dirk also supplied some nice synonyms for this. At the project we had sometimes communication problems about those two. So, when back at the office, I immediately introduced a name change for the business time. We have chosen for state_date for the business date and load_date for the assertion time (moment that the data arrived at the data warehouse).

The theory was also very clearly explained by Dirk, who introduced us into the world of Allen relationships and the terms for the different temporals that exists, like nontemporal, unitemporal and bitemporal. With his presentation in hand, I was able to convince my team to change our approach. The theory of Dirk helped me to introduce an insert-only architecture. With the exercises I did, I stand strong in the discussion with the team to get rid of the end-date columns. This column can be calculated, so it is yet not necessary.

Later on, in the project this decision seemed to be key in loading very old data into our data warehouse. We had to load 120 backup-databases into the data warehouse. I was assigned with this task, which took a lot of work, due to the little differences in the schemata between the different backups, I already had a lot to do, getting the data right and uploading it into the data warehouse. The latter was a lot easier, because of the insert-only architecture we used. We saved 1.5 Terabyte of expensive disk space and reduced the data warehouse to 400 Gigabytes. The old backups could be deleted, and we can access all the data, all the time. Before that change, the customer just reloaded a backup when needed.

The session from Dirk at DMZ Europe helped me to understand temporal correctly and helped me to explain the concepts to business analysts and data specialists. I used the examples from Dirk to show them the difference between NowNow, ThenThen and ThenNow. Still it is difficult to get my head around, but at least I feel safe with the knowledge acquired at Dirk’s bitemporal session.

Finally, I can recommend Dirk's training without doubt. If you are thinking about bitemporal data in your data warehouse or if you have problems with it, you should definitely contact Dirk.

Tijs van Rinsum, Qvada

Author of the book De Data Gastronoom: It’s a story about data no technical thing. For managers and people outside our space. Two guys in a restaurant talking about data topics. Like how to deal with history. What about scrum. And more. It’s a readable book for anyone.
Link: https://dedatagastronoom.nl


Upcoming Events for Temporal Data in a Fast-Changing World

TEDAMOH Academy - Temporal Data

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.

Over time, things change - things like customers, products, accounts, and so forth. But most of the data we keep about things describes what they are like currently, not what they used to be like. When things change, we update the data that describes them so that the description remains current. But all these things have a history, and many of them have a future as well, and often data about their past or about their future is also important. Tom Johnston

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) examples and participants will do several exercises. Corresponding SQL tutorials will be provided.

Generally, the training should take place as a two-day face-to-face training. As everyone knows, this is unfortunately not possible at the moment.

My experience with online trainings is that a whole day is exhausting for all participants and usually does not lead to the desired results. Therefore, in the case of a "locationless" training, I have decided to spread the 4 modules over a total of 4 days. As a rule from 9 a.m. to approx. 1 p.m.. Module 1 & 2 on the first two days of the event period and exactly one week later Module 3 & 4 (The last two days of the event period).

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. with 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.

If you have any doubts or questions, please do not hesitate to contact us!

Why are we doing this?

First of all, thank you to everyone who participated in one of my talks about temporal data, visited one of my trainings or took the time to write to me.

Often the desire to learn more about temporal data arose! Your positive feedback and success stories about bitemporal data in practice (see testimonials) are what drive us to build and run this training and to get better and better at it!

What is the training fee?

Early Bird: You receive a 15% discount on the regular rate as well as on the group rates. The rate is automatically reduced by the Early Bird.

All prices shown include VAT at the statutory rate of 19%. If you are a company in the EU (not Germany), the reverse charge procedure applies for VAT during cart checkout and no VAT is shown.

In all other cases, German VAT will be shown at the appropriate rate.

Frequently Asked Questions?

All FAQs can be read here.

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.

Event Date 2022-03-24 09:00
Event End Date 2022-04-01 13:00
Available place 12
Original Price 1.995,00€
Discounted Price 1.695,75€ (Until 2022-02-25 00:00)
Categories Temporal Data
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.

Event Date 2022-09-22 09:00
Event End Date 2022-09-30 13:00
Available place 12
Original Price 1.995,00€
Discounted Price 1.695,75€ (Until 2022-08-01 00:00)
Categories Temporal Data

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