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Michael Olschimke

Michael Olschimke is the Co-Founder and CEO of Scalefree and a "Data Vault 2.0 Pioneer" with over 20 years of IT experience. A Fulbright scholar and co-author of Building a Scalable Data Warehouse with Data Vault 2.0, Michael is a global authority on AI, Big Data, and scalable Lakehouse design across sectors like banking, automotive, and state security.

In Intermediate

Automating Business Logic

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Automating Business Logic

In this webinar, you’ll learn that Data Vault automation is not restricted to loading data, but can also be applied to the presentation layer.

There’s always some repeatable business logic – think of calculations such as currency conversion, Lifetime Value (LTV), or Net Present Value (NPV) – to feed different reports, even if all of them contain different information.

We’ll explain how you can create custom business templates and add additional layers in the information marts, to apply calculations repeatedly and even interdependently, thereby extending the scope of Data Vault automation from integration to presentation.

This webinar focuses on practical solutions.

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Webinar Agenda

1. How to get data out of a Data Vault.
2. What’s a PIT, what’s a bridge?
3. What’s meant by virtualization?
4. How to identify low-hanging fruits, i.e. the repeatable business logic in your solution.
5. How to automate those business rules using VaultSpeed.

In Data Vault Friday

Data Vault 2.0 Source System Disaster Recovery

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In our ongoing Data Vault Friday series, our CEO Michael Olschimke engages with a challenging question from our audience, aiming to find an elegant solution to a complex scenario.

“I’m trying to find an elegant way of addressing the following problem.

You have a DV2.0 Insert Only BI deployment fed by multiple OLTP systems. One of these OLTP systems will be subject to a disaster and associated recovery process. This will be done with a loss of 3h worth of data from the OLTP in question. During the 3 hours, multiple loads into the DV were completed.

I’m trying to avoid an effectivity satellite for each hub.”

In this insightful video, Michael explores strategies for handling data from multiple source systems with disaster considerations in a Data Vault 2.0 Insert Only BI deployment. The question prompts a discussion on avoiding the use of an effectivity satellite for each hub, offering alternative approaches to address the challenges posed by data loss during disaster recovery.

Michael shares practical insights and considerations for designing resilient solutions within the Data Vault framework while optimizing the balance between complexity and efficiency.

In Data Vault Friday

Data Vault 2.0 Project Tracking

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In our continuous Data Vault Friday series, our CEO Michael Olschimke addresses a pertinent question from our audience regarding the application of Scrum in Data Vault 2.0.

“We are struggling with the application of Scrum in Data Vault 2.0: the Kanban board is overloaded with technical user stories. However, in theory, the user stories should be oriented towards the business and user needs.”

In this insightful video, Michael delves into the challenges faced when integrating Scrum methodologies into Data Vault 2.0 projects, particularly the issue of an overloaded Kanban board with technical user stories. The question prompts a discussion on the alignment of user stories with business and user needs, emphasizing the importance of maintaining a business-centric focus.

Michael shares practical insights and recommendations for optimizing the use of Kanban boards in Data Vault 2.0 projects, ensuring a balance between technical requirements and business-oriented user stories.

In Data Vault Friday

(Logical) Information Marts in Data Vault

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In our continuous Data Vault Friday series, our CEO Michael Olschimke addresses a question from our audience that delves into the intricacies of the CDVP2 training.

“We are having trouble understanding the attached slide 28 of the CDVP2 training.

– What is the difference between Business DV Pits & Bridges and Pits & Bridges?
– We are confused about why Business Vault and Info Mart are put into one logical wrapper. Why does physical and logical wrapper differentiate?”

In this elucidating video, Michael provides clarification on the distinctions between “raw” and “business” Point-in-Time (PIT) and bridge tables. The question prompts a discussion on understanding the nuances of these components within the Data Vault methodology.

Michael shares insights into the reasoning behind grouping Business Vault and Info Mart into one logical wrapper while emphasizing the differentiation between physical and logical wrappers. The discussion provides valuable context for participants seeking clarity on the CDVP2 training material.

In Intermediate

Why Data Vault 2.0 Is the Best Data Model for Automation

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Why Data Vault 2.0 Is the Best Data Model for Automation

Many data teams worry that automation won’t work on their specific data and technology stack. They’ve learned the hard way that automation doesn’t always stand up to the complexity of different source data models, taxonomies, and tech stack components.
Join this webinar to understand how Data Vault 2.0 is designed to focus on models and logic, not complex code so that it’s rapidly becoming the DWH standard.

We’ll explain how Data Vault has taken the best of the more traditional modeling
approaches, such as Inmon or Kimball, to provide the level of abstraction, quality, and agility that automation requires.

You’ll learn how the Data Vault model and its methodology and architecture leverage
automation. And how we use integration templates based on Data Vault standards to pave the way to fully automated data loading.

This webinar takes you from theory to practice.

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Webinar Agenda

1. The pros and cons of different data modeling techniques.
2. The prerequisites for automation.
3. Why Data Vault works best.
4. How to create abstractions in data warehousing.
5. Demo: how it’s applied in VaultSpeed.

In Data Vault Friday

Supersetting in Data Vault

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In our ongoing Data Vault Friday series, our CEO Michael Olschimke engages with a thoughtful inquiry from our audience.

“Dear Scalefree team, we receive data from the source for multiple company forms (like HoldingCompany, JointVenture), and we want to know if it’s recommended to save them in different entities (e.g., HoldingCompany_h/s, JointVenture_h/s) or one big entity (Company_h/s).

If we split them, we will have for each company form (e.g., Holding Company) about 10 links; If we store everything in one Company entity, we may face the situation that different company forms have different master data in the future, besides, it violates the Data Vault 2.0 rule that we should save the data as delivered by the source.”

In this insightful video, Michael delves into the strategic considerations of applying sub-setting and super-setting in the context of Data Vault 2.0. The question prompts a discussion on where to employ these techniques and the potential exceptions that might arise from the default strategy.

Michael provides practical insights and recommendations for effectively handling diverse company forms within the Data Vault framework, ensuring compliance with Data Vault 2.0 principles while addressing the complexities of master data variations.

In Data Vault Friday

Reference Table Vs. Reference Hub in Data Vault

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In this week’s Data Vault Friday, our CEO Michael Olschimke addresses an intriguing question from our audience regarding the difference between a Reference Table and a Reference Hub.

“If I need to historize the reference table, I can use the Satellite pattern. Ok, I have now a Reference Satellite table. But what about the Reference Hub table? Is it effective to create a table with just one column?”

In this informative video, Michael explores the concept of historizing reference tables within Scalefree‘s Data Vault 2.0 projects. The question specifically focuses on the efficiency and effectiveness of creating a Reference Hub table with just one column.

Michael shares insights into the considerations and scenarios where creating a Reference Hub table with a single column can be a viable and effective approach. The discussion provides practical guidance for handling reference tables within the Data Vault 2.0 methodology.

In Data Vault Friday

Calculating Hash Keys in Business Vault

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In our ongoing Data Vault Friday series, our CEO Michael Olschimke delves into a thought-provoking question from our audience.

“When calculating hash_key in links in Business Vault, it sometimes can be quite expensive to join all hubs to get the business keys, etc. In many cases, we keep those hash_keys to keep the standards only. And even for any case where you may need to build a satellite for that link, that means you would have the same granularity. So is it still a no-go to generate the link hash_key from the hub hash_keys to prevent expensive joins in some cases? If so, what do you suggest?”

In this insightful video, Michael addresses the considerations and challenges related to calculating hash keys in links within the Business Vault. The question prompts a discussion on the trade-offs between keeping hash keys for standards and the potential expense of joins, especially when dealing with multiple hubs.

Michael shares his expertise on hashing practices in Data Vault 2.0 links, offering recommendations and considerations to optimize the balance between standards and performance in the Business Vault.

In Data Vault Friday

PIT Table Structure in Data Vault

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In our continuous Data Vault Friday series, our CEO Michael Olschimke engages with an insightful question from our audience.

“Is it possible to add business keys and/or descriptive attributes to a Point-in-Time (PIT) table to improve performance when filtering or joining data in the information mart?”

In this concise yet informative video, Michael delves into the consideration of enhancing the performance of filtering or joining data in the Information Mart by incorporating business keys and descriptive attributes into a PIT table. The question prompts a discussion on the circumstances and scenarios where denormalizing these elements into a PIT table may be beneficial.

Michael shares practical insights and considerations, providing clarity on when and how the inclusion of business keys and descriptive attributes in a PIT table can contribute to improved performance in data retrieval and analysis within the Information Mart.

In Data Vault Friday

Bridge Table and Zero Code Impact in Data Vault

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In our ongoing Data Vault Friday series, our CEO Michael Olschimke addresses a pertinent question from our audience.

“We are currently implementing a bridge table over a series of sprints. The table prepares a fact entity with many measure values that are added sprint by sprint. Some measures are based on other measures in the bridge table. Our issue is that the code to load the bridge table is already complex due to the many measures. It exceeds 800+ lines of code and requires constant reengineering when additional measures are added. Is there a more agile approach with less, maybe zero change impact on the existing code?”

In this insightful video, Michael explores strategies for building a bridge table in an agile and incremental fashion. The question prompts a discussion on addressing the complexity of the loading code and finding approaches that minimize change impact, ensuring a more flexible and adaptive development process.

The video offers practical insights and recommendations for streamlining the implementation of a bridge table, enhancing agility, and reducing the challenges associated with code maintenance in evolving data models.

In Intermediate

Boost ROI of Data Infrastructure with Automation

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Boost ROI of Data Infrastructure with Automation

Generating returns from a modern data infrastructure is tough. First, creating a central repository for easy data access requires much upfront, traditionally manual work to set up data ingestion, mapping, metadata management, etc. Changes in sources, tech stack, and taxonomies require more work. Or someone new comes on board and proposes building an entirely new model to answer the same business question. Typically, all this pushes the data team to take shortcuts to regain lost time, creating technical debt. In this webinar, we’ll explain how automation done right, following Data Vault 2.0 standards, will not only cut manual work but solve problems of agility, uncertainty, and output quality, to ultimately provide the return you expect. Learn about what can go wrong — and how to get it right.

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Webinar Agenda

1. Common pitfalls in data management.
2. How the problems were solved in the past: what worked and what didn’t
3. How Data Vault methodology combined with automation brings new solutions…
4. … And how this will save you time, and money.

In Data Vault Friday

Same-as-links Business Rules in Data Vault

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In our ongoing Data Vault Friday series, our CEO Michael Olschimke engages with a thought-provoking question from our audience regarding Same-as-Links.

“Sometimes mapping logic for Same-As-Links (SAL) requires complex ‘fuzzy’ business logic. When does the logic become too complex for the Raw Data Vault, and instead, the joining of similar tables from different sources becomes a Business Vault concern? It’s important to not have convoluted transformations in the Raw Data Vault, so where do we ‘draw the line’ on transformations being too convoluted/complex for a Raw Data Vault entity?”

In this enlightening video, Michael addresses the delicate balance between the Raw Data Vault, where no business logic is applied, and the Business Vault, where most business logic is implemented. He provides insights into recognizing when the mapping logic for Same-As-Links (SAL) becomes too intricate for the Raw Data Vault, prompting the shift to the Business Vault for handling complex transformations.

The discussion offers practical considerations and a clear perspective on drawing the line to maintain the efficiency and clarity of transformations within the Raw Data Vault.

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