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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.

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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.

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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.

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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.

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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.

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In our ongoing Data Vault Friday series, our trainer Marc Finger delves into an intriguing question posed by the audience.

“In Hubs, we add two ghost records: one with 0s (unknown/zero key) and another with f’s (sometimes called error key). In the loading of the stage, in which cases should we replace the generated hash key with the error key instead, and how? Right now, if the Business Key (BK) or combination of BKs is null, we are always replacing it with the zero key. My question is in which cases should we use the ffff key instead.”

In this informative video, Marc explores the usage and value of zero keys when loading links within the Data Vault framework. The question prompts a discussion on the considerations and scenarios where replacing the generated hash key with the error key, represented by ‘ffff,’ is beneficial.

The video provides practical insights and recommendations for optimizing the handling of ghost records and error keys, contributing to a more robust and efficient Data Vault implementation.

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

“What additional steps are there in a Real-Time loading pattern on top of the batch loading pattern?”

In this concise yet informative video, Michael focuses on the nuances of incorporating real-time loading patterns into the Data Vault 2.0 architecture. The question prompts a discussion about the specific steps that distinguish real-time loading from the traditional batch loading pattern.

Michael shares insights into the additional considerations and steps required to ensure the effectiveness of real-time data integration. The discussion provides valuable guidance for those looking to enhance their understanding of real-time loading within the context of the Data Vault 2.0 framework.

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

“From one of our source systems, we receive customer information in real-time messages. These messages are set up in a way that they always contain the business key(s) to identify the customer and the attribute(s) that have been entered, changed, or set to null.

For example, the first message includes customer creation with various attributes, including an email address and, of course, a Business Key. A second message later might include the Business Key, a new email address, and a new attribute (e.g., birthplace) that did not exist in the previous one.

We have a list of attributes that the end user is interested in, in our Information Mart, specifically in the Customer dimension. How would you manage this in the Data Vault architecture?”

In this enlightening video, Michael begins by discussing the design often encountered in such scenarios, highlighting areas for potential improvement. He then shares his insights on how to efficiently manage the message stream within the Data Vault 2.0 model. The discussion touches on optimizing the design for better usability in the Information Mart while maintaining the integrity and efficiency of the Data Vault architecture.