Watch the Video

In our ongoing Data Vault Friday series, our BI Consultant Julian Brunner delves into a question from the audience that addresses a common challenge.

“One of our sources delivers all the historical data in one batch. So all the records have the same load date. How can I load the data into the EDW properly?”

In this insightful video, Julian shares practical solutions and strategies for loading historical data into an Enterprise Data Warehouse (EDW) when faced with the unique scenario of receiving all records with the same load date. The question prompts a discussion on best practices to ensure proper handling and integration of historical data within the Data Vault framework.

Julian provides valuable insights into the considerations and steps involved in effectively managing historical data loads, offering guidance on maintaining data integrity and accuracy within the EDW.

Watch the Video

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.

Watch the Video

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.

Watch the Video

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.

Watch the Video

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.

Watch the Video

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.

Watch the Video

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.

Watch the Video

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.