Skip to main content
0
All Posts By

Sandra Krenn

Jun 30
Love27

Splitting a Satellite entity based on the source data

By Scalefree Newsletter No Comments
Satellite splitting criteria plays a vital role in a satellite’s structure. Being such, it is not recommended that the entirety of descriptive data related to a business key should be stored in a single satellite structure. Instead, raw data should preferably be split by certain criteria.

In general, we have defined the following types of satellite splits:

  1. Splitting by source system
  2. Splitting by rate of change

Additionally, we have defined two more types of splits as mentioned below:

  1. Splitting by level of security and by the level of privacy
  2. Business-driven split

A satellite split by source system is strongly recommended to prevent two issues when loading the data into the enterprise data warehouse: First, if two different source systems with different relational structures should be loaded into the same satellite entity, a transformation of the structure might be required. However, structural transformation requires business logic sooner or later and that should be deferred to the information delivery stage to support fully-auditable environments as well as the application of multiple business perspectives. Read More

May 28
Love24

What to consider for naming conventions in Data Warehousing – Part 2

By Scalefree Newsletter No Comments
In a previous blog post, we discussed the different aspects of a naming standard documentation – from letter case types to the consideration between using prefixes or suffixes in database object names.

Throughout this article, we will continue presenting our suggestions for naming conventions in a data warehouse solution, as well as sharing examples for naming standards, which both our team and our customers utilize internally.

Layer schemas

For layer schema names, we prefer using prefixes.
As discussed in the previous blog post, this convention boosts visibility in data exploration within the Enterprise Data Warehouse for developers and business users by grouping schemas of the same data warehouse layer together.
The following is a list of common Enterprise Data Warehouse layers and our associated recommendations regarding naming conventions: Read More

Apr 03
Love26

CHANGES WE ARE MAKING FOR YOU

By Announcements No Comments
This month, our blog post will take a slightly different tone than our normal publications due to the current Covid-19 pandemic and its paralyzing effect on many areas of public life.

This said, we at Scalefree are reflecting on the ways we can do our part to  help contain the virus. Thus, the health of Scalefree’s customers, partners, employees, and the overall community is our highest priority. We do not want to subject any person to undue risk, and certainly those around us, who could be more seriously affected by the virus.

Due to this growing concern regarding the coronavirus (COVID-19), as well as in alignment with the best practices in addition to restrictions from local authorities, we have chosen to make our training programs fully available as live and interactive online classes.

Therefore, we would like to give you an overview of what changes Scalefree is making to help  stem the spread of Covid-19 while at the same time delivering actionable insights to our partners, customers and employees.

Read More

Jan 28
Love26

Delete and Change Handling Approaches in Data Vault 2.0 without a Trail

By Scalefree Newsletter 2 Comments
In January of this year, we published a piece detailing an approach to handle deletes and business key changes of relationships in Data Vault without having an audit trail in place.
This approach is an alternative to the Driving Key structure, which is part of the Data Vault standards and a valid solution.
Though, at times it may be difficult to find the business keys in a relationship which will never change and therefore be used as the anchor keys, Link Driving Key, when querying. The presented method inserts counter records for changed or deleted records, specifically for transactional data, and is a straightforward as well as pragmatic approach. However, the article caused a lot of questions, confusion and disagreements.
That being said, it is the intention of this blogpost to dive deeper into the technical implementation in which we could approve by employing it. Read More

Dec 19
Love21

An Efficient Data Lake Structure

By Scalefree Newsletter One Comment
Within a hybrid data warehouse architecture, as promoted in the Data Vault 2.0 Boot Camp training, a data lake is used as a replacement for a relational staging area. Thus, to take full advantage of this architecture, the data lake is best organized in a way that allows efficient access within a persistent staging area pattern and better data virtualization.

Read More

Dec 11
Love19

Data Vault Use Cases Beyond Classical Reporting: Part 2

By Scalefree Newsletter No Comments
As we first introduced within the first part of the Data Vault Use Cases article series, the Enterprise Data Warehouse (EDW) can do more than just simple reporting and dashboarding. 

We previously explored how the EDW can help to improve data quality by implementing data cleansing rules. 

This can be applied by write-back operations that affect the source systems directly. Though this was only one example of how to add more value to the EDW.
The scalability and flexibility of Data Vault 2.0 offers a whole variety of use cases that can be realized, e.g. to optimize and automate operational processes, predict the future, push data back to operational systems as a new input or trigger events outside the data warehouse, to name a few.

Read More

Close Menu