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Achieve Data Lineage in Data Vault 2.0

By | Scalefree Newsletter | No Comments

One common requirement in data warehouse projects is to provide data lineage from end-to-end. However, custom solutions (for example custom Meta Marts for self-developed Data Vault generators) or tools from different vendors often break such end-to-end data lineage.

Unlike business or technical metadata, which is provided by the business or source applications, process execution metadata is generated by the data warehouse team and provides insights into the ETL processing for maintenance. The data is used by the data warehouse team or by end-users to better understand the data warehouse performance and results presented in the information marts. One type of process execution metadata is the control flow metadata which executes one or more data flows among other tasks. Logging the process execution provides a valuable tool for maintaining or debugging the ETL processes of the data warehouse because it provided information about the data lineage of all elements of the data warehouse.  Read More

Visual Data Vault by Example: Modeling in the Accounting Industry

By | Modeling | No Comments

With the advent of Data Vault 2.0, which adds architecture and process definitions to the Data Vault 1.0 standard, Dan Linstedt standardized the Data Vault symbols used in modeling. Based on these standardized symbols, the Visual Data Vault (VDV) modeling language was developed, which can be used by EDW architects to build Data Vault models. The authors of the book “Building a Scalable Data Warehouse”, who are the founders of Scalefree, required a visual approach to model the concepts of Data Vault in the book. For this purpose, they developed the graphical modeling language, which focuses on the logical aspects of Data Vault. The Microsoft Visio stencils and a detailed white paper are available on as a free download.

Hubs in Visual Data Vault

Business keys play an important role in every business, because they are referenced by business transactions and relationships between business objects. Whenever a business identifies and tracks business objects, business keys are used throughout business processes. This is one of the reasons why Data Vault is based on the business keys. In Data Vault models, business keys are stored in hub entities. The challenge is to identify the business keys which represent a business object uniquely. That can be just one business key, but also a composite key or a smart key. The first image shows a hub with only one business key attribute:

Here, the attribute Invoice Number is sufficient to identify the invoice. No other attribute is required (such as the invoice year). In other cases, it is not as easy Read More

Hash Keys in the Data Vault

By | Architecture | 3 Comments

One of the most obvious changes in Data Vault 2.0 is the introduction of hash keys in the model. These hash keys are mandatory because of the many advantages. Hash keys do not only speed up the loading process; they also ensure that the enterprise data warehouse can span across multiple environments: on-premise databases, Hadoop clusters and cloud storage.

Let’s discuss the performance gain first: to increase the loading procedures, dependencies in the loading process have to be minimized or even eliminated. Back in Data Vault 1.0 sequence numbers were used to identify a business entity and that had to include dependencies during the loading process as a consequence. These dependencies have slowed down the load process what is especially an issue in real-time-feeds. Hubs had to be loaded first before the load process of the satellites and links could start. The intention is to break these dependency by using the hash keys instead of sequence numbers as the primary key.

Business Keys vs Hash Keys

In advance, business keys may be a sequence number created by a single source system, e.g. the customer number. But, business keys can also be a composite key to uniquely identify a business entity, e.g. a flight in the aviation industry is identified by the flight number and the date because the flight number will be reused every day.

In general: a business key is the natural key used by the business to identify a business object.

While using the business keys in Data Vault might be an option, it is actually a slow one, using a lot of storage (even more than hash keys). Especially in links and their dependent satellites, many composite business keys are required to identify the relationship or transaction / event in a link – and to describe it in the satellite. This would require a lot of storage and slow down the loading process because not all database engines have the capability to execute efficient joins on variable length business keys. On the other hand we would have too many columns in the link, because every business key must be a part of the link. The issue at this point is that we also have different data types with different lengths in the links. This issue is exaggerated because it is also required to replicate the business keys into their satellites. To guarantee a consistent join performance, the solution is to combine the business keys into a single column value by using hash functions to calculate a unique representation of a business object.

Massively Parallel Processing (MPP)

Due to the independence during the load process of hubs, links and satellites, it is possible to do that all in parallel.

The idea is to use the fact that a hash key is derived from a business key or combination of business keys without the need of a lookup in the a parent table. Therefore, instead of looking up the sequence of a business key in a hub before describing the business key in the satellite, we can just calculate the hash key of the business key. The (correct) implementation of the hash function ensures that the same semantic business key leads to exactly the same hash key, regardless of the target entity loaded. Read More

The Value of Non-Historized Links

By | Modeling | 7 Comments

When Dan Linstedt, co-founder of Scalefree, invented the Data Vault, he had several goals in mind. One of the goals was to load data as fast as possible from the source into a data warehouse model, process it into information and present it to the business analyst in any desired target structure.

For simplicity and automation, the Data Vault model exists only of three basic entity types:

  1. Hubs: a distinct list of business keys
  2. Links: a distinct list of relationships between business keys
  3. Satellites: descriptive data, that describe the parent (business key or relationship) from a specific context, versioned over time.

Now, as we always teach (and sometimes preach): you can model all enterprise data using these three entity types alone. However, a model using only these entity types would have multiple disadvantages. Many complex joins, storage consumption, ingestion performance and missed opportunities for virtualization.

The solution? Adding a little more nuts and bolts to the core entity types of the Data Vault in order to cope with these issues. One of the nuts and bolts is the non-historized link, also known as Transaction Link:

In this example, Sales is a non-historized link that captures sales transactions of a customer, related to a store. The goal of the non-historized link is to ensure high performance on the way into the data warehouse and on the way out. Don’t forget, the ultimate goal of data warehousing is to build a data warehouse not just model it. And building a data warehouse involves much more than just the model: it requires people, processes, and technology. Read More