Data Vault Series (2)

Data Vault 2.0 Modeling Basics

Kent Graziano

by
Kent Graziano
Data Vault Master (CDVM2), Data Warrior LLC

Posted: October 20, 2015

In my last post, we looked at the need for an Agile Data Engineering solution, issues with some of the current data warehouse modeling approaches, the history of data modeling in general, and Data Vault specifically. This time we get into the technical details of what the Data Vault Model looks like and how you build one.

For my examples I will be using a simply Human Resources (HR) type model that most people should relate to (even if you have never worked with an HR model). In this post I will walk through how you get from the OLTP model to the Data Vault model.

Data Vault Prime Directive

One thing to get very clear up front is that, unlike many data warehouse implementations today, the Data Vault Method requires that we load data exactly as it exists in the source system. No edits, no changes, no application of soft business rules (including data cleansing).

Why?

So that the Data Vault is 100% auditable.

If you alter the data on the way into the Data Vault, you break the ability to trace the data to the source in case of an audit because you cannot match the data warehouse data to source data. Remember your EDW (Enterprise Data Warehouse) is the enterprise store of historical data too. Once the data is purged from the source systems, your EDW may also be your Source of Record. So it is critical the data remain clean.

We have a saying in Data Vault world – The DV is a source of FACTS, not a source of TRUTH (truth is often subjective & relative in the data world).

Now, you can alter the data downstream from the Data Vault when you build your Information Marts. I will discuss that in more detail in the 3rd and 4th articles.

Hubs

For a Data Vault, the first thing you do is model the Hubs. Hubs are the core of any DV design. If done properly, Hubs are what allow you to integrate multiple source systems in your data warehouse. To do that, they must be source system agnostic. That means they must be based on true Business Keys (or meaningful natural keys) that are not tied to any one source system.

That means you should not use source system surrogate keys for identification. Hubs keys must be based on an identifiable business element or elements.

What is an identifiable business element? It is a column (or set of columns) found in the systems that the business consistently uses to identify and locate the data. Regardless of source system, these elements must have the same semantic meaning (even though the names may vary from source to source). If you are very lucky, the source system model will have this defined for you in the form of alternate unique keys or indexes. If not you will need to engage in data profiling and conversations with the business customers to figure out what the business keys are.

This is the most important aspect of Data Vault modeling. You must get this right if you intend to build an integrated enterprise data warehouse for your organization.

(To be honest, in order to really, really do this right, you should start by building a conceptual model based on business processes using business terminology. If you do that, the Data Vault model will be most obvious to you and it will not be based on any existing source system).

As an example, look at these tables from a typical HR application. This is the source OLTP example we will work with.



Figure 1 – Source Model



The Primary Key (PK) on the LOCATION table is LOCATION_ID. But that is an integer surrogate key. It is not a good candidate for the Business Key.

Why? Because every source system can have an ID of 1, 2, 201, 5389, etc. It is just a number and has no meaning in the real world. Instead LOCATION_NAME is the likely candidate for the Business Key (BK). Looking in the database, we see that it has a unique index. Great! (If there were no unique indexes, we would have to talk to the business and/or query the data directly to find a unique set of columns to use.)

For the COUNTRIES table, the COUNTRY_ABBREV column will work as a Business Key, and for REGIONS, it would be the REGION_NAME.

Fundamentally, a Hub is a list of unique business keys.

A Hub table has a very simple structure. It contains:

  • A Hub PK
  • The Business Key column(s)
  • The Load Date (LOAD_DTS)
  • The Source for the record (REC_SRC)

New in DV 2.0, the Hub PK is a calculated field consisting of a Hash (often MD5) of the Business Key columns (more on that in a bit).

The Business Key must be a declared unique or alternate key constraint in the Hub. That means for each key there will be only on row in the Hub table, ever. It can be a compound key made up of more than one column.

The LOAD_DTS tells us the first time the data warehouse “knew” about that business key. So no matter how many loads you run, this row is created the first time and never dropped or updated.

The REC_SRC tells us which source system the row came from. If the value can come from multiple sources, this will tell us which source fed us the value first.

Simple hub table
Figure 2 – Simple Hub Table

In the HUB_LOCATION table, LOCATION_NAME is the Business Key. It is a unique name used in the source systems to identify a location. It must have a unique constraint or index declared on it in the database to prevent duplicates from being entered and to facilitate faster query access.

What can you do with a single Hub table?

You can do data profiling and basic analysis on the business key. Answer questions like:

  1. How many locations do we have?
  2. How many source systems provide us locations names?
  3. Are there data quality issues? Do we see Location Names that seem similar but are from different sources? (Hint: if you do, you may have a master data management issue)

Hash-based Primary Keys

One of the innovations in DV 2.0 was the replacement of the standard integer surrogate keys with hash-based primary keys. This was to allow a DV solution to be deployed, at least in part, on a Hadoop solution. Hadoop systems do not have surrogate key generators like a modern RDBMS, but you can generate an MD5 Hash. With a Hash Key in Hadoop and one in your RDBMS, you can “logically” join this data (with the right tools of course). (Plus sequence generators and counters can become a bottleneck on some systems at very high volumes and degrees of parallelism.)

Here is an example of Oracle code to create the HUB_LOCATION_KEY:

dbms_obfuscation_toolkit.md5(upper(trim(location_name)))

For a Hub and source Stage tables, we apply an MD5 (or other) hash calculation to the Business Key columns. So if you have so much data coming so fast that you cannot load it quickly into your database, one option is to build stage tables (really copies of source tables) on Hadoop so you do not lose the data. As you load those files, the hash calculation is applied to the Business Key and added as a new piece of data during the load. Then when it is time to load to your database you can compare the Hash Key on Hadoop to the Hash Key in your Hub table to see if the data is loaded already or not. (Note: how to load a data vault is too much for a blog post; please look at the new Data Vault book or the online training class for details).

Links

The Link is the key to flexibility and scalability in the Data Vault modeling technique. They are modeled in such a way as to allow for changes and additions to the model over time by providing the ability to easily add new objects and relationships without having to change existing structures or load routines.

In a Data Vault model, all source data relationships (i.e., foreign keys) and events are represented as Links. One of the foundational rules in DV is that Hubs can have no FKs, so to represent the joins between Hub concepts, we must use a Link table. The purpose of the Link is to capture and record the relationship of data elements at the lowest possible grain. Other examples of Links include transactions and hierarchies (because in reality those are the intersection of a bunch of Hubs too).

A Link is therefore an intersection of business keys. It contains the columns that represent the business keys from the related Hubs. A Link must have more than one parent table. There must be at least two Hubs, but, as in the case of a transaction, they may be composed of many Hubs. A Link table’s grain is defined by the number of parent keys it contains (very similar to a Fact table in dimensional modeling).

Like a Hub, the Link is also technically a simple structure. It contains:

  • A Link PK (Hash Key)
  • The PKs from the parent Hubs – used for lookups
  • The Business Key column(s) – new feature in DV 2.0
  • The Load Date (LOAD_DTS)
  • The Source for the record (REC_SRC)
Link and parent hubs
Figure 3 – Link and Parent Hubs

In Figure 3 you see the results of converting the FK to REGIONS from the COUNTRIES table (Figure 1) into a Link table. The PK column LNK_REGION_COUNTRY_KEY is a hash key calculated against the Business Key columns from the contributing Hubs. This gives us a unique key for every combination of Country and Region that may be fed to us by the source systems.

In Oracle that might look like:

dbms_obfuscation_toolkit.md5(upper(trim(country_abbrv))||’^’|| upper(trim(region_name)))

Just as in the Hubs, the Link records only the first time that the relationship appears in the DV.

In addition, the Link contains the PKs from the parent Hubs (which should be declared as an alternate unique key or index). This makes up the natural key for the Link.

New to DV 2.0 is the inclusion of the text business key columns from the parent Hubs. Yes, this is a specific de-normalization.

Why? For query performance when you want to extract data from the Data Vault (more on this in part 3 & 4 of this series). Depending on your platform, you may consider adding a unique key constraint or index on these columns as well.

Why are Links Many-to-Many?

Links are intersection tables and so, by design, they represent a many-to-many relationship. Since a FK is usually 1:M, you may ask why it is done this way.

One word: Flexibility!

Remember that part of the goal of the Data Vault is to store only the facts and avoid reengineering (refactoring) even if the business rules or source systems change. So if you did your business modeling right, the only thing that might change would be the cardinality of a relationship. Now granted it is not likely that one Region will ever be in two Countries, but if that rule changed or you get a new source system that allows it, the 1:M might become a M:M. With a Link, we can handle either with no change to the design or the load process!

That is dynamic adaptability. That is an agile design. (Maximize the amount of work not done.)

Using this type of design pattern allows us to more quickly adapt and absorb business rule and source system changes while minimizing the need to re-engineer the Data Vault. Less work. Less time. No re-testing. Less money!

Satellites

Satellites (or Sats for short) are where all the big action is in a Data Vault. These structures are where all the descriptive (i.e., non-key) columns go, plus this is where the Change Data Capture (CDC) is done and history is stored. The structure and concept are very much like a Type 2 Slowly Changing Dimension.

To accomplish this function, the Primary Key for a Sat contains two parts: the PK from its Parent Hub (or Link) plus the LOAD_DTS. So every time we load the DV and find new records or changed records, we insert those records into the Sats and give them a timestamp. (On a side note, this structure also means that a DV is real-time-ready in that you can load whenever and as often as you need as long as you set the LOAD_DTS correctly.)

This is the only structure in the core Data Vault that has a two-part key. That is as complicated as it gets from a structure perspective.

Of course, a Sat must also have the REC_SRC column for auditability. REC_SRC will tell us the source of each row of data in the Sat.

Important Note: The REC_SRC in a Sat does NOT have to be the same as that in the parent Hub or Link. Remember that Hubs and Links record the source of the concept key or relationship the first time the DV sees it. Subsequent loads may find different sources provide different descriptive information at different times for one single Hub record (don’t forget that we are integrating systems too).

You may have noticed that not all the columns in Figure 1 ended up in the Hubs or the Link tables that we have looked at.

Where do they go? They go in the Sats.



Figure 4 – Hubs and Links with Sats



In Figure 4, look at SAT_LOCATIONS. There you see all the address columns from the original table LOCATIONS (Figure 1). Likewise in SAT_COUNTRIES you see COUNTRY_NAME and in SAT_REGIONS you see REGION_ID (remember that was the source system PK but NOT the Business Key, so it goes here so we can trace back to the source if needed).

Use of HASH_DIFF columns for Change Data Capture (CDC)

Another innovation that came with DV 2.0 is the use of a Hash-based column for determining if a record in the source has changed from what was previously loaded into the Data Vault. We call that a Hash Diff (for difference). Every Sat must have this column to be DV 2.0 compliant.

So, how this works is that you first calculate a Hash on the combination of all of the descriptive (non-meta data) columns in the Sat.

Examples for Oracle for the three Sats in Figure 4 are:

SAT_REGIONS.HASH_DIFF = dbms_obfuscation_toolkit.md5(TO_CHAR(region_id))

SAT_COUNTRIES.HASH_DIFF = dbms_obfuscation_toolkit.md5(TRIM(country_name))

SAT_LOCATIONS.HASH_DIFF =
   dbms_obfuscation_toolkit.md5(
      TRIM(street_address) || ’^’ || TRIM(city) || ^ || TRIM(state_province) 
      || ’^’ || TRIM(postal_code))

Now when you get ready to do another load, you must calculate a Hash on the inbound values (using exactly the same formula) and then compare that to the HASH_DIFF column in the Sat for the most recent row (i.e., from the last load date) for the same Hub Business Key. If the Hash calculation is different, then you create a new row. If not, you do nothing (which is way faster).

This is exactly what you do when you build a Type 2 SCD (Slowly Changing Dimension).

The difference is that instead of comparing every single column in the feed to every single column in the Sat, you only have to compare one column – the HASH_DIFF. (See my white paper to learn on how we did it in DV 1.0)

So which do you think is faster on a very wide table?

Comparing one column vs. 50 columns (or several hundred columns)?

In fact, since all the HASH_DIFFs are the same size, the speed of the comparison allows you to scale to wider tables without the CDC process slowing your down.

Is it overkill for a small, narrow table? Probably.

But if you want a standards based, repeatable approach, you apply the same rules regardless. Plus if you follow the patterns, you can automate the generation of the design and load processes (or buy software that does it for you).

Another Note: There are no updates! We never over write data in a Data Vault! We only insert new data. That allows us to keep a clean audit trail.

Conclusion

So there you have the basics (and then some) of what makes up a raw Data Vault Data Model and you have seen some of the innovations from Data Vault 2.0

Just remember this list:

  • Hubs = Business Keys
  • Links = Associations / Transactions
  • Satellites = Descriptors

Hubs make it business driven and allow for integration across systems.

Links give you the flexibility to absorb structural and business rule changes without re-engineering (and therefore without reloading any data).

Sats give you the adaptability to record history at any interval you want plus unquestionable auditability and traceability to your source systems.

All together you get agility, flexibility, adaptability, auditability, scalability, and speed to market.

What more could a data warehouse architect want?

Want more in-depth details? Check out LearnDataVault.com

Until next time!

Kent Graziano

Kent Graziano, Data Vault Master (CDVM2), Data Warrior LLCKent Graziano is a recognized industry expert, leader, trainer, and published author in the areas of data modeling, data warehousing, data architecture, and various Oracle tools (like Oracle Designer and Oracle SQL Developer Data Modeler). A certified Data Vault Master, Data Vault 2.0 Practitioner (CDVP2), and an Oracle ACE Director with over 30 years experience in the Information Technology business. Nowadays Kent is independent consultant (Data Warrior LLC) working out of The Woodlands, Texas.

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