Implementing a Dimensional Knowledge Warehouse with Databricks SQL: Half 2

As organizations consolidate analytics workloads to Databricks, they typically must adapt conventional knowledge warehouse strategies. This collection explores how you can implement dimensional modeling—particularly, star schemas—on Databricks. The primary weblog centered on schema design. This weblog walks by way of ETL pipelines for dimension tables, together with Slowly Altering Dimensions (SCD) Sort-1 and Sort-2 patterns. The final weblog will present you how you can construct ETL pipelines for reality tables.

Slowly Altering Dimensions (SCD)

Within the final weblog, we outlined our star schema, together with a reality desk and its associated dimensions.  We highlighted one dimension desk particularly, DimCustomer, as proven right here (with some attributes eliminated to preserve house):

The final three fields on this desk, i.e., StartDate, EndDate and IsLateArriving, characterize metadata that assists us with versioning information.  As a given buyer’s earnings, marital standing, house possession, variety of kids at house, or different traits change, we are going to need to create new information for that buyer in order that details reminiscent of our on-line gross sales transactions in FactInternetSales are related to the precise illustration of that buyer.  The pure (aka enterprise) key, CustomerAlternateKey, would be the similar throughout these information however the metadata will differ, permitting us to know the interval for which that model of the client was legitimate, as will the surrogate key, CustomerKey, permitting our details to hyperlink to the precise model.  

NOTE: As a result of the surrogate secret is generally used to hyperlink details and dimensions, dimension tables are sometimes clustered based mostly on this key. Not like conventional relational databases that make the most of b-tree indexes on sorted information, Databricks implements a singular clustering methodology referred to as liquid clustering. Whereas the specifics of liquid clustering are outdoors the scope of this weblog, we constantly use the CLUSTER BY clause on the surrogate key of our dimension tables throughout their definition to leverage this function successfully.

This sample of versioning dimension information as attributes change is called the Sort-2 Slowly Altering Dimension (or just Sort-2 SCD) sample. The Sort-2 SCD sample is most popular for recording dimension knowledge within the traditional dimensional methodology. Nevertheless, there are different methods to cope with adjustments in dimension information.

Probably the most frequent methods to cope with altering dimension values is to replace present information in place.  Just one model of the document is ever created, in order that the enterprise key stays the distinctive identifier for the document.  For numerous causes, not the least of that are efficiency and consistency, we nonetheless implement a surrogate key and hyperlink our reality information to those dimensions on these keys. Nonetheless, the StartDate and EndDate metadata fields that describe the time intervals over which a given dimension document is taken into account lively usually are not wanted. This is called the Sort-1 SCD sample.  The Promotion dimension in our star schema offers a superb instance of a Sort-1 dimension desk implementation:

However what concerning the IsLateArriving metadata subject seen within the Sort-2 Buyer dimension however lacking from the Sort-1 Promotion dimension? This subject is used to flag information as late arriving.  A late arriving document is one for which the enterprise key reveals up throughout a reality ETL cycle, however there is no such thing as a document for that key situated throughout prior dimension processing.  Within the case of the Sort-2 SCDs, this subject is used to indicate that when the information for a late arriving document is first noticed in a dimension ETL cycle, the document ought to be up to date in place (identical to in a Sort-1 SCD sample) after which versioned from that time ahead.  Within the case of the Sort-1 SCDs, this subject isn’t vital as a result of the document shall be up to date in place regardless.

NOTE: The Kimball Group acknowledges further SCD patterns, most of that are variations and mixtures of the Sort-1 and Sort-2 patterns. As a result of the Sort-1 and Sort-2 SCDs are probably the most ceaselessly carried out of those patterns and the strategies used with the others are intently associated to what’s employed with these, we’re limiting this weblog to simply these two dimension varieties. For extra details about the eight sorts of SCDs acknowledged by the Kimball Group, please see the Slowly Altering Dimension Strategies part of this doc.

Implementing the Sort-1 SCD Sample

With knowledge being up to date in place, the Sort-1 SCD workflow sample is probably the most simple of the two-dimensional ETL patterns. To assist all these dimensions, we merely:

1. Extract the required knowledge from our operational system(s)
2. Carry out any required knowledge cleaning operations
3. Evaluate our incoming information to these already within the dimension desk
4. Replace any present information the place incoming attributes differ from what’s already recorded
5. Insert any incoming information that don’t have a corresponding document within the dimension desk

As an example a Sort-1 SCD implementation, we’ll outline the ETL for the continuing inhabitants of the DimPromotion desk.

Step 1: Extract knowledge from an operational system

Our first step is to extract the information from our operational system.  As our knowledge warehouse is patterned after the AdventureWorksDW pattern database offered by Microsoft, we’re utilizing the intently related AdventureWorks (OLTP) pattern database as our supply. This database has been deployed to an Azure SQL Database occasion and made accessible inside our Databricks setting by way of a federated question.  Extraction is then facilitated with a easy question (with some fields redacted to preserve house), with the question outcomes persevered in a desk in our staging schema (that’s made accessible solely to the information engineers in the environment by way of permission settings not proven right here). That is however one among some ways we will entry supply system knowledge on this setting:

Step 2: Evaluate incoming information to these within the desk

Assuming we now have no further knowledge cleaning steps to carry out (which we might implement with an UPDATE or one other CREATE TABLE AS assertion),  we will then deal with our dimension knowledge replace/insert operations in a single step utilizing a MERGE assertion, matching our staged knowledge and dimension knowledge on the enterprise key:

One essential factor to notice concerning the assertion, because it’s been written right here, is that we replace any present information when a match is discovered between the staged and revealed dimension desk knowledge. We might add further standards to the WHEN MATCHED clause to restrict updates to these situations when a document in staging has totally different info from what’s discovered within the dimension desk, however given the comparatively small variety of information on this explicit desk, we’ve elected to make use of the comparatively leaner logic proven right here.  (We are going to use the extra WHEN MATCHED logic with DimCustomer, which accommodates way more knowledge.)

The Sort-2 SCD sample

The Sort-2 SCD sample is a little more complicated. To assist all these dimensions, we should:

1. Extract the required knowledge from our operational system(s)
2. Carry out any required knowledge cleaning operations
3. Replace any late-arriving member information within the goal desk
4. Expire any present information within the goal desk for which new variations are present in staging
5. Insert any new (or new variations) of information into the goal desk

Step 1: Extract and cleanse knowledge from a supply system

As within the Sort-1 SCD sample, our first steps are to extract and cleanse knowledge from the supply system.  Utilizing the identical strategy as above, we concern a federated question and persist the extracted knowledge to a desk in our staging schema:

Step 2: Evaluate to a dimension desk

With this knowledge landed, we will now examine it to our dimension desk to be able to make any required knowledge modifications.  The primary of those is to replace in place any information flagged as late arriving from prior reality desk ETL processes.  Please observe that these updates are restricted to these information flagged as late arriving and the IsLateArriving flag is being reset with the replace in order that these information behave as regular Sort-2 SCDs shifting ahead:

Step 3: Expire versioned information

The subsequent set of knowledge modifications is to run out any information that have to be versioned.  It’s essential that the EndDate worth we set for these matches the StartDate of the brand new document variations we are going to implement within the subsequent step.  For that purpose, we are going to set a timestamp variable for use between these two steps:

NOTE: Relying on the information out there to you, chances are you’ll elect to make use of an EndDate worth originating from the supply system, at which level you wouldn’t essentially declare a variable as proven right here.

Please observe the extra standards used within the WHEN MATCHED clause.  As a result of we’re solely performing one operation with this assertion, it could be doable to maneuver this logic to the ON clause, however we stored it separated from the core matching logic, the place we’re matching to the present model of the dimension document for readability and maintainability.

As a part of this logic, we’re making heavy use of the equal_null() perform.  This perform returns TRUE when the primary and second values are the identical or each NULL; in any other case, it returns FALSE.  This offers an environment friendly option to search for adjustments on a column-by-column foundation.  For extra particulars on how Databricks helps NULL semantics, please check with this doc.

At this stage, any prior variations of information within the dimension desk which have expired have been end-dated.  

Step 4: Insert new information

We are able to now insert new information, each really new and newly versioned:

As earlier than, this might have been carried out utilizing an INSERT assertion, however the consequence is identical.  With this assertion, we now have recognized any information within the staging desk that don’t have an unexpired corresponding document within the dimension tables. These information are merely inserted with a StartDate worth per any expired information that will exist on this desk.

Subsequent steps: implementing the very fact desk ETL

With the scale carried out and populated with knowledge, we will now concentrate on the very fact tables. Within the subsequent weblog, we are going to exhibit how the ETL for these tables may be carried out.

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