Implementing a Dimensional Knowledge Warehouse with Databricks SQL: Half 2

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

Slowly Altering Dimensions (SCD)

Within the final weblog, we outlined our star schema, together with a truth desk and its associated dimensions.  We highlighted one dimension desk specifically, 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, signify metadata that assists us with versioning data.  As a given buyer’s revenue, marital standing, dwelling possession, variety of youngsters at dwelling, or different traits change, we are going to wish to create new data for that buyer in order that details reminiscent of our on-line gross sales transactions in FactInternetSales are related to the best illustration of that buyer.  The pure (aka enterprise) key, CustomerAlternateKey, would be the identical throughout these data however the metadata will differ, permitting us to know the interval for which that model of the shopper was legitimate, as will the surrogate key, CustomerKey, permitting our details to hyperlink to the best model.  

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

This sample of versioning dimension data as attributes change is named the Sort-2 Slowly Altering Dimension (or just Sort-2 SCD) sample. The Sort-2 SCD sample is most well-liked for recording dimension knowledge within the basic dimensional methodology. Nonetheless, there are different methods to cope with adjustments in dimension data.

One of the vital frequent methods to cope with altering dimension values is to replace present data 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 truth data 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 aren’t wanted. This is named the Sort-1 SCD sample.  The Promotion dimension in our star schema supplies an excellent instance of a Sort-1 dimension desk implementation:

However what concerning the IsLateArriving metadata area seen within the Sort-2 Buyer dimension however lacking from the Sort-1 Promotion dimension? This area is used to flag data as late arriving.  A late arriving document is one for which the enterprise key reveals up throughout a truth 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 area is used to indicate that when the information for a late arriving document is first noticed in a dimension ETL cycle, the document must 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 area isn’t essential as a result of the document might be up to date in place regardless.

NOTE: The Kimball Group acknowledges extra SCD patterns, most of that are variations and combos of the Sort-1 and Sort-2 patterns. As a result of the Sort-1 and Sort-2 SCDs are probably the most regularly 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 only these two dimension varieties. For extra details about the eight sorts of SCDs acknowledged by the Kimball Group, please see the Slowly Altering Dimension Methods 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 easy of the two-dimensional ETL patterns. To assist these kinds of 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 data to these already within the dimension desk
4. Replace any present data the place incoming attributes differ from what’s already recorded
5. Insert any incoming data 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 supplied 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 surroundings 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 endured in a desk in our staging schema (that’s made accessible solely to the information engineers in our surroundings by means of permission settings not proven right here). That is however one in all some ways we are able to entry supply system knowledge on this surroundings:

Step 2: Evaluate incoming data to these within the desk

Assuming we’ve no extra knowledge cleaning steps to carry out (which we might implement with an UPDATE or one other CREATE TABLE AS assertion),  we are able to 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 data when a match is discovered between the staged and printed dimension desk knowledge. We might add extra 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 data 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 comprises way more knowledge.)

The Sort-2 SCD sample

The Sort-2 SCD sample is a little more advanced. To assist these kinds of 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 data within the goal desk
4. Expire any present data within the goal desk for which new variations are present in staging
5. Insert any new (or new variations) of data 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 problem 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 are able to 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 data flagged as late arriving from prior truth desk ETL processes.  Please notice that these updates are restricted to these data flagged as late arriving and the IsLateArriving flag is being reset with the replace in order that these data behave as regular Sort-2 SCDs shifting ahead:

Step 3: Expire versioned data

The subsequent set of knowledge modifications is to run out any data 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 accessible to you, you could 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 notice the extra standards used within the WHEN MATCHED clause.  As a result of we’re solely performing one operation with this assertion, it might be attainable to maneuver this logic to the ON clause, however we saved 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 supplies an environment friendly technique to search for adjustments on a column-by-column foundation.  For extra particulars on how Databricks helps NULL semantics, please seek advice from this doc.

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

Step 4: Insert new data

We are able to now insert new data, each actually new and newly versioned:

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

Subsequent steps: implementing the very fact desk ETL

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

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