Implementing a Dimensional Information Warehouse with Databricks SQL: Half 2

As organizations consolidate analytics workloads to Databricks, they usually have to adapt conventional knowledge warehouse strategies. This collection explores methods to implement dimensional modeling—particularly, star schemas—on Databricks. The primary weblog targeted on schema design. This weblog walks by ETL pipelines for dimension tables, together with Slowly Altering Dimensions (SCD) Sort-1 and Sort-2 patterns. The final weblog will present you methods 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, symbolize metadata that assists us with versioning data.  As a given buyer’s revenue, marital standing, house possession, variety of kids at house, or different traits change, we are going to need to create new data for that buyer in order that details similar to 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 data 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’s generally used to hyperlink details and dimensions, dimension tables are sometimes clustered based mostly on this key. In contrast to conventional relational databases that make the most of b-tree indexes on sorted data, Databricks implements a singular clustering technique often called 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 function 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 popular for recording dimension knowledge within the basic dimensional methodology. Nonetheless, there are different methods to cope with modifications in dimension data.

One of the vital frequent methods to cope with altering dimension values is to replace current data in place.  Just one model of the file is ever created, in order that the enterprise key stays the distinctive identifier for the file.  For varied 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 file is taken into account energetic should not wanted. This is named the Sort-1 SCD sample.  The Promotion dimension in our star schema supplies a great instance of a Sort-1 dimension desk implementation:

However what in regards to 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 file is one for which the enterprise key reveals up throughout a truth ETL cycle, however there is no such thing as a file for that key positioned 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 file is first noticed in a dimension ETL cycle, the file needs to be up to date in place (similar 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 needed as a result of the file will likely be up to date in place regardless.

NOTE: The Kimball Group acknowledges extra 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 continuously 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 sorts. For extra details about the eight kinds 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 help a majority of these dimensions, we merely:

1. Extract the required knowledge from our operational system(s)
2. Carry out any required knowledge cleaning operations
3. Examine our incoming data to these already within the dimension desk
4. Replace any current data the place incoming attributes differ from what’s already recorded
5. Insert any incoming data that should not have a corresponding file within the dimension desk

For instance 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 surroundings through a federated question.  Extraction is then facilitated with a easy question (with some fields redacted to preserve house), with the question outcomes continued in a desk in our staging schema (that’s made accessible solely to the information engineers in the environment by permission settings not proven right here). That is however one in every of some ways we will entry supply system knowledge on this surroundings:

Step 2: Examine incoming data to these within the desk

Assuming now we have no extra 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 in regards to the assertion, because it’s been written right here, is that we replace any current 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 file in staging has completely 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 much more knowledge.)

The Sort-2 SCD sample

The Sort-2 SCD sample is a little more advanced. To help a majority of 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 data within the goal desk
4. Expire any current 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 difficulty a federated question and persist the extracted knowledge to a desk in our staging schema:

Step 2: Examine to a dimension desk

With this knowledge landed, we will now examine it to our dimension desk as a way 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 word 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 transferring ahead:

Step 3: Expire versioned data

The following set of information modifications is to run out any data that should be versioned.  It’s essential that the EndDate worth we set for these matches the StartDate of the brand new file 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, it’s possible 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 word 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 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 file for readability and maintainability.

As a part of this logic, we’re making heavy use of the equal_null() operate.  This operate 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 strategy to search for modifications 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 data within the dimension desk which have expired have been end-dated.  

Step 4: Insert new data

We will now insert new data, each really 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, now we have recognized any data within the staging desk that don’t have an unexpired corresponding file within the dimension tables. These data are merely inserted with a StartDate worth in keeping with 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 will now deal with the very fact tables. Within the subsequent weblog, we are going to show how the ETL for these tables will be carried out.

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