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What’s a Dimensional Mannequin and Find out how to Implement It on the Databricks Lakehouse Platform


Dimensional modeling is without doubt one of the hottest information modeling methods for constructing a contemporary information warehouse. It permits prospects to shortly develop details and dimensions based mostly on enterprise wants for an enterprise. When serving to prospects within the discipline, we discovered many are on the lookout for finest practices and implementation reference structure from Databricks.

On this article, we intention to dive deeper into the perfect observe of dimensional modeling on Databricks’ Lakehouse Platform and supply a stay instance to load an EDW dimensional mannequin in real-time utilizing Delta Dwell Tables.

Listed below are the high-level steps we are going to cowl on this weblog:

  1. Outline a enterprise downside
  2. Design a dimensional mannequin
  3. Finest practices and suggestions for dimensional modeling
  4. Implementing a dimensional mannequin in a Databricks Lakehouse
  5. Conclusion

1. Outline a enterprise downside

Dimensional modeling is business-oriented; it at all times begins with a enterprise downside. Earlier than constructing a dimensional mannequin, we have to perceive the enterprise downside to unravel, because it signifies how the info asset will probably be introduced and consumed by finish customers. We have to design the info mannequin to assist extra accessible and sooner queries.

The Enterprise Matrix is a basic idea in Dimensional Modeling, under is an instance of the enterprise matrix, the place the columns are shared dimensions and rows signify enterprise processes. The outlined enterprise downside determines the grain of the actual fact information and required dimensions. The important thing concept right here is that we may incrementally construct extra information property with ease based mostly on the Enterprise Matrix and its shared or conformed dimensions.

A Business Matrix with Shared Dimensions and Business Processes
A Enterprise Matrix with Shared Dimensions and Enterprise Processes

Right here we assume that the enterprise sponsor wish to group to construct a report to offer insights on:

  1. What are the highest promoting merchandise to allow them to perceive product reputation
  2. What are the perfect performing shops to be taught good retailer practices

2. Design a dimensional mannequin

Primarily based on the outlined enterprise downside, the info mannequin design goals to signify the info effectively for reusability, flexibility and scalability. Right here is the high-level information mannequin that might clear up the enterprise questions above.

A diagram shows how star schema works, with facts and dimensions built in Lakehouse
Dimensional Mannequin on the Lakehouse

The design must be simple to know and environment friendly with totally different question patterns on the info. From the mannequin, we designed the gross sales truth desk to reply our enterprise questions; as you possibly can see, apart from the overseas keys (FKs) to the size, it solely comprises the numeric metrics used to measure the enterprise, e.g. sales_amount.

We additionally designed dimension tables similar to Product, Retailer, Buyer, Date that present contextual info on the actual fact information. Dimension tables are usually joined with truth tables to reply particular enterprise questions, similar to the preferred merchandise for a given month, which shops are the best-performing ones for the quarter, and so forth.

3. Finest practices and suggestions for dimensional modeling

With the Databricks Lakehouse Platform, one can simply design & implement dimensional fashions, and easily construct the details and dimensions for the given topic space.

Beneath are among the finest practices advisable whereas implementing a dimensional mannequin:

  • One ought to denormalize the dimension tables. As a substitute of the third regular kind or snowflake kind of mannequin, dimension tables usually are extremely denormalized with flattened many-to-one relationships inside a single dimension desk.
  • Use conformed dimension tables when attributes in several dimension tables have the identical column names and area contents. This benefit is that information from totally different truth tables will be mixed in a single report utilizing conformed dimension attributes related to every truth desk.
  • A typical development in dimension tables is round monitoring adjustments to dimensions over time to assist as-is or as-was reporting. You may simply apply the next fundamental methods for dealing with dimensions based mostly on totally different necessities.
    • The sort 1 approach overwrites the dimension attribute’s preliminary worth.
    • With the kind 2 approach, the most typical SCD approach, you utilize it for correct change monitoring over time.

    This may be simply achieved out of the field with Delta Dwell Tables implementation.

    • One can simply carry out SCD kind 1 or SCD kind 2 utilizing Delta Dwell Tables utilizing APPLY CHANGES INTO
  • Major + International Key Constraints permit finish customers like yourselves to know relationships between tables.
  • Utilization of IDENTITY Columns mechanically generates distinctive integer values when new rows are added. Id columns are a type of surrogate keys. Seek advice from the weblog hyperlink for extra particulars.
  • Enforced CHECK Constraints to by no means fear about information high quality or information correctness points sneaking up on you.

4. Implementing a dimensional mannequin in a Databricks Lakehouse

Now, allow us to have a look at an instance of Delta Dwell Tables based mostly dimensional modeling implementation:

The instance code under exhibits us tips on how to create a dimension desk (dim_store) utilizing SCD Sort 2, the place change information is captured from the supply system.


-- create the gold desk
CREATE INCREMENTAL LIVE TABLE dim_store
TBLPROPERTIES ("high quality" = "gold")
COMMENT "Slowly Altering Dimension Sort 2 for retailer dimension within the gold layer";

-- retailer all adjustments as SCD2
APPLY CHANGES INTO stay.dim_store
FROM STREAM(stay.silver_store)
  KEYS (store_id)
  SEQUENCE BY updated_date
  COLUMNS * EXCEPT (_rescued_data, input_file_name)
  STORED AS SCD TYPE 2;

The instance code under exhibits us tips on how to create a truth desk (fact_sale), with the constraint of valid_product_id we’re in a position to make sure all truth data which are loaded have a legitimate product related to it.


-- create the actual fact desk for gross sales in gold layer
CREATE STREAMING LIVE TABLE fact_sale (
  CONSTRAINT valid_store_business_key EXPECT (store_business_key IS NOT NULL) ON VIOLATION DROP ROW,
  CONSTRAINT valid_product_id EXPECT (product_id IS NOT NULL) ON VIOLATION DROP ROW
) 
TBLPROPERTIES ("high quality" = "gold", "ignoreChanges" = "true")
COMMENT "gross sales truth desk within the gold layer" AS
  SELECT
    sale.transaction_id,
    date.date_id,
    buyer.customer_id,
    product.product_id AS product_id,
    retailer.store_id,
    retailer.business_key AS store_business_key,
    sales_amount
  FROM STREAM(stay.silver_sale) sale
  INNER JOIN stay.dim_date date
  ON to_date(sale.transaction_date, 'M/d/yy') = to_date(date.date, 'M/d/yyyy') 
  -- solely be a part of with the lively prospects
  INNER JOIN (SELECT * FROM stay.dim_customer WHERE __END_AT IS NULL) buyer
  ON sale.customer_id = buyer.customer_id
  -- solely be a part of with the lively merchandise
  INNER JOIN (SELECT * FROM stay.dim_product WHERE __END_AT IS NULL) product
  ON sale.product = product.SKU
  -- solely be a part of with the lively shops
  INNER JOIN (SELECT * FROM stay.dim_store WHERE __END_AT IS NULL) retailer
  ON sale.retailer = retailer.business_key

The Delta Dwell Desk pipeline instance could possibly be discovered right here. Please discuss with Delta Dwell Tables quickstart on tips on how to create a Delta Dwell Desk pipeline. As seen under, DLT affords full visibility of the ETL pipeline and dependencies between totally different objects throughout bronze, silver, and gold layers following the lakehouse medallion structure.

End to End DLT Pipeline
Finish to Finish DLT Pipeline

Right here is an instance of how the dimension desk dim_store will get up to date based mostly on the incoming adjustments. Beneath, the Retailer Brisbane Airport was up to date to Brisbane Airport V2, and with the out-of-box SCD Sort 2 assist, the unique document ended on Jan 07 2022, and a brand new document was created which begins on the identical day with an open finish date (NULL) – which signifies the newest document for the Brisbane airport.

SCD Type 2 for Store Dimension
SCD Sort 2 for Retailer Dimension

For extra implementation particulars, please discuss with right here for the total pocket book instance.

5. Conclusion

On this weblog, we discovered about dimensional modeling ideas intimately, finest practices, and tips on how to implement them utilizing Delta Dwell Tables.

Be taught extra about dimensional modeling at Kimball Know-how.

Get began on constructing your dimensional fashions within the Lakehouse

Attempt Databricks free for 14 days.

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