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

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Dimensional modeling is among the hottest information modeling strategies for constructing a contemporary information warehouse. It permits clients to shortly develop information and dimensions primarily based on enterprise wants for an enterprise. When serving to clients within the subject, we discovered many are in search of greatest practices and implementation reference structure from Databricks.

On this article, we intention to dive deeper into the most effective follow of dimensional modeling on Databricks’ Lakehouse Platform and supply a dwell 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. Greatest practices and proposals 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 might be introduced and consumed by finish customers. We have to design the info mannequin to assist extra accessible and quicker 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 symbolize enterprise processes. The outlined enterprise downside determines the grain of the very fact information and required dimensions. The important thing thought right here is that we may incrementally construct extra information property with ease primarily based 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 want to workforce to construct a report to provide insights on:

  1. What are the highest promoting merchandise to allow them to perceive product recognition
  2. What are the most effective performing shops to study good retailer practices

2. Design a dimensional mannequin

Primarily based on the outlined enterprise downside, the info mannequin design goals to symbolize the info effectively for reusability, flexibility and scalability. Right here is the high-level information mannequin that might remedy 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 needs to be simple to grasp and environment friendly with completely different question patterns on the info. From the mannequin, we designed the gross sales truth desk to reply our enterprise questions; as you may see, aside from the international 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 data on the very 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. Greatest practices and proposals for dimensional modeling

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

Under are a few of the greatest practices really useful whereas implementing a dimensional mannequin:

  • One ought to denormalize the dimension tables. As an alternative of the third regular type 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 numerous dimension tables have the identical column names and area contents. This benefit is that information from completely different truth tables may be mixed in a single report utilizing conformed dimension attributes related to every truth desk.
  • A regular pattern in dimension tables is round monitoring modifications to dimensions over time to assist as-is or as-was reporting. You may simply apply the next fundamental strategies for dealing with dimensions primarily based on completely different necessities.
    • The kind 1 method overwrites the dimension attribute’s preliminary worth.
    • With the sort 2 method, the commonest SCD method, 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
  • Main + International Key Constraints enable finish customers like yourselves to grasp relationships between tables.
  • Utilization of IDENTITY Columns routinely generates distinctive integer values when new rows are added. Identification 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 primarily based 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 modifications as SCD2
APPLY CHANGES INTO dwell.dim_store
FROM STREAM(dwell.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 ready to make sure all truth information which might be loaded have a legitimate product related to it.


-- create the very 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(dwell.silver_sale) sale
  INNER JOIN dwell.dim_date date
  ON to_date(sale.transaction_date, 'M/d/yy') = to_date(date.date, 'M/d/yyyy') 
  -- solely be part of with the energetic clients
  INNER JOIN (SELECT * FROM dwell.dim_customer WHERE __END_AT IS NULL) buyer
  ON sale.customer_id = buyer.customer_id
  -- solely be part of with the energetic merchandise
  INNER JOIN (SELECT * FROM dwell.dim_product WHERE __END_AT IS NULL) product
  ON sale.product = product.SKU
  -- solely be part of with the energetic shops
  INNER JOIN (SELECT * FROM dwell.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 seek advice from Delta Dwell Tables quickstart on tips on how to create a Delta Dwell Desk pipeline. As seen under, DLT gives full visibility of the ETL pipeline and dependencies between completely 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 primarily based on the incoming modifications. Under, the Retailer Brisbane Airport was up to date to Brisbane Airport V2, and with the out-of-box SCD Sort 2 assist, the unique report ended on Jan 07 2022, and a brand new report was created which begins on the identical day with an open finish date (NULL) – which signifies the most recent report for the Brisbane airport.

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

For extra implementation particulars, please seek advice from right here for the complete pocket book instance.

5. Conclusion

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

Study extra about dimensional modeling at Kimball Expertise.

Get began on constructing your dimensional fashions within the Lakehouse

Attempt Databricks free for 14 days.

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