• Microsoft Fabric For B...
This video explores common transformation techniques in Silver and Gold zones that are part of Medallion architecture. I explain data enrichment and type conversion transformations and demonstrate how to use PySpark API's and methods to address these tasks.
I also demonstrate how to process historical data from the Bronze layer using Window functions. Next, I explain core Kimball dimensional modelling concepts and demonstrate how they can be implemented using PySpark methods.
Finally, I demonstrate creating aggregates.
You can download the related demo notebook from here: github.com/faz...
Chapters:
00:00- Introduction
02:21- Preview
06:19- Lakehouse historical data storage strategy
09:00- Demo start- preparing data
10:24- Creating shortcuts to Bronze tables
11:24- Notebook demo- reading data from shortcuts
12:30- Inspecting data frame schema
13:48- Data Type conversion transformations
16:05- Ordering data
20:00- Handling historical data using Window functions
24:25- Data enrichment transformations
25-45- Using regular expressions to parse text data
26:40- Generating time dimension
30:45- Dimensional modelling concepts
32:12- Slowly changing dimensions (SCD)
33:05- SCD Type-2 dimensions
34:54- Surrogate keys
35:32- Relationships between facts and dimensions
37:00- Generating surrogate keys using monotonically_increasing_id function
38:00- Distributed computing and Spark partitions
41:31- Reducing data frame partition count
43:02- How to link Fact and Dimension tables
47:14- Incremental write into destination tables
49:02- Using MERGE INTO query for destination write
50:50- Aggregation transformations
Please subscribe: / @fazizov
Official Documentation:
learn.microsof...
learn.microsof...
sparkbyexample...
www.kimballgro...
spark.apache.o...
Hashtags:
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