databricks-scd-maye-msft 0.0.1
Slowly Changing Dimensions implemenation with Databricks Delta Lake
Requires Python: >=3.6

# Slowly Changing Dimensions implemenation with Databricks Delta Lake

## What is Slowly Changing Dimension

Slowly Changing Dimensions (SCD) are dimensions which change over time and in Data Warehuse we need to track the changes of the attributes keep the accuracy of the report.

And typically there are three types of SCD

- Type 1: SCD1, No history preservation
- Type 2: SCD2, Unlimited history preservation and new rows
- Type 3: SCD3, Limited history preservation

For example we have a dataset

ShortName | Fruit | Color | Price
--------- | ----- | ----- | -----
FA | Fiji Apple | Red | 3.6
BN | Banana | Yellow | 1
GG | Green Grape | Green | 2
RG | Red Grape | Red | 2

If we change the price of "Fiji Apple" into 3.5, the dataset will be 

with SCD1:

ShortName | Fruit | Color | Price
--------- | ----- | ----- | -----
FA | Fiji Apple | Red | 3.5
BN | Banana | Yellow | 1
GG | Green Grape | Green | 2
RG | Red Grape | Red | 2

with SCD2:

ShortName | Fruit | Color | Price | is_last
--------- | ----- | ----- | ----- | -------
FA | Fiji Apple | Red | 3.5 | Y
FA | Fiji Apple | Red | 3.6 | N
BN | Banana | Yellow | 1 | Y
GG | Green Grape | Green | 2 | Y
RG | Red Grape | Red | 2 | Y

with SCD3:

ShortName | Fruit | Color | Price | Color_old | Price_old 
--------- | ----- | ----- | ----- | --------- | --------- 
FA | Fiji Apple | Red | 3.5 | Red | 3.6
BN | Banana | Yellow | 1 | NULL | NULL
GG | Green Grape | Green | 2 | NULL | NULL
RG | Red Grape | Red | 2 | NULL | NULL

## SCD implementation in Databricks

In this repository, there are [implementations](./SCD.py) of SCD1, SCD2 and SCD3 in python and Databricks Delta Lake.

1. SCD1

```python
SCD1(df, target_table_name, target_partition_keys, key_cols, current_time):
```

Parameters:

- df: source dataframe
- target_table_name: target table name 
- target_partition_keys: partition key of the target table
- key_cols: columns of the key for each row
- current_time: current timestamp

Here is the code to show an example of SCD1

```python
from pyspark.sql import functions as F
from pyspark.sql import DataFrame
import datetime

# create sample dataset
df1 = spark.createDataFrame([('FA', 'Fiji Apple', 'Red', 3.5)
                           ,('BN', 'Banana', 'Yellow', 1.0)
                           ,('GG', 'Green Grape', 'Green', 2.0)
                           ,('RG', 'Red Grape', 'Red', 2.0)], 
                           ['ShortName','Fruit', 'Color', 'Price'])
# prepare parameters
current_time = datetime.datetime.now().strftime("%Y-%m-%dT%H:%M:%S")
target_partition_keys = ['ShortName']
key_cols = "ShortName,Fruit"
target_table_name_scd1 = 'default.table_scd1'
# call the SCD1 function
SCD1(df1, target_table_name_scd1, target_partition_keys, key_cols, current_time)
# display the result
display(spark.sql(f"select * from {target_table_name_scd1}"))
```

![Image of SCD1](images/SCD1-1.png)

Change the price of "Fiji Apple" into 3.5 and run SCD1 again

```python
df2 = spark.createDataFrame([('FA', 'Fiji Apple', 'Red', 3.6)
                           ,('BN', 'Banana', 'Yellow', 1.0)
                           ,('GG', 'Green Grape', 'Green', 2.0)
                           ,('RG', 'Red Grape', 'Red', 2.0)], 
                           ['ShortName','Fruit', 'Color', 'Price'])
# call the SCD1 function again
SCD1(df2, target_table_name_scd1, target_partition_keys, key_cols, current_time)
display(spark.sql(f"select * from {target_table_name_scd1}"))
```

![Image of SCD1](images/SCD1-2.png)

1. SCD2

```python
SCD2(df, target_table_name, target_partition_keys, key_cols, current_time):
```

Parameters:

- df: source dataframe
- target_table_name: target table name 
- target_partition_keys: partition key of the target table
- key_cols: columns of the key for each row
- current_time: current timestamp

Here is the code to show an example of SCD2

```python
from pyspark.sql import functions as F
from pyspark.sql import DataFrame
import datetime

# create sample dataset
df1 = spark.createDataFrame([('FA', 'Fiji Apple', 'Red', 3.5)
                           ,('BN', 'Banana', 'Yellow', 1.0)
                           ,('GG', 'Green Grape', 'Green', 2.0)
                           ,('RG', 'Red Grape', 'Red', 2.0)], 
                           ['ShortName','Fruit', 'Color', 'Price'])
# prepare parameters
current_time = datetime.datetime.now().strftime("%Y-%m-%dT%H:%M:%S")
target_partition_keys = ['ShortName']
key_cols = "ShortName,Fruit"
target_table_name_scd1 = 'default.table_scd2'
# call the SCD1 function
SCD2(df1, target_table_name_scd1, target_partition_keys, key_cols, current_time)
# display the result
display(spark.sql(f"select * from {target_table_name_scd2}"))
```

![Image of SCD1](images/SCD2-1.png)

Change the price of "Fiji Apple" into 3.5 and run SCD2 again

```python
df2 = spark.createDataFrame([('FA', 'Fiji Apple', 'Red', 3.6)
                           ,('BN', 'Banana', 'Yellow', 1.0)
                           ,('GG', 'Green Grape', 'Green', 2.0)
                           ,('RG', 'Red Grape', 'Red', 2.0)], 
                           ['ShortName','Fruit', 'Color', 'Price'])
# call the SCD1 function again
SCD2(df2, target_table_name_scd2, target_partition_keys, key_cols, current_time)
display(spark.sql(f"select * from {target_table_name_scd2}"))
```

![Image of SCD1](images/SCD2-2.png)

1. SCD3

```python
SCD3(df, target_table_name, target_partition_keys, key_cols, current_time):
```

Parameters:

- df: source dataframe
- target_table_name: target table name 
- target_partition_keys: partition key of the target table
- key_cols: columns of the key for each row
- current_time: current timestamp

Here is the code to show an example of SCD3

```python
from pyspark.sql import functions as F
from pyspark.sql import DataFrame
import datetime

# create sample dataset
df1 = spark.createDataFrame([('FA', 'Fiji Apple', 'Red', 3.5)
                           ,('BN', 'Banana', 'Yellow', 1.0)
                           ,('GG', 'Green Grape', 'Green', 2.0)
                           ,('RG', 'Red Grape', 'Red', 2.0)], 
                           ['ShortName','Fruit', 'Color', 'Price'])
# prepare parameters
current_time = datetime.datetime.now().strftime("%Y-%m-%dT%H:%M:%S")
target_partition_keys = ['ShortName']
key_cols = "ShortName,Fruit"
target_table_name_scd1 = 'default.table_scd3'
# call the SCD1 function
SCD3(df1, target_table_name_scd3, target_partition_keys, key_cols, current_time)
# display the result
display(spark.sql(f"select * from {target_table_name_scd3}"))
```

![Image of SCD1](images/SCD3-1.png)

Change the price of "Fiji Apple" into 3.5 and run SCD3 again

```python
df2 = spark.createDataFrame([('FA', 'Fiji Apple', 'Red', 3.6)
                           ,('BN', 'Banana', 'Yellow', 1.0)
                           ,('GG', 'Green Grape', 'Green', 2.0)
                           ,('RG', 'Red Grape', 'Red', 2.0)], 
                           ['ShortName','Fruit', 'Color', 'Price'])
# call the SCD1 function again
SCD3(df2, target_table_name_scd3, target_partition_keys, key_cols, current_time)
display(spark.sql(f"select * from {target_table_name_scd3}"))
```

![Image of SCD1](images/SCD3-2.png)