In #azuredatafactory at #transform and #enrich part can be done automatically or manually written by #pyspark two examples below one data source #csv another is #sqlserver with #incrementalloading
Below is a simple end-to-end PySpark code example for a transform and enrich process in Azure Databricks. This example assumes you have a dataset stored in Azure Blob Storage, and you're using Azure Databricks for processing.
```python
# Import necessary libraries
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, lit, concat
# Initialize SparkSession
spark = SparkSession.builder \
.appName("Transform and Enrich Process") \
.getOrCreate()
# Read data from Azure Blob Storage
df = spark.read.csv("wasbs://<container_name>@<storage_account>.blob.core.windows.net/<file_path>", header=True)
# Perform transformations
transformed_df = df.withColumn("new_column", col("old_column") * 2)
# Enrich data
enriched_df = transformed_df.withColumn("enriched_column", concat(col("new_column"), lit("_enriched")))
# Show final DataFrame
enriched_df.show()
# Write enriched data back to Azure Blob Storage
enriched_df.write.mode("overwrite").csv("wasbs://<container_name>@<storage_account>.blob.core.windows.net/<output_path>")
# Stop SparkSession
spark.stop()
```
Remember to replace `<container_name>`, `<storage_account>`, `<file_path>`, and `<output_path>` with your actual Azure Blob Storage container name, storage account name, file path, and output path respectively.
This code reads a CSV file from Azure Blob Storage, performs some transformations (multiplying a column by 2 in this case), enriches the data by adding a new column, displays the final DataFrame, and then writes the enriched data back to Azure Blob Storage.
Here's an updated version of the PySpark code to handle incremental loading, enrichment, and transformation from a SQL Server data source in Azure Databricks:
```python
# Import necessary libraries
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, lit, concat
# Initialize SparkSession
spark = SparkSession.builder \
.appName("Incremental Load, Transform, and Enrich Process") \
.getOrCreate()
# Read data from SQL Server
jdbc_url = "jdbc:sqlserver://<server_name>.database.windows.net:1433;database=<database_name>;user=<username>;password=<password>"
table_name = "<table_name>"
df = spark.read.jdbc(url=jdbc_url, table=table_name)
# Perform transformations
transformed_df = df.withColumn("new_column", col("old_column") * 2)
# Enrich data
enriched_df = transformed_df.withColumn("enriched_column", concat(col("new_column"), lit("_enriched")))
# Show final DataFrame
enriched_df.show()
# Write enriched data back to SQL Server (assuming you have write access)
enriched_df.write.jdbc(url=jdbc_url, table="<target_table_name>", mode="overwrite")
# Stop SparkSession
spark.stop()
```
Replace `<server_name>`, `<database_name>`, `<username>`, `<password>`, `<table_name>`, and `<target_table_name>` with your actual SQL Server connection details, source table name, and target table name respectively.
This code reads data from SQL Server incrementally, performs transformations, enriches the data, displays the final DataFrame, and then writes the enriched data back to SQL Server. Make sure to handle incremental loading logic based on your specific requirements, such as using timestamps or unique identifiers to fetch only new or updated records from the source.
Azure Data Factory (ADF) can handle incremental loading, transformation, and enrichment processes. Here's how you can achieve it using ADF:
1. Incremental Loading:
- Use a Source dataset to connect to your SQL Server database.
- Configure a Source dataset to use the appropriate query or table with a filter condition to fetch only new or updated records since the last execution.
- In the Copy Data activity, enable the "Incremental Copy" option and configure the appropriate settings to determine how to identify new or updated records.
2. Transformation:
- After loading the data into Azure Blob Storage or Azure Data Lake Storage (ADLS), use a Data Flow activity to perform transformations using Spark-based code or graphical transformations in ADF Data Flows.
3. Enrichment:
- Similarly, use Data Flow activities in ADF to enrich the data by joining with other datasets, applying business rules, or adding new columns.
4. Writing Back:
- Once the transformation and enrichment are complete, use a Sink dataset to write the data back to SQL Server or any other desired destination.
Azure Data Factory provides a visual interface for building and orchestrating these activities in a pipeline. You can define dependencies, scheduling, and monitoring within ADF to automate and manage the entire process efficiently.
Remember to consider factors like data volume, frequency of updates, and performance requirements when designing your ADF pipelines. Additionally, ensure that proper error handling and logging mechanisms are in place to handle any issues during the execution.
However remember that, Azure Data Factory (ADF) does not directly support running Azure Databricks notebooks within its pipeline activities. However, you can integrate Azure Databricks with Azure Data Factory to execute Databricks notebooks as part of your data transformation or processing workflows.
Here's a general approach to achieve this integration:
1. Databricks Notebook:
- Develop your data transformation logic using Databricks notebooks in Azure Databricks. Ensure that your notebook is parameterized and can accept input parameters or arguments.
2. ADF Linked Service:
- Create a Databricks Linked Service in ADF to establish a connection with your Databricks workspace. This linked service will contain the necessary authentication information and endpoint details.
3. ADF Notebook Activity:
- Add a Notebook activity in your ADF pipeline. Configure this activity to execute the Databricks notebook stored in your Databricks workspace.
- Provide the required parameters and arguments to the notebook activity if your notebook is parameterized.
4. Triggering:
- Trigger the ADF pipeline based on a schedule, event, or dependency to execute the Databricks notebook as part of your data processing workflow.
5. Monitoring and Logging:
- Monitor the execution of the ADF pipeline and the Databricks notebook through ADF monitoring features and Databricks job logs respectively.
- Implement logging and error handling within your Databricks notebook to capture any issues during execution.
By integrating Azure Databricks with Azure Data Factory in this manner, you can leverage the scalability and processing power of Databricks for your data transformation tasks while orchestrating the overall workflow within ADF.
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