What about the initial data tables and objects needed for ingesting data? Can they be scripted using Python or Azure CLI?

Absolutely! You can script the initial data tables and objects needed for ingesting data in Azure Synapse Analytics using both Python and Azure CLI. Here's a breakdown of each approach:

Python:

1. Creating External Tables: You can use libraries like pyodbc and pymssql to connect to Synapse SQL and execute CREATE TABLE statements defining your external tables. These tables will serve as the landing points for your ingested data from various sources.

import pymssql

# Connect to Synapse SQL
connection = pymssql.connect(server="your_server_name", user="your_username", password="your_password")
cursor = connection.cursor()

# Create an external table for CSV data
cursor.execute("""
CREATE EXTERNAL TABLE my_data_table (
  id INT,
  name VARCHAR(100),
  date DATE
)
WITH (
  LOCATION = '/your/data/path/data.csv',
  FILE_FORMAT = (TYPE = 'CSV', FIELD_QUOTE = '"', FIELD_DELIMITER = ',')
);
""")

# Create another external table for Parquet data
cursor.execute("""
CREATE EXTERNAL TABLE another_data_table (
  id INT,
  price DECIMAL(10, 2),
  category VARCHAR(50)
)
WITH (
  LOCATION = '/your/data/path/data.parquet',
  FILE_FORMAT = (TYPE = 'PARQUET')
);
""")

# Commit changes and close connection
connection.commit()
cursor.close()
connection.close()
                

2. Creating Views: For further data organization or transformation, you can create views in Python using similar SQL statements.

cursor.execute("""
CREATE VIEW filtered_data AS
SELECT * FROM my_data_table WHERE date > '2023-10-01';
""")
                

3. Creating User-Defined Functions (UDFs): If you need complex data transformations, Python allows defining UDFs using libraries like udftools. These UDFs can then be registered in Synapse SQL and used within your data pipelines.

Azure CLI:

1. Using az synapse sql commands: You can directly create external tables and views through the Azure CLI.

az synapse sql table create --name my_data_table --schema your_schema \
--file-format "CSV" --field-delimiter "," --field-quote "\"" \
--location "/your/data/path/data.csv"

az synapse sql table create --name another_data_table --schema your_schema \
--file-format "PARQUET" --location "/your/data/path/data.parquet"

az synapse sql view create --name filtered_data --schema your_schema \
--sql "SELECT * FROM my_data_table WHERE date > '2023-10-01';"
                

2. Leveraging Azure Data Factory (ADF): While not directly scripting, ADF offers a visual interface to create external tables and views within Synapse pipelines. This can be a good option for users less comfortable with command-line tools.

Choosing the right approach:

• Python: Offers greater flexibility and control for complex data transformations and UDF creation. Requires strong Python skills and familiarity with relevant libraries.
• Azure CLI: Easier to learn and execute basic table and view creation. Less customizable for complex transformations.

Remember, regardless of your chosen method, designing your initial data tables and objects efficiently is crucial for a smooth data ingestion process. Consider factors like data format, partitioning, and access permissions to ensure effective data management within your Synapse Analytics environment.