Detail what the following would look like. Ingest source data into an Azure data lake, do some basic data validation using python and T-SQL. Then load data to a "curated" area of the lake. Landing in the curated area of the lake would trigger loading data to Synapse Analytics.

Here's how you can achieve data ingestion, basic validation, and curated landing in Azure Data Lake Storage (ADLS) with Python and T-SQL, triggering subsequent loading to Synapse Analytics:

1. Ingestion and Validation:
   • Python:
     • Connect to ADLS: Use libraries like azure-storage-blob to connect to ADLS and download the source files.
     • Read and validate data: Use pandas to read the data as DataFrames and perform basic checks:
       • Null values: Check for missing values using isnull() and handle them appropriately (e.g., imputing, dropping rows).
       • Dates: Validate date formats using pd.to_datetime() and ensure consistent formatting.
       • Numeric values: Check for non-numeric values and handle them based on your needs (e.g., converting or dropping rows).
   • T-SQL (within Synapse Analytics):
     • Create external tables: Define external tables in Synapse SQL pointing to the downloaded files in ADLS.
     • Use T-SQL functions: Perform validation checks within the external tables using functions like ISNULL(), TRY_CONVERT(), and ISNUMERIC().
     • Filter or transform data: Apply conditional statements or transformations based on the validation results.
2. Curation and Landing:
   • Python:
     • Transform and prepare data: After validation, use pandas or other libraries to further transform the data as needed (e.g., aggregations, calculations).
     • Upload curated data: Upload the transformed data to a designated "curated" directory within ADLS using azure-storage-blob.
   • T-SQL:
     • Create a staging table: Define a temporary table in Synapse SQL with the desired schema for the curated data.
     • Insert data from external tables: Use INSERT INTO statements with appropriate filters and transformations to populate the staging table from the validated external tables.
     • Clean up: Drop the external tables and staging table after successful data transfer.
3. Triggering Synapse Analytics Load:
   • Azure Data Factory (ADF):
     • Create an ADF pipeline with two activities:
       • Copy Activity: Transfer the curated data from ADLS to a designated landing zone within Synapse Analytics.
       • Synapse Analytics Activity: Execute the data loading process into your target tables using T-SQL or Python scripts.
     • Configure an Event Trigger on the "curated" directory in ADLS to automatically start the pipeline when new data arrives.
   • Azure Functions:
     • Develop an Azure Function triggered by Azure Blob Storage notifications when new data arrives in the "curated" directory.
     • Download the data from ADLS and execute the data loading process into Synapse Analytics using scripts or ADF pipelines.

Comparison:

Feature	Python	T-SQL (Synapse Analytics)
Ingestion & Validation	Flexible, allows complex transformations	Efficient for basic checks
Data Curation	More control over data preparation	Simpler, leverages Synapse tables
Triggering Synapse Load	Requires ADF setup or Azure Functions	Direct integration with Synapse

Choosing the right approach depends on your needs and complexity:

• Python: Ideal for complex data validation, transformations, and flexible workflows.
• T-SQL: Efficient for basic checks and leveraging Synapse features for data curation and loading.

Remember to implement error handling, logging, and monitoring throughout the process for optimal data management and pipeline reliability.