Step 1 :
Ensure that your files have a consistent naming or storage pattern. For example, header files can have a naming pattern like header_YYYYMMDD.json
and the corresponding data file as data_YYYYMMDD.csv
.
You can use the OPENROWSET
function to read files in Serverless SQL. Here's an example on how you might read a header and data file:
-- Read header
SELECT *
FROM OPENROWSET(
BULK 'adftutorial/salessmall/header_YYYYMMDD.json',
FORMAT = 'JSON'
) AS headers
-- Read data
SELECT *
FROM OPENROWSET(
BULK 'adftutorial/salessmall/data_YYYYMMDD.csv',
FORMAT = 'CSV',
FIELDTERMINATOR = ',',
ROWTERMINATOR = '\n',
FIRSTROW = 2
) AS data
To deal with the dynamic schema, consider the following:
- Header File as the Schema Source: Treat the header file as a source of truth for your schema. Before querying the data file, parse the header file to determine the structure of the data file.
- Query Generation: Once you have the schema from the header, you can generate the required query dynamically to fetch data based on the schema.
- Schema Evolution: When unioning data files with different schemas, you'll need to identify missing columns in each dataset and then add them with default or null values. This way, you can generate a union-compatible schema for all files.
Step 2 :
To union files, especially when the schema might change, you can perform a union operation with the assumption that the columns might not be the same across all files. You'd likely need a dynamic SQL generation mechanism, perhaps outside of Synapse, to adjust the SELECT
statement columns according to the schema present in each header.
For instance:
- Parse each header file to get a list of columns.
- Use that column list to build a dynamic SQL query for each data file.
- Introduce null values for missing columns.
- Use UNION ALL to combine all datasets.
Don't forget that :
- Dealing with dynamic schemas can introduce complexity, especially if the changes in the schema are frequent or vast.
- Repeatedly generating dynamic SQL, parsing headers for each query, and other operations can lead to performance overheads.
- Depending on the complexity and frequency of schema changes, you might want to consider third-party ETL or data integration tools that can better handle schema drift without manual intervention.