aidp-azuresql
Read or write Azure SQL Database from an AIDP notebook through the AIDP `aidataplatform` Spark format handler. Use when the user mentions Azure SQL, Azure SQL Database, AZURE_SQLSERVER, or a database.windows.net endpoint. Auth is SQL username/password.
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- oracle-samples/oracle-aidp-samples
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- 2026年7月10日 22:17
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SKILL.md
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- aidp-azuresql
- description
- Read or write Azure SQL Database from an AIDP notebook through the AIDP `aidataplatform` Spark format handler. Use when the user mentions Azure SQL, Azure SQL Database, AZURE_SQLSERVER, or a database.windows.net endpoint. Auth is SQL username/password.
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# `aidp-azuresql` — Azure SQL Database via AIDP `aidataplatform`
Use the dedicated Azure SQL connector type, `AZURE_SQLSERVER`. It supports ingestion reads and writes, external-catalog access, `catalog.id`, and SQL pushdown.
## When to use
- Read or write Azure SQL Database from an AIDP notebook.
- Mentioned: "Azure SQL", "Azure SQL Database", or `database.windows.net`.
## When NOT to use
- For self-managed Microsoft SQL Server → [`aidp-sqlserver`](../aidp-sqlserver/SKILL.md).
- For Azure Data Lake Storage → [`aidp-azure-adls`](../aidp-azure-adls/SKILL.md).
## Ingestion read
```python
import os
from oracle_ai_data_platform_connectors.aidataplatform import (
AIDP_FORMAT, aidataplatform_options,
)
opts = aidataplatform_options(
type="AZURE_SQLSERVER",
host=os.environ["AZURE_SQL_HOST"],
port=int(os.environ.get("AZURE_SQL_PORT", "1433")),
database_name=os.environ["AZURE_SQL_DATABASE"],
user=os.environ["AZURE_SQL_USER"],
password=os.environ["AZURE_SQL_PASSWORD"],
schema=os.environ.get("AZURE_SQL_SCHEMA", "dbo"),
table=os.environ["AZURE_SQL_TABLE"],
)
df = spark.read.format(AIDP_FORMAT).options(**opts).load()
df.show(5)
```
## Ingestion write
`CREATE`, `APPEND`, `OVERWRITE`, and `MERGE` are supported. `write.merge.keys` is required for `MERGE`.
```python
write_opts = aidataplatform_options(
type="AZURE_SQLSERVER",
host=os.environ["AZURE_SQL_HOST"],
port=int(os.environ.get("AZURE_SQL_PORT", "1433")),
database_name=os.environ["AZURE_SQL_DATABASE"],
user=os.environ["AZURE_SQL_USER"],
password=os.environ["AZURE_SQL_PASSWORD"],
schema=os.environ.get("AZURE_SQL_SCHEMA", "dbo"),
table=os.environ["AZURE_SQL_TARGET_TABLE"],
extra={"write.mode": "CREATE"},
)
df.write.format(AIDP_FORMAT).options(**write_opts).save()
```
## External catalog and `catalog.id`
Create an Azure SQL external catalog in **Master Catalogs** first. Use a three-part name for catalog reads and writes, or `catalog.id` to reuse its saved connection.
```python
catalog_df = spark.table("<CATALOG_NAME>.<SCHEMA>.<TABLE_NAME>")
catalog_df.show(5)
catalog_id_df = (spark.read.format(AIDP_FORMAT)
.option("catalog.id", "<CATALOG_ID>")
.option("schema", "<SCHEMA>")
.option("table", "<TABLE_NAME>")
.load())
catalog_id_df.write.format(AIDP_FORMAT) \
.option("catalog.id", "<CATALOG_ID>") \
.option("schema", "<SCHEMA>") \
.option("table", "<TARGET_TABLE_NAME>") \
.option("write.mode", "APPEND") \
.save()
```
## Pushdown SQL
```python
pushdown_df = (spark.read.format(AIDP_FORMAT)
.options(**opts)
.option("pushdown.sql", "SELECT TOP 10 * FROM <SCHEMA>.<TABLE_NAME>")
.load())
pushdown_df.show(5)
```
## Gotchas
- Use `AZURE_SQLSERVER`, not `SQLSERVER`, for Azure SQL Database.
- Azure SQL normally uses port `1433`; pass the fully-qualified `*.database.windows.net` host.
- `schema` is usually `dbo`; `database.name` is the Azure SQL database.
- AIDP needs egress to the Azure SQL endpoint. Configure Azure firewall rules for the AIDP network path.
## References
- Official sample: [AzureSQL notebook](https://github.com/oracle-samples/oracle-aidp-samples/blob/main/data-engineering/ingestion/Read_Write_External_Ecosystem_Connectors/AzureSQL.ipynb)
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