@Rongali_Vasu, @gowri_databrick
While waiting for Lakeflow Connect connectors for MySQL, Oracle, or PostgreSQL to become GA, one alternative is to use JDBC connections from Databricks. This can work with most RDBMS platforms that provide a JDBC driver.
For example, you can use PySpark to read from an RDBMS:
jdbc_url = "jdbc:postgresql://<host>:5432/<database>"
connection_properties = {
"user": "<username>",
"password": "<password>",
"driver": "org.postgresql.Driver"
}
df = (
spark.read
.jdbc(
url=jdbc_url,
table="public.customer",
properties=connection_properties
)
)
display(df)
The same approach can be used for other databases such as MySQL or Oracle by changing the JDBC URL and driver.
For production workloads, I would avoid hardcoding credentials and instead use a secret scope, Key Vault, or another supported credential-management approach.
JDBC is not a replacement for all the capabilities that Lakeflow Connect provides, especially around managed ingestion and CDC, but it can be a practical option when you need to connect to an RDBMS before the required connector is generally available.
