Step 1: Set Up a JDBC Connection

1. Install the JDBC Driver

   • Ensure you have the JDBC driver for the external database (e.g., MySQL, Postgres, SQL Server).
   • Place the driver in the Databricks cluster using the %pip install command or by uploading the driver file.

2. Create a JDBC Connection String
   Use the syntax for the database. For example:

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   jdbc:mysql://<hostname>:<port>/<database>?user=<username>&password=<password>

   Replace <hostname>, <port>, <database>, <username>, and <password> with the appropriate values.

3. Read Data from the External Database
   Use the following Databricks command to read data:

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   jdbc_url = "jdbc:mysql://<hostname>:<port>/<database>" properties = { "user": "<username>", "password": "<password>", "driver": "com.mysql.cj.jdbc.Driver" } df = spark.read.jdbc(url=jdbc_url, table="source_table", properties=properties) df.display()

Step 2: Enable Change Data Capture (CDC)

Most databases have built-in CDC capabilities. Here are some examples:

• MySQL: Use binlog with tools like Debezium.
• SQL Server: Enable CDC for the tables with:
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  EXEC sys.sp_cdc_enable_table @source_schema = N'schema_name', @source_name = N'table_name', @role_name = NULL;
• Postgres: Use logical replication slots or tools like Debezium.

Step 3: Stream Data into Databricks

1. Set Up a Streaming Framework
   Use a tool like Apache Kafka, AWS DMS, or a Databricks partner integration to stream data into Databricks.

2. Configure Delta Live Tables (DLT)
   Create a Delta Live Table pipeline for CDC using the Databricks SQL syntax.

   Example:

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   CREATE OR REFRESH STREAMING LIVE TABLE cdc_table AS SELECT * FROM ( SELECT *, _change_type FROM cloud_files( "/path/to/cdc/files", "json", map("mergeSchema", "true") ) ) WHERE _change_type IN ('insert', 'update', 'delete');

3. Merge Updates into Delta Table
   Use the MERGE INTO command to handle inserts, updates, and deletions:

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   MERGE INTO target_table t USING cdc_table c ON t.id = c.id WHEN MATCHED AND c._change_type = 'update' THEN UPDATE SET * WHEN MATCHED AND c._change_type = 'delete' THEN DELETE WHEN NOT MATCHED THEN INSERT *;

Step 4: Schedule Synchronization

• Use Databricks Workflows to schedule your DLT pipelines to run periodically and keep the target data synchronized.

Step 5: Monitor and Optimize

• Monitor the pipeline using the Databricks Jobs and DLT dashboard for errors and performance issues.
• Optimize the process by using partitioning, Z-order indexing, and caching.

Helpful Resources

• Databricks JDBC Documentation
• Delta Live Tables Documentation
• Change Data Capture (CDC) on Databricks

This approach allows you to maintain a replica of your external database in Databricks with minimal latency and high accuracy.