How to load data from Redshift to Databricks Lakehouse

1. Create an IAM Role for Redshift:

- Go to AWS IAM and create a new role.

- Attach policies that allow Redshift to access S3 (AmazonS3FullAccess or a more restrictive custom policy if needed).

- Attach this role to your Redshift cluster.

2. Create an S3 Bucket:

- Go to the AWS S3 service and create a new bucket to store the exported data.

3. Export Data from Redshift:

- Use the `UNLOAD` command in Redshift to export the data to the S3 bucket.

- Make sure to choose a suitable file format (CSV, AVRO, Parquet, etc.) for the exported data.

- Example SQL command:

```sql

UNLOAD ('SELECT * FROM your_table')

TO 's3://your-bucket-name/exported-data-prefix'

IAM_ROLE 'arn:aws:iam::your-account-id:role/your-redshift-role'

FORMAT AS PARQUET;

```

1. Configure Databricks CLI:

- Install the Databricks CLI on your local machine.

- Configure it using the Databricks workspace token.

2. Mount the S3 Bucket to Databricks:

- Use Databricks to mount the S3 bucket as a DBFS (Databricks File System) mount point.

- Use the `dbutils.fs.mount` command in a Databricks notebook:

```python

dbutils.fs.mount(

source = "s3a://your-bucket-name",

mount_point = "/mnt/your-mount-point",

extra_configs = {"fs.s3a.access.key": "your-access-key", "fs.s3a.secret.key": "your-secret-key"}

)

```

1. Create a Databricks Notebook:

- Create a new Databricks notebook to perform the data import.

2. Read Data from the Mounted S3 Bucket:

- Use the Databricks DataFrame API to read the data from the DBFS mount point.

- For example, if you exported the data in Parquet format:

```python

df = spark.read.parquet("/mnt/your-mount-point/exported-data-prefix")

```

3. Write Data to Databricks Lakehouse:

- Decide on the target location in Databricks Lakehouse (Delta Lake).

- Write the DataFrame to the Delta Lake using the DataFrame API.

- Example to write data as a Delta table:

```python

df.write.format("delta").save("/mnt/your-delta-table-path")

```

4. Create a Table:

- Optionally, you can create a table that references the Delta files.

- Use SQL commands in the Databricks notebook:

```sql

CREATE TABLE your_table_name

USING DELTA

LOCATION '/mnt/your-delta-table-path'

```

1. Validate Data:

- Run queries against the new table or DataFrame to ensure the data has been transferred correctly.

- Compare record counts and sample data between Redshift and Databricks.

2. Unmount S3 Bucket (Optional):

- If you no longer need the S3 bucket mounted to Databricks, unmount it.

- Use the `dbutils.fs.unmount` command in a Databricks notebook:

```python

dbutils.fs.unmount("/mnt/your-mount-point")

```

3. Clean Up S3 Bucket:

- Remove the exported data from the S3 bucket if it's no longer needed.

1. Automate Data Transfer:

- If you need to move data regularly, consider automating the process.

- Use AWS Data Pipeline, AWS Lambda, or other AWS services to schedule and run the Redshift `UNLOAD` command.

- Use Databricks Jobs to schedule data import into Databricks Lakehouse.

By following these steps, you can successfully transfer data from Amazon Redshift to Databricks Lakehouse without the need for third-party connectors or integrations. Always ensure that you are following best practices for security and data governance when transferring data between systems.