How to load data from Redshift to Databricks Lakehouse
Learn how to use Airbyte to synchronize your Redshift data into Databricks Lakehouse within minutes.
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How to Sync to Manually
Step 1: Export Data from Redshift to S3
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;
```
Step 2: Transfer Data from S3 to Databricks
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"}
)
```
Step 3: Import Data into Databricks Lakehouse
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'
```
Step 4: Validate and Clean Up
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.
Step 5: Schedule Data Refresh (Optional)
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.