TL;DR

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:

1. set up Redshift as a source connector (using Auth, or usually an API key)
2. set up Databricks Lakehouse as a destination connector
3. define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Redshift

A fully managed data warehouse service in the Amazon Web Services (AWS) cloud, Amazon Redshift is designed for storage and analysis of large-scale datasets. Redshift allows businesses to scale from a few hundred gigabytes to more than a petabyte (a million gigabytes), and utilizes ML techniques to analyze queries, offering businesses new insights from their data. Users can query and combine exabytes of data using standard SQL, and easily save their query results to their S3 data lake.

What is Databricks Lakehouse

Databricks is an American enterprise software company founded by the creators of Apache Spark. Databricks combines data warehouses and data lakes into a lakehouse architecture.

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Prerequisites

1. A Redshift account to transfer your customer data automatically from.
2. A Databricks Lakehouse account.
3. An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including Redshift and Databricks Lakehouse, for seamless data migration.

When using Airbyte to move data from Redshift to Databricks Lakehouse, it extracts data from Redshift using the source connector, converts it into a format Databricks Lakehouse can ingest using the provided schema, and then loads it into Databricks Lakehouse via the destination connector. This allows businesses to leverage their Redshift data for advanced analytics and insights within Databricks Lakehouse, simplifying the ETL process and saving significant time and resources.

Methods to Move Data From Redshift to databricks lakehouse

• Method 1: Connecting Redshift to databricks lakehouse using Airbyte.
• Method 2: Connecting Redshift to databricks lakehouse manually.

Method 1: Connecting Redshift to databricks lakehouse using Airbyte

Step 1: Set up Redshift as a source connector

1. Open the Airbyte UI and navigate to the "Sources" tab.

2. Click on the "Create a new connection" button and select "Redshift" as the source.

3. Enter a name for the connection and click "Next".

4. Enter the necessary credentials for your Redshift database, including the host, port, database name, username, and password.

5. Test the connection to ensure that the credentials are correct and the connection is successful.

6. Select the tables or views that you want to replicate from Redshift to Airbyte.

7. Choose the replication method, either full or incremental, and set any necessary parameters.

8. Click "Create connection" to save the configuration and start the replication process.

9. Monitor the replication progress and troubleshoot any errors that may occur. 10. Once the replication is complete, you can use the data in Airbyte for further analysis or integration with other tools.

Step 2: Set up Databricks Lakehouse as a destination connector

1. First, navigate to the Airbyte website and log in to your account.
2. Once you are logged in, click on the "Destinations" tab on the left-hand side of the screen.
3. Scroll down until you find the "Databricks Lakehouse" connector and click on it.
4. You will be prompted to enter your Databricks Lakehouse credentials, including your account name, personal access token, and workspace ID.
5. Once you have entered your credentials, click on the "Test" button to ensure that the connection is successful.
6. If the test is successful, click on the "Save" button to save your Databricks Lakehouse destination connector settings.
7. You can now use the Databricks Lakehouse connector to transfer data from your source connectors to your Databricks Lakehouse destination.
8. To set up a data transfer, navigate to the "Sources" tab and select the source connector that you want to use.
9. Follow the prompts to enter your source connector credentials and configure your data transfer settings.
10. Once you have configured your source connector, select the Databricks Lakehouse connector as your destination and follow the prompts to configure your data transfer settings.
11. Click on the "Run" button to initiate the data transfer.

Step 3: Set up a connection to sync your Redshift data to Databricks Lakehouse

Once you've successfully connected Redshift as a data source and Databricks Lakehouse as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

1. Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
2. Choose your source: Select Redshift from the dropdown list of your configured sources.
3. Select your destination: Choose Databricks Lakehouse from the dropdown list of your configured destinations.
4. Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
5. Select the data to sync: Choose the specific Redshift objects you want to import data from towards Databricks Lakehouse. You can sync all data or select specific tables and fields.
6. Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
7. Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
8. Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Redshift to Databricks Lakehouse according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your Databricks Lakehouse data warehouse is always up-to-date with your Redshift data.

Method 2: Connecting Redshift to databricks lakehouse manually

Moving data from Amazon Redshift to Databricks Lakehouse can be achieved without third-party connectors by using a combination of native tools and services provided by AWS and Databricks. This guide assumes you have the necessary permissions in both environments and have installed the required CLI tools (AWS CLI, Databricks CLI).

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.

Use Cases to transfer your Redshift data to Databricks Lakehouse

Integrating data from Redshift to Databricks Lakehouse provides several benefits. Here are a few use cases:

1. Advanced Analytics: Databricks Lakehouse’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Redshift data, extracting insights that wouldn't be possible within Redshift alone.
2. Data Consolidation: If you're using multiple other sources along with Redshift, syncing to Databricks Lakehouse allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
3. Historical Data Analysis: Redshift has limits on historical data. Syncing data to Databricks Lakehouse allows for long-term data retention and analysis of historical trends over time.
4. Data Security and Compliance: Databricks Lakehouse provides robust data security features. Syncing Redshift data to Databricks Lakehouse ensures your data is secured and allows for advanced data governance and compliance management.
5. Scalability: Databricks Lakehouse can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Redshift data.
6. Data Science and Machine Learning: By having Redshift data in Databricks Lakehouse, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
7. Reporting and Visualization: While Redshift provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to Databricks Lakehouse, providing more advanced business intelligence options. If you have a Redshift table that needs to be converted to a Databricks Lakehouse table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

1. Configure a Redshift account as an Airbyte data source connector.
2. Configure Databricks Lakehouse as a data destination connector.
3. Create an Airbyte data pipeline that will automatically be moving data directly from Redshift to Databricks Lakehouse after you set a schedule

With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.

We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!