Table of Contents
How to Connect PeaSoup S3 to Databricks #
Follow these steps to configure Databricks to access and store data on PeaSoup S3. Since PeaSoup uses an S3-compatible API, you can easily integrate it as a data source and storage backend for your Databricks workflows.
Prerequisites #
- PeaSoup S3 Access: Ensure you have the following information:
- Access key
- Secret key
- Bucket name
- PeaSoup S3 endpoint URL (e.g.,
https://s3.pscloud.io
)
- Databricks workspace with access to configure external data sources.
Steps to Connect PeaSoup S3 to Databricks #
- Log into Databricks:
- Open your Databricks workspace by going to the Databricks portal and logging in with your credentials.
- Set Up AWS S3 Credentials for PeaSoup:
- In Databricks, navigate to your notebook or cluster settings.
- Set the following environment variables with your PeaSoup S3 credentials:spark.conf.set(“fs.s3a.access.key”, “your-access-key”) spark.conf.set(“fs.s3a.secret.key”, “your-secret-key”) spark.conf.set(“fs.s3a.endpoint”, “https://s3.pscloud.io”) spark.conf.set(“fs.s3a.path.style.access”, “true”)
- These settings will allow Databricks to authenticate and interact with PeaSoup S3 via the S3A protocol.
- Access PeaSoup S3 Data:
- Once the credentials are configured, you can read and write data to PeaSoup S3 using Databricks notebooks.
- For example, to read a CSV file from PeaSoup S3:val df = spark.read.option(“header”, “true”) .csv(“s3a://your-bucket-name/path-to-your-file.csv”) df.show()
- To write data to PeaSoup S3:df.write.mode(“overwrite”).csv(“s3a://your-bucket-name/output-path/”)
- Configure PeaSoup S3 for Delta Lake (Optional):
- Databricks integrates with Delta Lake for optimized storage. You can use PeaSoup S3 as a storage backend for Delta Lake by specifying the S3 path.
- To read and write Delta Lake data to PeaSoup S3:df.write.format(“delta”).save(“s3a://your-bucket-name/delta-lake-output/”) val deltaDF = spark.read.format(“delta”).load(“s3a://your-bucket-name/delta-lake-output/”)
- Secure PeaSoup S3 with IAM Roles (Optional):
- For enhanced security, you can use IAM roles to authenticate with PeaSoup S3 instead of hardcoding access keys. This is especially useful if PeaSoup supports IAM-like functionality for access control.
- In the Databricks workspace, configure the cluster to assume an IAM role that has the necessary permissions for the PeaSoup S3 bucket.
- Test and Monitor Data Access:
- Once the setup is complete, run tests to read from and write to PeaSoup S3 via Databricks to ensure that everything is working as expected.
- Use Databricks’ monitoring tools to track job progress and data access performance when interacting with PeaSoup S3.
Optional: Tuning Performance for S3 Access #
- For improved performance when working with large datasets, you can adjust some of the S3A connector settings:
- spark.hadoop.fs.s3a.connection.maximum: Increase the maximum number of connections for high throughput.
- spark.hadoop.fs.s3a.multipart.size: Adjust the multipart upload size for larger data transfers.
- spark.hadoop.fs.s3a.fast.upload: Enable faster uploads for large files.
Notes #
- Ensure that the network and firewall settings on PeaSoup S3 allow access from Databricks to the specified S3 endpoint.
- PeaSoup’s S3-compatible API makes it easy to use with Databricks, enabling cloud-based data processing and storage for advanced analytics and machine learning workflows.