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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 https://s3.eu-west-1.peasoup.cloud
  • Databricks workspace with access to configure external data sources.

Steps to Connect PeaSoup S3 to Databricks

  1. Log into Databricks:
    • Open your Databricks workspace by going to the Databricks portal and logging in with your credentials.
  2. 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.eu-west-1.peasoup.cloud”) 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.
  3. 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/”)
  4. 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/”)
  5. 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.
  6. 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.