Configure Cloud Object Storage (GCS, S3, Azure) for Data Lake

Ilum allows you to link GCS, S3, WASBS, and HDFS storages to your clusters. Linking storage allows Ilum to automatically configure all your jobs to use your cloud data lakes seamlessly, eliminating the need for manual Spark parameter configuration.

Supported Storage Providers

Provider	Type	Description
Google Cloud Storage	GCS	Native integration for GCP projects.
Amazon S3	S3	Standard S3 and S3-compatible storage support.
Azure Blob Storage	WASBS/ABFS	Integration for Azure data lakes.
HDFS	HDFS	Connect to existing Hadoop Distributed File Systems.

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Google Cloud Storage (GCS)

Google Cloud Storage (GCS)

Step 1: Create a GCS Bucket

Demo:

Guide In Full Screen

1. Create a Google Cloud Project

   • Open Google Cloud Console and go to Project Selector / Manage Resources.
   • Click New Project / Create Project.
   • Enter a Project name, choose Organization and Location.

2. Create a GCS Bucket

   • In the Console, navigate to Cloud Storage → Buckets.
   • Click Create.
   • Enter a globally unique Bucket name (e.g., my-ilum-bucket) and select your Region.
   note

   Remember the bucket name you created - you will need it when adding this storage to Ilum.

3. Create a Service Account and JSON Key

   • Go to IAM & Admin → Service Accounts.
   • Click Create Service Account, fill in details, and grant Storage Admin roles.
   • Click the created email, go to the Keys tab, and Create new key (JSON).
   • Save the downloaded JSON file securely.
   important

   Organization Policy Update: In new organizations, creating service account keys might be disabled by default. Contact your administrator if you cannot create keys.

Step 2: Add GCS to Ilum Cluster

Demo:

Guide in Full Screen

1. Navigate to Workloads → Clusters → Edit → Storage → Add Storage.

2. Configure General Settings:

Parameter	Value Example	Description
Name	my-gcs-storage	Unique name for this storage config.
Type	GCS	Select GCS provider.
Spark Bucket	my-ilum-bucket	Bucket for Spark logs/events.
Data Bucket	my-ilum-bucket	Bucket for your data.

3. Configure GCS Authorization: Open your JSON key file and copy the values:

Parameter	Source Key	Description
Client Email	client_email	Service account email address.
Private Key	private_key	Full key including -----BEGIN....
Private Key ID	private_key_id	Key ID string.

4. Click Submit to save.

Amazon S3

Amazon S3

info

The process for adding S3 storage is nearly identical to GCS. You will need to provide your AWS credentials (Access Key and Secret Key) instead of a JSON key file.

1. Navigate to Workloads → Clusters → Edit → Storage → Add Storage.
2. Select S3 as the Type.
3. Fill in the required fields:

Parameter	Description
Name	Unique name for this storage config.
Access Key	Your AWS Access Key ID.
Secret Key	Your AWS Secret Access Key.
Region	AWS Region of your bucket (e.g., us-east-1).
Endpoint	(Optional) Custom endpoint for S3-compatible storage (e.g., MinIO).

Azure Blob Storage

Azure Blob Storage

info

The process for adding Azure storage is nearly identical to GCS and S3. You will need your Azure Storage Account Name and Access Key.

1. Navigate to Workloads → Clusters → Edit → Storage → Add Storage.
2. Select Azure (or WASBS) as the Type.
3. Fill in the required fields:

Parameter	Description
Name	Unique name for this storage config.
Account Name	Your Azure Storage Account name.
Account Key	Your Azure Storage Account Access Key.
Container	Name of the container to use.

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Step 3: Verify Connection

To ensure your storage is correctly configured, run a simple Spark job.

1. Create a Code Service:

   • Go to Workloads → Services → New Service +.
   • Select Type: Code, Language: Scala, and your Cluster.

2. Execute Test Code: Paste and run the following Scala code:

   Test Storage Connection

   // Write test data
   val data = Seq(("Alice", 34), ("Bob", 45))
   val df = spark.createDataFrame(data).toDF("name", "age")
   
   // Replace with your bucket path (e.g., gs://..., s3a://..., wasbs://...)
   val path = "gs://my-ilum-bucket/output/" 
   
   df.write.mode("overwrite").format("csv").save(path)
   
   // Read back data
   spark.read.format("csv").load(path).show()

3. Check Results: If the job completes and displays the data table, your storage connection is active.

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Common Issues & FAQ

Why do I get a "Permission Denied" error?

Cause: The Service Account or User doesn't have permissions to access the bucket. Solution:

1. Go to your cloud provider's console (e.g., Google Cloud Console).
2. Navigate to the bucket's Permissions tab.
3. Grant your service account the Storage Admin or Storage Object Admin role.

Why does it say "Bucket does not exist"?

Cause: The bucket name in your code doesn't match the actual bucket name, or the region is incorrect. Solution:

1. Verify the bucket exists in your cloud console.
2. Check that the bucket name in your code matches exactly (names are often case-sensitive).

Why do I get "Invalid credentials"?

Cause: The keys (JSON or Access Keys) were not copied correctly. Solution:

1. Re-open your key file.
2. Carefully copy the values again. For GCS, ensure you include the -----BEGIN PRIVATE KEY----- and -----END PRIVATE KEY----- lines.
3. Re-save the storage configuration in Ilum.