Your cloud data zone
Receive versioned Sage data in Amazon S3, Azure Data Lake Storage Gen2, or Google Cloud Storage.
AWS · Azure · Google Cloud
Connect once.
Query anywhere.
Bring Sage into existing applications and analytics without re-platforming. Discover governed data through your catalog, query it with SQL, call it through APIs, or subscribe to material events through messaging.
MULTI-CLOUD DATA PATHYOUR CLOUD ACCOUNT
Sage managed deliveryPARQUET · ICEBERG · MANIFEST
↓ SCOPED CLOUD IDENTITY
S3 · ADLS GEN2 · CLOUD STORAGEENCRYPTED · VERSIONED · PARTITIONED
↓ DISCOVER / QUERY / MODEL
AWS · AZURE · GOOGLE CLOUD ANALYTICSYOUR COMPUTE
CatalogDiscover governed tables and schemas
SQLQuery with the engines you already use
APIsEmbed intelligence in existing applications
MessagingSubscribe to real-time material events
Use the services you operate
One governed source across every cloud.
Sage supplies consistent identifiers, schemas, timestamps, and evidence so each platform can focus on the workload—not data repair.
Native analytics
Query with Athena or Redshift, Fabric or Synapse, and BigQuery—without rebuilding the financial data foundation.
Large-scale processing
Use EMR, Azure Databricks, Google Cloud Dataproc, or your preferred Spark runtime over open tables.
Governed discovery
Publish consistent schemas through Glue, Microsoft Purview, Dataplex, or an Iceberg REST Catalog.
Model-ready features
Build point-in-time training sets for SageMaker, Azure Machine Learning, Vertex AI, and agentic workflows.
Operational automation
Route delivery and intelligence events through EventBridge, Azure Event Grid, Pub/Sub, queues, and functions.
Aligned to your cloud controls.
Connection surfacesUse catalog endpoints, SQL, REST APIs, webhooks, or messaging without changing the surrounding application.
IdentityScoped IAM roles, Azure managed identities, or GCP service accounts with least-privilege access.
EncryptionTransport encryption and customer-managed keys through AWS KMS, Azure Key Vault, or Cloud KMS.
Network posturePrivate destination patterns and service-controlled access without exposing analytical stores publicly.
Catalog designStable dataset names, partition conventions, column documentation, and schema version metadata.
Cost controlColumnar formats, partition pruning, incremental delivery, and lifecycle-friendly object organization.