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Overview

Prizm connects to a broad range of data sources, orchestration tools, BI platforms, and third-party systems. Each connector enables Prizm to discover assets, extract lineage, monitor quality, and surface observability signals from that source.

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Supported Sources

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Cloud Data Warehouses & Lakehouses

Snowflake

Deep integration with native query log lineage, query history, and schema discovery.

Databricks

Unified platform support for Delta Lake, Unity Catalog, and Databricks SQL.

BigQuery

Google BigQuery integration with dataset discovery and query-level lineage.

Amazon Redshift

Redshift cluster and Serverless support with usage-based lineage extraction.

SQL Server

Microsoft SQL Server and Azure SQL Database integration.

SAP HANA

SAP HANA in-memory database connectivity for enterprise data assets.

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Cloud Storage

ADLS — Azure Data Lake Storage

Azure Data Lake Storage Gen2 with hierarchical namespace and file-level lineage.

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Data Transformation & Pipeline Tools

dbt

Parse dbt manifests for model-level lineage, tests, and documentation sync.

Apache Airflow

DAG discovery, task-level lineage, and execution metadata from Airflow deployments.

Azure Data Factory

ADF pipeline integration for transformation lineage and execution monitoring.

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BI & Visualization Tools

Tableau

Workbook and datasource discovery with field-level lineage back to source tables.

Power BI

Dataset, report, and dashboard lineage with semantic model integration.

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ITSM & Ticketing

ServiceNow

Auto-create and synchronize incidents and change requests from Prizm quality events.

Jira

Create Jira issues from data quality exceptions and track resolution status.

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Data Catalog & Governance Platforms

Alation

Bidirectional metadata sync with Alation’s data catalog.

Atlan

Integration with Atlan for unified governance and metadata exchange.

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Connector Architecture

All Prizm connectors share a common architecture: 
Data Source
    │
    ├─ Schema Discovery (metadata extraction)
    ├─ Asset Inventory (tables, views, models)
    ├─ Query Log / Audit Log Analysis (lineage)
    ├─ Profiling Execution (JDBC / API)
    └─ Event / Webhook Signals (freshness, failures)

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Connection Types

Connection TypeUse Case
JDBCDirect SQL-based connectivity for warehouse sources
REST APIBI tools, ITSM platforms, and SaaS data sources
WebhooksReal-time event signals from pipelines and orchestrators
Native SDKDeep platform integrations (Snowflake, Databricks, dbt)

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Adding a New Source

1

Navigate to Connections

Go to Settings → Connections in the Prizm UI.
2

Select Source Type

Choose from the supported connector list and provide connection credentials.
3

Configure Discovery Scope

Define which databases, schemas, and tables to include in Prizm’s catalog.
4

Run Initial Discovery

Trigger the first asset discovery run to populate the catalog with metadata and lineage.
5

Configure Profiling Schedule

Set profiling frequency, run scope (full, incremental, sampling), and alert thresholds.