DataBridge
A data pipeline and integration platform that reduces bespoke ETL/ELT work to configuration — providing a visual pipeline builder, 200+ connectors, built-in data quality, and centralised monitoring.
The problem
Data teams maintaining pipelines instead of building capability
Enterprise data teams spend the majority of their time building and maintaining bespoke ETL/ELT pipelines. Each new data source — a new SaaS tool, a new operational database, a new partner feed — requires custom integration work: connection management, schema mapping, incremental load logic, retry handling, monitoring.
When a pipeline breaks, it's usually discovered by someone noticing that a report is wrong or stale — not by the monitoring system flagging a failure. When a source schema changes, pipelines silently start producing incorrect output until someone notices. Each pipeline is slightly different, so there's no shared runbook for on-call engineers.
The result is a data team where the majority of capacity is absorbed by infrastructure maintenance rather than building the analytical products and data models that make data useful to the business.
Capabilities
What DataBridge provides
Visual pipeline builder
Design pipelines visually with a live data preview at each stage. Source configuration, transformations, quality checks, and destinations are all configured through the builder — no boilerplate code per pipeline.
200+ connectors
Pre-built, maintained connectors for relational databases, cloud data warehouses, SaaS platforms, file sources, REST APIs, and streaming sources. New connectors are added regularly based on customer request.
Declarative transformations
SQL-based and Python transformations defined declaratively and version-controlled in Git. Transformations run in the pipeline execution context — no separate transformation cluster to manage.
Built-in data quality
Define quality rules per pipeline — null checks, range validation, referential integrity, schema drift detection. Quality failures are logged, reported, and can trigger alerts or halt pipeline execution depending on severity.
Centralised monitoring and lineage
A single operations dashboard showing pipeline health, run history, failure details, and processing volumes across all pipelines. End-to-end lineage tracking shows exactly where each field in a dataset originates.
Flexible execution
Schedule-based execution (cron), trigger-based execution (webhook or API call), and event-driven execution from Kafka topics or cloud event services. Change data capture support for operational database sources.
Use cases
What teams use DataBridge for
ERP data into cloud warehouse
Extracting financial, inventory, and operational data from SAP or Oracle ERP systems and loading it into a cloud data warehouse — with incremental loads, schema mapping, and automated reconciliation between source and destination row counts.
SaaS application data consolidation
Unifying CRM, marketing automation, product analytics, and customer success data from multiple SaaS tools into a single analytical layer — enabling a consistent view of customer acquisition, retention, and revenue across systems that don't natively share data.
Real-time CDC from operational databases
Change data capture from PostgreSQL, MySQL, and SQL Server operational databases into streaming destinations or near-real-time data warehouse loads — for operational dashboards that need current data rather than batch updates.
Data migration between platforms
Migrating data from a legacy data warehouse to a modern cloud platform — with transformation logic to adapt to the new schema, validation at each migration batch, and the ability to run source and destination in parallel until confidence in migration completeness is confirmed.
Technical specifications
Platform details
- Deployment
- Kubernetes-based. Available as fully managed SaaS or on-premise deployment in your own infrastructure.
- Execution engine
- Apache Spark for large-scale data processing. Lightweight Python executor for smaller workloads. Engine selection is automatic based on data volume.
- Storage format
- Delta Lake support for ACID transactions, time travel, and schema enforcement on lakehouse destinations.
- Versioning
- Git-based pipeline versioning. Pipeline definitions are stored as YAML and can be managed through standard Git workflows including CI/CD pipeline promotion.
- API
- Full REST API for pipeline management, trigger execution, monitoring queries, and lineage retrieval. OpenAPI spec provided.
- Compliance
- SOC 2 Type II certified. Column-level encryption for sensitive data in transit and at rest. GDPR-compliant data residency options available in SaaS deployments.
Pricing
Licensing
DataBridge is available as a SaaS subscription or on-premise licence. SaaS pricing is based on data volume processed and the number of active connectors. On-premise licensing is based on the number of nodes deployed. Contact us for a tailored quote.
SaaS subscription with volume-based pricing
On-premise perpetual licence with annual maintenance
Implementation and connector development support available
Target users
Common questions
Frequently asked questions
Fivetran and Airbyte are focused specifically on the extraction and loading phase — moving data from sources to destinations with minimal transformation. DataBridge covers the full pipeline: extraction, transformation (SQL and Python), data quality, and loading, with centralised monitoring across all pipelines in a single platform. If you're running dbt separately for transformation and have fragmented monitoring, DataBridge consolidates those layers. For teams that primarily need extraction connectors and are happy managing transformation separately, Fivetran or Airbyte remain strong choices — and DataBridge can coexist with them in architectures where they already have significant adoption.
DataBridge detects schema changes at the source on each pipeline run. When a schema change is detected — a column added, renamed, or removed — the pipeline notifies the pipeline owner and, depending on your configured response policy, either halts execution (for breaking changes) or continues and records the schema drift event (for additive changes). You can configure schema evolution policies per pipeline, so a new column in a SaaS source is automatically added to the destination, while a renamed column that breaks a transformation triggers a halt and an alert.
Yes. DataBridge integrates with dbt as an orchestration layer — it can trigger dbt runs after ingestion pipelines complete, pass pipeline metadata to dbt models, and incorporate dbt model lineage into the DataBridge lineage graph. Teams with an existing, established dbt project can use DataBridge for the ingestion and monitoring layers while keeping their existing dbt transformation codebase intact. The integration uses dbt's REST API and is compatible with both dbt Core and dbt Cloud.
DataBridge supports column-level PII classification in the pipeline configuration. Classified columns can be masked, hashed, or encrypted in transit and at rest, with policies configurable by destination. Access to unmasked data is logged for audit purposes. For on-premise deployments, data never leaves your infrastructure. For SaaS deployments, data is processed in isolated tenant environments and is not retained beyond pipeline execution — DataBridge does not store copies of your source data.
Standard pipelines using pre-built connectors can be configured by a data engineer or analytics engineer comfortable with SQL. Setting up a new connector to a standard source — a relational database, a SaaS platform, a cloud storage bucket — requires familiarity with connection credentials and basic schema knowledge, not programming. Custom transformations using Python require Python proficiency. Building a new connector for a source not in the pre-built library requires API knowledge and Python development experience, and is typically done by a data engineer or supported by AlgoDomain's implementation team.
See DataBridge with your data sources
Demonstrations are most useful when configured for the sources and destinations you actually work with. Tell us your stack and we'll show DataBridge handling pipelines relevant to your environment.