Universal Data Integration & Connectors
Native connectors for 50+ data sources with zero-code schema mapping, real-time and batch integration modes, and data contract monitoring that ensures every pipeline meets quality and latency SLAs
Pre-built connectors for 50+ data sources. Each connector handles authentication, schema discovery, and extraction with no custom code.
- Relational: SQL Server, PostgreSQL, MySQL, Oracle, DB2
- Cloud: AWS S3, Azure Data Lake, GCP BigQuery, Snowflake
- SaaS: Salesforce, SAP, Microsoft 365, ServiceNow
- Streaming: Kafka, RabbitMQ, AWS Kinesis
Native Connectors for Every Source
Connect to relational databases, cloud data lakes, SaaS applications, APIs, file systems, and streaming platforms with pre-built connectors. Each connector handles authentication, schema discovery, incremental extraction, and error recovery natively — no custom coding required.
The connector library covers the most common enterprise data sources: SQL Server, PostgreSQL, MySQL, Oracle, MongoDB, Elasticsearch, AWS S3, Azure Data Lake, GCP BigQuery, Salesforce, SAP, Microsoft 365, Kafka, and many more.
Each connector is tested against production workloads and includes built-in retry logic, connection pooling, and rate limiting. Custom connectors can be developed using our open connector SDK for proprietary or niche systems.
- Pre-built connectors for 50+ databases, lakes, APIs, and SaaS platforms
- Automatic schema discovery and metadata extraction
- Incremental extraction for efficient change data capture
- Built-in retry logic, connection pooling, and rate limiting
- Open connector SDK for building custom connectors
Zero-Code Schema Mapping
Map source schemas to target formats using an intuitive visual interface. AI-assisted suggestions recommend mappings based on column names, data types, and sample values. Complex transformations are handled with a drag-and-drop pipeline builder — no SQL or code required.
- Visual schema mapping interface with drag-and-drop
- AI-assisted mapping suggestions based on column semantics
- Built-in transformations: type casting, string manipulation, aggregation
- Reusable mapping templates for common integration patterns
- Version-controlled mapping definitions with rollback support
Real-Time & Batch Integration
Choose the right integration mode for each use case. Real-time CDC (Change Data Capture) streams changes as they happen. Scheduled batch jobs handle bulk data movement during maintenance windows. Hybrid pipelines combine both modes for optimal balance of freshness and efficiency.
- Real-time CDC for sub-second data propagation
- Scheduled batch jobs with configurable frequencies
- Hybrid pipelines combining real-time and batch modes
- Exactly-once delivery semantics for critical data
- Dead letter queues and error handling for failed records
Data Contracts & SLA Monitoring
Define data contracts between producers and consumers. Contracts specify schema expectations, quality thresholds, freshness SLAs, and volume bounds. The platform monitors every contract continuously and alerts when SLAs are at risk of breach.
- Schema contracts enforce structure expectations between teams
- Quality SLAs define acceptable thresholds per dataset
- Freshness monitoring ensures data arrives on time
- Volume monitoring catches unexpected spikes or drops
- Contract violation alerts with automated escalation
System Architecture
How It Works
Select Connector
Choose from 50+ pre-built connectors or use the SDK to build a custom one. Provide connection credentials and the system discovers available schemas.
Map Schema
Use the visual mapping interface to define source-to-target transformations. AI suggests mappings based on column names and data types.
Define Contract
Set quality thresholds, freshness SLAs, and volume expectations. The platform monitors these contracts continuously.
Run & Monitor
Execute pipelines in real-time or batch mode. Monitor throughput, latency, and data quality in the integration dashboard.
Select Connector
Choose from 50+ pre-built connectors or use the SDK to build a custom one. Provide connection credentials and the system discovers available schemas.
Map Schema
Use the visual mapping interface to define source-to-target transformations. AI suggests mappings based on column names and data types.
Define Contract
Set quality thresholds, freshness SLAs, and volume expectations. The platform monitors these contracts continuously.
Run & Monitor
Execute pipelines in real-time or batch mode. Monitor throughput, latency, and data quality in the integration dashboard.
Use Cases
Data Warehouse Loading
Extract from operational databases and load into your data warehouse or data lake with automated schema mapping and quality validation.
Cross-System Synchronization
Keep master data synchronized across CRM, ERP, and HR systems in real-time with CDC-based integration.
Cloud Migration
Migrate data from on-premise systems to cloud platforms with zero-code mapping and validation at every step.
API Data Ingestion
Connect to external APIs (government registries, market data, partner systems) and ingest structured data on schedule.
Event-Driven Architecture
Capture real-time events from Kafka streams and route them to analytics, monitoring, and compliance systems.
Multi-Source Consolidation
Consolidate data from multiple subsidiaries, branches, or acquired companies into a unified view.