Built for the people who own the pipeline
Whether you're a data engineer tired of connector fees, a CFO tired of surprise invoices, or a platform lead with sovereignty requirements — Bridge meets you where you are.
For Data Engineering
Declarative pipelines with log-based CDC at <60s P95 lag and 120 MB/s per-node throughput. Checkpoint-resume and auto-rollback keep every row accounted for across 100+ sources — you ship pipelines, not 2 a.m. pages.
Explore →For Analytics & BI
Land schema-enforced, CDC-fed data in Snowflake, BigQuery, Redshift, or Databricks — dashboards refresh on sub-minute lag instead of overnight batches, across 100+ connectors with unlimited access from the Team tier.
Explore →For Finance / CFO
One metric — rows synced — with a hard 3× monthly spend cap enforced by the engine, so the invoice can never exceed 3× the listed price. Model the switch in the TCO calculator before you commit.
Explore →For Platform & Infra
Self-host in your VPC, on-prem, or isolated networks with file-exchange sync — data stays in your perimeter, access is governed down to table level, and the platform carries a 99.9% uptime SLA.
Explore →Start from the job you need done
Purpose-built starting points drawn from real deployments — each one maps to the capabilities your team repeats.
Real-time Database Replication
Capture every committed change from Oracle, MySQL, PostgreSQL, and SQL Server at the database log level — with sub-minute lag and full lineage. Log-based CDC parses redo, binlog, and WAL directly so production cores stay untouched.
- ·Log-based CDC — captures directly from database logs
- ·Checkpoint-resume and automatic rollback for every row
- ·Schema drift handling without breaking downstream models
- ·Data masking before it reaches analytics
- ·In-VPC / in-perimeter operation
Business value
Keep dashboards, caches, and search indexes in sync with production at <60s P95 lag — with up to 120 MB/s per-node throughput for the initial load.
Data Warehouse & Lakehouse Loading
Land clean, typed, fresh data in Snowflake, BigQuery, Redshift, Databricks, or your lakehouse — with incremental and CDC-merge modes, schema enforcement on load, and predictable per-row pricing. Your dbt models and dashboards stay stable while the bill stays a fixed, capped line item.
- ·Typed, validated landing with schemas enforced at load
- ·Incremental & CDC merge — no full reloads
- ·100+ connectors, unlimited from the Team tier
- ·Schema drift handling protects downstream models
- ·3× monthly spend cap replaces surprise overages
Business value
Feed BI and ML reliably across 100+ connectors — a 3× monthly cap turns a variable integration bill into a forecastable line item.
Legacy Modernization & System Offload
Retire aging systems of record without freezing them. Migration Studio runs old and new in parallel, diffs every row, and lets you flip destinations pipeline-by-pipeline — so your BI keeps working and the legacy bill finally retires.
- ·Dual-run migration with row-level verification
- ·Log-based capture — no changes to the legacy system
- ·Checkpoint-resume for long backfills
- ·No source downtime required
- ·Predictable cost during the parallel-run window
Business value
De-risk the move off legacy databases while production keeps running — backfills resume from the last checkpoint instead of starting over.
Secure File & Network-Boundary Exchange
Move data across secure boundaries without moving control out of your hands. Convert database changes and files into ferry payloads, exchange them across isolated boundaries or security gateways via SFTP, and rehydrate them on the other side — all running inside your VPC or on-prem, never in a third-party SaaS.
- ·In-VPC / on-prem execution — no source data sent to SaaS
- ·File exchange and SFTP across security zones
- ·Bidirectional transfer with verification
- ·Operation audit trail and fine-grained permissions
- ·Recoverable, resumable batch tasks
Business value
Meet the security posture of banks, government, energy, telecom, and critical-infrastructure teams while still synchronizing data across zones.
Streaming, Event & AI-Ready Pipelines
Capture Kafka, queues, CDC streams, and batch files in one engine — and prepare AI-ready datasets with in-flight transformation, masking, and routing to vector stores, lakes, or downstream topics. No glue code, no per-event pricing, with backpressure handling and checkpoint resume.
- ·Native Kafka, Kinesis, and queue consumers with offset management
- ·CDC-to-stream and CDC-to-AI-store routing
- ·Batch file ingestion without bespoke loaders
- ·Backpressure handling and checkpoint resume
- ·Data masking before it reaches AI/ML models
Business value
Unify streaming, event, and AI-prep pipelines in one governed engine — the same platform carries 280K+ rows/s capture in production.
Multi-cloud & Hybrid Data Consolidation
Break down data silos across clouds and on-prem into one governed plane. Pipelines run where the data lives, with file-exchange sync for isolated networks and data that stays within your perimeter — so sovereignty is a property of the architecture, not a checkbox.
- ·File-exchange sync for isolated networks
- ·In-region operation keeps data where it must stay
- ·Data stays within your perimeter by default
- ·Unified control plane across clouds and on-prem
- ·Schema drift handling across heterogeneous sources
Business value
Consolidate distributed estates under one governed plane — 100+ connectors across clouds and on-prem, data never leaving your perimeter.
Which one are you?
Tell us your role and current stack — we'll map the shortest path to predictable pipelines.