GIFRÖST

GIFRÖSTdata-in-motion platform

A Change Data Capture, replication, and consistency platform that moves source-system changes into Kafka, applications, cloud platforms, analytics, and lakehouses without stopping business operations.

2 TB
data per day
3-4 s
near real-time reaction
24/7
flow without downtime

Platform

One platform for CDC, streaming, and data control

GIFRÖST covers change capture, event transport, routing, transformations, downstream delivery, and data verification through the Compare module.

  1. Log-based CDC

    The platform reads changes from transaction logs to reduce source-system load and keep operations continuous.

  2. Kafka as the backbone

    The change stream lands in the event layer, where it can feed applications, analytics, cloud platforms, and downstream processes.

  3. Compare as the proof layer

    The Compare module verifies whether data copies remain aligned with the source during migration, replication, reporting, and active-active scenarios.

How it works

From transaction log to ready stream

The platform uses logical, database-level replication instead of physical array replication. This lets teams choose data scope, filter objects, and prepare streams for specific consumers.

  1. 01

    Selected tables and schemas

    Teams can replicate full databases, schemas, or selected tables instead of moving everything without control.

  2. 02

    Routing and transformations

    Data can be filtered, cleaned, enriched, or routed to different consumers while it is still in motion.

  3. 03

    In-flight anonymization

    GIFRÖST supports safe data preparation for test, development, and analytics environments.

Scenarios

Where data must always stay current — without maintenance windows

GIFRÖST fits projects where an organization needs fresh data for operational systems, warehouses, lakehouses, AI, reporting, and business continuity plans.

Zero-downtime migrations

A continuous stream of changes reduces cutover risk and helps prepare the target environment before switching.

Real-time analytics and lakehouse

The platform feeds warehouses, data lakes, and lakehouses with fresh events from on-premises, cloud, and hybrid systems.

AI, ML, and real-time applications

Fresh data can feed AI models, LLMs, decision systems, and applications that should not wait for manual integrations.

Operations

Flow control instead of a black box

GIFRÖST combines open-source technologies with commercial support and an operational layer for monitoring, diagnosing, and evolving production data flows.

01

Fast setup

The product layer simplifies pipeline configuration so teams do not need to build the whole integration platform from scratch.

02

Monitoring and alerting

The platform documentation covers monitoring, alerting, Kafka management, database monitoring, and pipeline creation.

03

Stability at high volume

The architecture is designed for high change volume, scaling, and continuous operation.

Requirements

Enterprise-ready

Deployment requires Linux infrastructure, containerization, and planned capacity for transaction logs. This lets the platform run as a controlled layer close to data sources and targets.

01

Linux and containers

The documentation points to x64 Linux distributions plus Docker and Docker Compose as the installation foundation.

02

Space planning

Disk capacity should account for daily uncompressed transaction-log size, retention, and operational reserve.

03

Source and target connectivity

The GIFRÖST machine needs network traffic to source and target databases plus the ports required by the platform.

Ready for real-time streaming data integration?

Let us talk about sources, targets, change volume, and operational requirements. We will show how to arrange CDC, replication, and data verification in one platform.

Book a GIFRÖST consultation