Integration Platform
Basics
Description
The Integration Platform enables secure, scalable, and automated data exchange between Tealfabric IO and external systems such as ERP, HR, finance, EHS, procurement, and supplier platforms. Its purpose is to eliminate manual data handling by synchronizing master data and operational datasets, ensuring that information flows consistently across the organization. The platform standardizes, validates, and transforms incoming data so it can be reliably used within Tealfabric IO for operations, analytics, and reporting.
By providing automated data pipelines and full traceability, the Integration Platform supports regulatory and compliance use cases such as CSRD while remaining flexible for broader business needs. It ensures data quality, preserves auditability, and enables Tealfabric IO to operate as a connected backbone within a wider digital ecosystem, supporting multi-entity and multi-tenant environments at scale.
ProcessFlows
1. Source System Onboarding
GovernanceConfigure and register external systems or data providers, define connection methods (API, file, event, message), authenticate access, and assign ownership and scope (entities, suppliers, data domains).
No steps defined for this processflow.
2. Data Ingestion
Data PipelinePull or receive data from source systems on a scheduled, event-driven, or ad-hoc basis. Support batch and near–real-time flows for master data, transactional data, and ESG metrics.
No steps defined for this processflow.
3. Data Mapping & Transformation
Data PipelineMap source fields to Tealfabric IO data models, harmonize identifiers, standardize units and formats, and enrich data with entity, supplier, and time metadata.
No steps defined for this processflow.
4. Validation & Quality Control
ComplianceApply validation rules for completeness, consistency, and plausibility. Flag errors, route exceptions for review, and block or quarantine invalid data when required.
No steps defined for this processflow.
5. Persistence & Versioning
Data GovernanceStore validated data with versioning, timestamps, and lineage information to support auditability and historical analysis.
No steps defined for this processflow.
6. Workflow Triggering
Data PipelineTrigger downstream processes when data is received or updated (e.g. CSRD readiness checks, supplier follow-ups, recalculation of KPIs).
No steps defined for this processflow.
7. Outbound Data Distribution
Data PipelinePush processed or aggregated data to reporting tools, dashboards, regulatory reports, or back to external systems.
No steps defined for this processflow.
8. Monitoring & Exception Handling
GovernanceMonitor integration health, track failures and delays, notify owners, and support retries and corrections.
No steps defined for this processflow.
Integrations
No integrations configured for this playbook.
WebApps, Callbacks, and Webhooks
Webhooks
Webhook for Remote System Notifications
The Webhook captures notifications and event triggers from external systems, partners, or suppliers and delivers them to Tealfabric IO in real time. It enables asynchronous communication by listening for events such as data updates, new submissions, or status changes, and then initiating corresponding workflows or data processing within the platform.
By providing a reliable, secure, and auditable entry point for external events, the webhook ensures timely updates, automates downstream processes, and maintains traceability of all incoming triggers. It supports scalable integration with multiple systems without requiring manual intervention or constant polling.
Datapools
DLQ - Dead Letter Queue
The Dead Letter Queue (DLQ) is a specialized queue within the Integration Platform that captures ESG and operational data messages that fail to process successfully during ingestion or transformation. It stores these messages along with error details, enabling teams to analyze, debug, and resolve issues without disrupting the main data flows.
By isolating failed messages, the DLQ ensures that valid data continues to move through the pipeline while providing traceability, accountability, and a systematic way to retry or correct errors. This enhances the reliability, resilience, and auditability of Tealfabric IO’s integration workflows.
Integration Cache / Pipeline Datapool
The Integration Cache / Pipeline Datapool acts as a temporary buffer for data flowing through the Integration Platform, supporting both real-time and batch processing. It holds data in transit from source systems, partners, or external APIs before it is validated, transformed, and committed to the Master Data Datapool.
This datapool enables smooth and reliable data pipelines by handling high-volume inputs, asynchronous flows, and transient errors. It also supports automated retries, throttling, and intermediate transformations, ensuring that downstream processes receive consistent, accurate, and ready-to-use data.
Master Data (Golden Record) Datapool
The Master Data Datapool is the authoritative repository for validated and harmonized organizational, supplier, and ESG data. It consolidates information from internal systems, external sources, and supplier submissions to create a single source of truth, ensuring consistency, accuracy, and completeness across Tealfabric IO workflows.
This datapool supports reporting, analytics, and compliance processes by providing trusted data for calculations, aggregations, and decision-making. It maintains traceability, versioning, and lineage, so every data point can be audited and linked back to its source, enabling scalable and reliable operations across the platform.
Raw / Staging Datapool
The Raw / Staging Datapool temporarily stores incoming data from internal systems, external partners, and suppliers before it is validated, transformed, or integrated into the Master Data Datapool. It preserves the original, unprocessed data, ensuring traceability and enabling error analysis or reprocessing if needed.
This datapool supports batch and real-time ingestion, allows initial quality checks, and provides a safe buffer for handling high-volume or asynchronous data flows. By isolating raw inputs, it protects downstream processes from errors and ensures that only cleansed, standardized data moves into authoritative repositories.