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Data Collected And Delivered To Where It Matters

Porifera is an independent data-control layer that collects, shapes, and delivers clean, real-time IT, OT, and IoT data to your systems and platforms—on-premises, in the cloud, or hybrid—without vendor lock-in or per-GB fees.

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What Porifera Is

Porifera is an upstream data-control layer that standardizes how data enters, changes, and moves across IT, OT, and IoT. It decouples sources from tools so you can add or swap destinations without touching collectors, and replace scripts with policies that decide what’s kept, transformed, or dropped.

Core Capabilities

Ingest

  • A single upstream ingestion layer across environments
  • Continuous capture with local buffering in case of connectivity lose
  • Central source definitions rolled out safely across sites

Shape

  • Filtering, redaction, field mapping, normalization, enrichment
  • Keep-only and deny rules to remove noise before costs accrue
  • Schema guarantees so every consumer gets predictable JSON

Deliver

  • Policy-based routing with delivery guarantees
  • One-to-many destinations including SIEM, analytics, digital twins, and AI
  • Add or change destinations without redeploying collectors
  • A single upstream ingestion layer across environments
  • Continuous capture with local buffering in case of connectivity lose
  • Central source definitions rolled out safely across sites
  • Filtering, redaction, field mapping, normalization, enrichment
  • Keep-only and deny rules to remove noise before costs accrue
  • Schema guarantees so every consumer gets predictable JSON
  • Policy-based routing with delivery guarantees
  • One-to-many destinations including SIEM, analytics, digital twins, and AI
  • Add or change destinations without redeploying collectors

Porifera - Promise to Capability Map

How It Works

Porifera operates through four core components:

Agent: A lightweight edge collector for IT, OT, and IoT data. It performs local shaping, including filtering, enriching, throttling, and masking, before streaming normalized events.
Fleet Manager: A central control service that manages configurations for agents and bridges and organizes the infrastructure into data clusters.
Bridge: A modular connector that sends normalized Kafka streams to downstream systems such as BI, SIEM, AI, digital twins, and data lakes in vendor-neutral formats.
Dashboard: A web interface that shows end-to-end data flows with live metrics such as throughput, drops, latency, and errors, and includes a CLI for advanced users.

Flow: Define policy in fleet manager → agents apply it at the edge → data moves over streaming → destinations consume the same standardized stream in parallel.

Get Operational In Days, Not Months

Deploy, Secure, and Operate

Deployment

Security

Operations and Observability

Deployment

  • Containerized on your infrastructure, on premises or cloud
  • Agents belong to clusters and inherit their configuration
  • One token per cluster for agent registration and signed configuration pulls
  • Versioned configuration with staged rollout and rollback

Security

  • OAuth 2.0 and OIDC with role based access and audit trails
  • Cluster scoped tokens for agent enrollment and configuration
  • Encryption in transit with optional encryption at rest
  • Policy based redaction and field filtering at the edge

Operations and Observability

  • Metrics for heartbeats, throughput, lag, error rates, and queue depth
  • Dashboards to visualize end to end delivery
  • Admin UI and CLI for diffs, dry run, rollout, and rollback
  • Audit logs for configuration and token changes

Supported Data Sources and Destinations

Sources

     Examples of Data Sources Supported

  • IT: Windows Event Log, Syslog, HTTP/NGINX/Apache logs, App logs, DB query taps, Message queues.
  • OT: OPC UA (passive wire parsing), Modbus/TCP, BACnet/IP, DNP3, and IEC-62056.
  • IoT/Edge: MQTT topics, sensor gateways, device APIs.
  • Network Copies: SPAN/TAP mirroring for traffic parsing where agents can’t be installed.

Destinations

      Examples of Destinations Supported

  • SIEM and Security: Microsoft Sentinel, Splunk, IBM QRadar, Elastic Security
  • AI and ML Endpoints: Feature stores and vector databases such as Feast, Pinecone, OpenSearch vector, Azure AI Search vector
  • Search and Log Stores: OpenSearch, Azure Data Explorer Kusto, ClickHouse
  • Industrial Time Series and Historians: AVEVA PI System, Canary Historian, InfluxDB, Timescale, Amazon Timestream
  • Digital Twin Platforms: Autodesk Tandem, Azure Digital Twins, Siemens
  • Warehouses and Lakehouses: Snowflake, Databricks with Delta Lake, Google BigQuery, Amazon Redshift, Azure Synapse
  • Object Storage and Data Lakes: Amazon S3, Azure Data Lake Storage, Google Cloud Storage, MinIO, table formats such as Apache Iceberg and Apache Hudi

Top Usability with an Intelligent UI

The Porifera Effect

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