Real-time Analytics
Replace batch pipelines with continuous ingestion and live queries. Analysts and operators see what's happening now, not what happened last night.
The problem
Traditional data warehouses run on batch cycles. Data arrives through scheduled ETL jobs, often hours behind.
What this means in practice:
- Analysts query snapshots that were fresh when the pipeline last ran
- Dashboards show hour-old data while operations move faster
- Issues get missed, responses come late
This worked when decisions happened in planning meetings. But operations now move faster — and batch pipelines can't keep up.
How Tacnode solves it
Tacnode Context Lake ingests data continuously and makes it queryable immediately — no batch windows, no waiting for the next pipeline run.
How it works:
- Data streams in from Kafka, CDC, and APIs
- Transformations run incrementally as data arrives
- Dashboards and queries always reflect current state
Analysts see live data. Operators respond to what's actually happening — not what happened last night.
Key Capabilities
Incremental Materialized Views
Define transformations declaratively in SQL. They execute continuously as data arrives — no external orchestration, no batch scheduling.
Data Lake Integration
Query Iceberg tables directly alongside streaming data. Unify your data lake and real-time analytics without moving data.
Tiered Storage
Hot data stays in high-performance storage; cold historical data moves automatically to cost-effective object storage. Query both seamlessly.
PostgreSQL Compatible
Use existing tools, drivers, and ORMs. Standard SQL queries work out of the box — no proprietary syntax to learn.
How it works
Architecture Highlights
- Data becomes queryable as it arrives — no batch windows
- Compute scales independently from storage for elastic capacity
- Analytical queries run without impacting other workloads
- Schema evolution handled automatically — no migration scripts
Use Cases
Real-time dashboards
Power dashboards with always-current data. No cache invalidation, no stale reads—just live state.
Streaming ETL replacement
Replace Kafka + Flink + warehouse pipelines with a single system. Ingest, transform, and serve in one boundary.
Operational analytics
Run analytical queries against live operational data without impacting transactional workloads.
Capabilities
- Continuous ingestion
- Sub-second freshness
- Incremental transforms
Integrations
- Kafka
- CDC
- BI tools
Documentation
Collective intelligence for your AI systems.
Enable shared, live, and semantic context so automated decisions stay aligned at scale.