What is Tacnode

Tacnode is a Context Lake — a new type of data system that brings together everything you’d normally split across separate tools: transactional databases, data warehouses, search engines, vector stores, and stream processors. In a traditional stack, you constantly have to ask: Where does this data belong? A new event might need to go into your database. A log file might go into a warehouse. A customer support ticket might be indexed in your search engine. An embedding from your AI pipeline might get stored in a separate vector DB. And every time you need to combine them, you’re stuck building fragile pipelines or syncing jobs. Tacnode flips this model. Instead of scattering data across silos, everything goes into the Context Lake. Once it’s there, it’s instantly queryable across all workloads: real-time streams, historical analytics, text search, and AI retrieval. Think of the Context Lake as the nervous system for your applications:

• Every event is captured the moment it happens.
• Historical memories are available alongside fresh signals.
• Structured, semi-structured, and unstructured data are treated as equal citizens.
• Your AI models don’t have to piece together fragments — they get the full context in one shot.

This is not just a storage layer, and it’s not just a database. It’s a unified context engine for the modern data era.

Why Tacnode

Real-Time by Default: Most data systems were designed in an era when “yesterday’s numbers” were good enough. Tacnode is designed for a world where milliseconds matter — whether it’s a liquidation event in perpetual trading, detecting fraud as it happens, or powering an AI agent that can’t afford to hallucinate outdated answers.

• Data is queryable the instant it arrives.
• Pipelines don’t need to “catch up” before you see results.
• Applications, analytics, and AI agents can all react in real time, instead of waiting minutes or hours.

Unified Architecture: Instead of stitching together five or six different engines, Tacnode runs them all natively inside the Context Lake. That means:

• No ETL pipelines between systems — everything lives in one place.
• No schema gymnastics to convert between formats — Tacnode accepts structured rows, JSON, documents, embeddings, and time-series events out of the box.
• No syncing headaches — you’re not constantly worrying about data drifting between your OLTP and OLAP layers.
• The result is a system that’s simpler, cheaper, and faster — because there are fewer moving parts.

AI-Native: For developers, this means you can go from “data in” to “AI out” without duct-taping together half a dozen specialized systems. AI doesn’t just need raw data — it needs context. Tacnode bakes this in at the storage and query layer.

• Vectors and embeddings are first-class citizens — not bolt-ons.
• You can run hybrid queries: structured + text + vector similarity, all in one SQL statement.
• Retrieval-augmented generation (RAG) and intelligent agents are natural use cases, not hacks.

Developer-Friendly: Tacnode is Postgres-compatible. This makes Tacnode accessible to teams that don’t have the time (or appetite) to learn a whole new stack which means:

• Your existing SQL knowledge works — no need to learn a new query language.
• You can keep using Postgres drivers, ORMs, and BI tools you already know.
• Migration paths are straightforward — swap in Tacnode without rewriting your app.

Cloud-Native: Tacnode is built for the cloud era, not for racks of servers in a datacenter. It’s everything you’d expect from a modern SaaS data system, with the power of a unified architecture under the hood.

• Elastic scaling — spin up or down with workload demand.
• Pay for what you use — no idle clusters burning money.
• Multi-cloud and hybrid support — deploy where your business needs it, without lock-in.

Product Architecture

Tacnode is built from the ground up to unify, not bolt on. Here’s how the layers fit together:

Context Ingestion:

• Accepts multiple formats — SQL inserts, JSON events, documents, logs, embeddings.
• Ingests at high throughput with millisecond-level latency.
• Automatically indexes for queries across transactional, analytical, and vector workloads.

Unified Storage Engine:

• Handles both row-oriented (transactional) and columnar (analytical) access patterns.
• Optimized to keep hot data in memory while still scaling to terabytes or petabytes.
• No need to choose between a transactional DB or an OLAP warehouse — you get both.

Query Layer:

• Fully SQL-compatible, with PostgreSQL syntax support.
• Capable of joining live event streams with historical data in one query.
• Supports joins across structured, unstructured, and vector data sources.

AI/Vector Layer:

• Embeddings can be ingested directly or generated on the fly.
• Built-in similarity search, nearest neighbor lookups, and hybrid ranking.
• Perfect for semantic search, RAG, recommendations, and personalization.

Streaming Core:

• Sub-millisecond event processing.
• Built-in triggers for alerting, risk checks, and automated workflows.
• Eliminates the need for a separate event bus or stream processor.

Core Concepts

Context Lake

All your data, in all formats, queryable in real time, always in context. Instead of scattering information across systems, Tacnode keeps everything unified so you can act without delay.

Nodegroup

A Nodegroup is the computing module used for executing SQL commands. Each Nodegroup operates with its own resources and can dynamically scale up or down as needed. They are completely independent from one another, ensuring no impact on each other's performance. You have the flexibility to create multiple Nodegroups based on your business needs.

Unified Queries

One query, many workloads. Example: Join live trades with historical positions, embed documents for semantic relevance, and run a similarity filter — all in one SQL statement.

Real-Time Indexing

Data becomes queryable the instant it’s ingested. No batch jobs, no nightly ETL, no “pipeline lag.”

AI-Native

Tacnode doesn’t treat embeddings as second-class add-ons. Vectors, semantic similarity, and hybrid retrieval are built into the core query engine.

Elastic Cloud Footprint

You can expand or contract Tacnode your workload, so you never pay for idle infrastructure.