Tacnode vs OLAP Databases
When your queries need to drive decisions, not dashboards
Examples: ClickHouse, Snowflake, BigQuery, Redshift
Overview
OLAP databases were built to answer questions like "What were last quarter's sales by region?" They're optimized for scanning massive datasets and returning aggregates to human analysts who interpret the results, spot anomalies, and decide what to do next. That workflow assumes humans are in the loop—and that minutes or hours of data delay are acceptable. AI agents operate differently. A fraud detection agent doesn't run a report and wait for a human to act. It evaluates each transaction as it happens and blocks fraudulent ones in real-time. A pricing agent doesn't analyze yesterday's demand—it adjusts prices based on inventory levels right now. These agents need data that's current to the second, consistent across all concurrent queries, and queryable with millisecond-level latency. OLAP databases weren't designed for this. Tacnode was.
Key Differences
Who's Querying
AI agents and automated systems making hundreds of thousands of decisions per second, each query triggering an immediate action.
Human analysts running ad-hoc queries, building dashboards, and generating reports that inform future decisions.
Freshness Requirements
Data must reflect reality within milliseconds. An agent pricing inventory based on 5-minute-old stock levels will misprice.
Data can be minutes to hours old. Analysts reviewing trends don't need sub-second freshness.
Query-to-Action Gap
Zero gap. The query result is the action—block this transaction, set this price, route this request.
Large gap. Query results feed into human review, discussion, and eventual manual action.
Feature Comparison
| Feature | Tacnode | OLAP Databases |
|---|---|---|
| Primary Consumer | AI agents, automated systems | Human analysts, BI tools |
| Query Latency | Milliseconds | Seconds to minutes |
| Data Freshness | Milliseconds (continuous) | Minutes to hours (batch/micro-batch) |
| Consistency Model | ACID with serializable isolation | Eventual consistency |
| Concurrent Query Load | High (many concurrent queries) | Moderate (analytics workloads) |
| Vector Search | Native HNSW/IVFFlat indexes | Requires external vector DB |
| Full-Text Search | Native with relevance ranking | Limited or external |
| Time Travel Queries | Native (FOR SYSTEM_TIME AS OF) | Limited or requires setup |
| JSON/Semi-Structured | JSONB with path queries, indexed | Supported, varies by system |
| Geospatial (PostGIS) | Native support | Limited or external |
| Write Pattern | High-frequency streaming inserts | Bulk/batch loads, ETL |
| CDC Ingestion | Native from Postgres, MySQL, Kafka | Requires external tools |
| CDC Export | Native to Kafka | Requires external tools |
| Materialized Views | Incremental (auto-updating) | Batch refresh only |
| MV Freshness | Updates as data arrives | Stale between refreshes |
| PostgreSQL Compatibility | Wire protocol compatible | Not compatible |
| Workload Isolation | Nodegroups with isolated resources | Shared resources or separate clusters |
| Point Lookups | Optimized | Full scan or materialized views |
| Storage Architecture | Hybrid row + columnar | Columnar only |
| Row-Level Security | Native | Varies by system |
| Column-Level Security | Native | Varies by system |
When to Choose
Choose Tacnode
Your workload involves AI agents or automated systems that query continuously and act immediately. You need low-latency reads with fresh data. You're building fraud detection, dynamic pricing, real-time personalization, or autonomous operations where stale data causes incorrect decisions.
Choose OLAP Databases
Your workload is human-driven analytics: dashboards, quarterly reports, ad-hoc exploration of historical data. Query latency in the 1-10 second range is acceptable. Data can be hours old without business impact.
What Teams Are Saying
"As Cider scaled globally, we reached a point where operational data was being copied into too many systems just to keep up with queries. Tacnode allowed us to collapse that complexity into a single real-time layer. We're now querying fresh data directly, at scale, without the operational overhead that used to slow us down."
Coexistence & Complementary Use
Most teams need both. OLAP databases are excellent for business intelligence—understanding what happened and why. Tacnode is for systems that need to act on what's happening now. A common pattern: OLAP for analyst dashboards and model training, Tacnode for serving those models in production.
Architecture Comparison
Building real-time AI with olap databases requires stitching together multiple systems. Each connection is a potential failure point.
Typical OLAP Databases Stack
5+ SystemsTacnode Context Lake
1 SystemTacnodeFraud Detection: OLAP vs Context Lake
The Scenario
An e-commerce platform processes 50,000 transactions per minute. Each transaction needs to be scored for fraud risk before authorization—checking the user's recent purchase velocity, device fingerprint history, and shipping address patterns.
With OLAP Databases
With an OLAP database, you'd batch-load transaction data every few minutes, run aggregation queries to compute risk signals, and store results in a serving layer. But by the time the data lands, fraudulent transactions have already been approved. You're always investigating fraud after it happens, not preventing it.
With Tacnode
With Tacnode, transaction events stream in continuously. When a new transaction arrives, your fraud agent queries the user's last 100 transactions, device history, and address patterns—all in a single low-latency query. The agent scores the transaction and blocks it before authorization completes.
Tacnode Query Example
-- Low-latency query on fresh data
SELECT
COUNT(*) FILTER (WHERE created_at > NOW() - INTERVAL '1 hour') as hourly_velocity,
COUNT(DISTINCT device_id) FILTER (WHERE created_at > NOW() - INTERVAL '24 hours') as device_count,
bool_or(address_id != ANY(trusted_addresses)) as new_address
FROM transactions t
JOIN user_profiles u ON t.user_id = u.id
WHERE t.user_id = $1
AND t.created_at > NOW() - INTERVAL '30 days';Frequently Asked Questions
Ready to evaluate Tacnode?
See how the Context Lake compares to olap databases for your specific use case.