Tacnode vs Data Lakehouses
From batch intelligence to real-time coherence
Examples: Databricks, Delta Lake, Apache Iceberg, Apache Hudi
Overview
Data lakehouses unified data lakes and warehouses for analytics and ML training—exceptional for batch workloads like model training, Spark jobs, and feature pipelines. But lakehouses operate on human timescales (minutes to hours). AI agents operate on millisecond timescales, making irreversible decisions continuously. Tacnode is the real-time serving layer for the models that lakehouses train.
Key Differences
Latency Profile
Millisecond-level reads with enforced temporal envelopes. Every query returns a coherent, fresh view.
Optimized for throughput over latency. Batch jobs measure in minutes; streaming in seconds.
Workload Type
Real-time serving: high-concurrency queries designed for low latency.
Batch processing: large-scale data transformations, model training, and ETL pipelines.
Feature Freshness
Online features computed in real-time with low-latency serving for immediate decisions.
Offline features computed in batch, typically hours behind real-time state.
Feature Comparison
| Feature | Tacnode | Data Lakehouses |
|---|---|---|
| Primary Workload | Real-time serving | Batch processing |
| Latency Target | Milliseconds | Minutes to hours |
| Feature Computation | Online, real-time | Offline, batch |
| Concurrency | High (many concurrent queries) | Moderate (batch jobs) |
| Decision Coherence | Enforced | Not applicable |
| Agent Memory | Native, durable | Requires external serving |
| Streaming Latency | Milliseconds | Seconds to minutes |
| Semantic Operations | Transactional | Batch embeddings |
When to Choose
Choose Tacnode
Choose Tacnode when agents need to act on features in real-time. When Decision Coherence matters—all agents must see the same state. When you're serving the models lakehouses trained. When milliseconds, not minutes, define your latency envelope.
Choose Data Lakehouses
Data lakehouses excel at large-scale data engineering, model training, and batch feature computation. For teams focused on ML training pipelines, data transformation, and offline analytics, the lakehouse architecture is unmatched.
Coexistence & Complementary Use
Tacnode and lakehouses are complementary layers. Use lakehouses to train models and compute batch features. Use Tacnode to serve those features to agents in real-time. The pattern: lakehouses for intelligence creation, Tacnode for intelligence serving.
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See how the Context Lake compares to data lakehouses for your specific use case.