Two sovereign platforms. Zero JVM. Zero Python runtime. Zero telemetry. Post-quantum secured. GPU-native from the ground up.
Apache Spark and Databricks impose four compounding penalties on every enterprise workload. These are not edge cases — they are structural costs baked into every query you run.
Every operation crosses a JVM boundary, serializes, deserializes, and fights the garbage collector.
Spark's master-node architecture creates single points of failure and linear scale ceilings.
Databricks and Spark phone home with usage data. Your workloads are not private.
No native GPU execution path. Data must cross CPU→GPU manually for every operation.
A sovereign distributed big-data engine built from the ground up in Haskell and C23/CUDA. No JVM. No Python. No master nodes. Every operation is hardware-native and cryptographically auditable.
A Haskell compiler for a new domain-specific language. Lowers analytics primitives directly to C23 and CUDA kernels. Type-safe. Functional. Zero runtime overhead.
Stack-based bytecode VM with 7 production CUDA kernels: Bitonic sort, distributed hash join, columnar map/filter/reduce, matrix multiply, L1/L2 normalization.
A sovereign notebook UI — the Jupyter replacement. Compiled to WebAssembly. Runs natively or in any browser. Zero Python. Zero JVM.
C23 HTTP + SSE sovereign BI dashboard server. Real-time streaming analytics visualization without a cloud dependency.
ML-DSA-65 (FIPS 204) post-quantum signed tokens. Every session cryptographically authenticated. Stateless. <1ms verification.
Immutable, SHA-256 hash-chained audit log of every data operation. Tamper-evident by design. Built for HIPAA, SOC2, GDPR.
LDB columnar format: memory-mapped, GPU-aligned blocks. NVMe → VRAM direct DMA. No serialization step.
No master nodes. Every peer authenticates via PQC and is a sovereign node. UDP P2P fabric. No SPOF.
© 2024–2026 Scott Allen Baker. LambdaC and LambData are proprietary software. All rights reserved.
Measured on development hardware: Intel Ultra 7 265KF (20-core), RTX 3060, NVMe SSD. No A100. No H100. No data center. This is a laptop-class benchmark.
| Metric | LambData | Spark Equivalent |
|---|---|---|
| Dataset volume | 18,499,998 rows / 5 tables | Same |
| 5-stage pipeline (compile + typecheck) | 0.166s | N/A (JVM warmup: 15–30s) |
| 10M-row sort + window + groupby | 5.5s (GPU argsort) | ~80s |
| Total 5-stage wall-clock | 13.99s | ~180s |
| Throughput | ~1.32M rows/sec | ~100K rows/sec |
| Speedup vs Spark | ~13× faster | baseline |
Projected on GCP A100 (80GB HBM2e, 6912 CUDA cores): 50–90× faster than Spark. A100 delivers 8–15× further GPU speedup over RTX 3060 on sort-heavy workloads.
A full-Rust, post-quantum-encrypted, GPU-distributed RAG engine. Replace Python, LangChain, and Kubernetes with a stack enterprises can trust with their most sensitive data. Zero plaintext at rest. Zero telemetry.
ONNX Runtime + CUDA embedding, HNSW vector search, Ollama LLM generation — all in pure Rust. No Python. No LangChain overhead.
LambdaC lvm_nodes repurposed as GPU embedding workers. Distributed via UDP mesh. Hardware-native vector compute.
A sovereign RAG query interface compiled to WebAssembly via Leptos. Replaces Streamlit and Gradio entirely.
Rust Axum server coordinating the UDP worker mesh. PQC-authenticated peers. No Kubernetes. No service mesh overhead.
ML-KEM-768 key encapsulation + AES-256-GCM shard encryption. Data never stored in plaintext — even before cloud sync.
ML-DSA signed, append-only access log. Quantum-proof chain of custody for every document ingested and every query answered.
No GC. No JVM. No Python runtime. Memory safety by design. Predictable latency. Binary deploys with no dependency hell.
Any LLM via Ollama. Any object storage via object_store crate. Self-hosted or cloud. Your data, your infrastructure.
© 2024–2026 Scott Allen Baker. RusticAgentic is proprietary software. All rights reserved.
| Feature | LambData | Databricks | Spark | DuckDB | LangChain RAG |
|---|---|---|---|---|---|
| JVM-free execution | ✓ | ✗ | ✗ | ✓ | ✗ |
| Native GPU kernels | ✓ | Partial | ✗ | ✗ | ✗ |
| Post-quantum auth | ✓ | ✗ | ✗ | ✗ | ✗ |
| Immutable audit log | ✓ | ✗ | ✗ | ✗ | ✗ |
| Zero telemetry | ✓ | ✗ | ✗ | ✓ | ✗ |
| WASM browser UI | ✓ | ✗ | ✗ | ✗ | ✗ |
| Encrypted RAG vault | ✓ | ✗ | ✗ | ✗ | ✗ |
| Masterless mesh | ✓ | ✗ | ✗ | ✗ | ✗ |
12 years of enterprise systems engineering — HIPAA-compliant medical records infrastructure, HITRUST audits, CrowdStrike fleet management, Databricks clusters — Scott lived the problems LambData solves before building the solution.
In 2022, he built HazyNet — an industrial-grade Spark/Scala/CUDA pipeline processing 19.6 million NYC Taxi records at 100GB+ scale. He earned the Databricks Certified Developer credential. Then he measured the JVM tax, concluded the stack had to be replaced, and spent four years building the replacement.
Solo. Self-funded. Zero external investment.
Interested in licensing, enterprise deployment, acquisition, or partnership? Reach out directly. No gatekeepers.