The evolution of the Big Data stack

July 7, 2025 · Baoss

Data + AI Stack 2026: From Hadoop to Autonomous Agents

In 2017 we published on this blog the evolution of the Big Data stack: from GFS/MapReduce to Colossus, Spanner and Dataflow. Eight years later, the landscape has changed so much that that architecture looks like industrial archaeology. The problem is no longer processing petabytes in batch; it is serving inferences in milliseconds, governing models that hallucinate, and orchestrating agents that make business decisions without human supervision.

At BAOSS we have accompanied a dozen clients —banking, retail, logistics, public sector— through this transition during 2026-2027. What follows is not vendor theory: these are real patterns, real metrics and real pains that we have solved (or are solving) in production.

The 2026-2027 problem: three bottlenecks that kill projects

  • Time-to-value: 68% of corporate GenAI initiatives never get past PoC (Gartner, May 2025). The root cause: decoupled architectures where data, models and business logic live in separate silos.
  • Ghost governance: Models that retrain themselves, RAGs that leak PII, agents that execute financial transactions without audit trails. 41% of Spanish CISOs admit to having no full visibility of their AI estate (INCIBE, Q1 2025).
  • Invisible cost: A badly sized GPU cluster burns €12,000/month on idle inference. We have seen cloud bills grow 3.7× after enabling RAG without semantic caching or intelligent routing.

Reference architecture 2026: layers, not silos

The answer is not “yet another tool”. It is an orchestration layer that joins data, models and execution with observable contracts. The stack we deploy today at BAOSS has five planes:

  1. Unified Data Plane: Iceberg + Trino/StarRocks over S3/MinIO. Real ACID, time travel, zero overnight ETL. A retail client cut data freshness time by 82% (24 h → 4 h) migrating from Hive to Iceberg.
  2. Governed Model Plane: vLLM or Ollama to serve Llama-3.1-70B, Qwen2.5-72B or Nemotron-3-Ultra with continuous batching and prefix caching. Key metric: TTFT (Time To First Token) < 120 ms at p95.
  3. Knowledge Plane (RAG 2.0): Hybrid indexes (BM25 + dense + ColBERT) with cross-encoder re-ranking. Adaptive semantic chunking, not fixed token windows. A banking case: retrieval accuracy from 61% to 89% just by changing the chunking strategy.
  4. Agent Plane: LangGraph + MCP (Model Context Protocol) for stateful agents, native human-in-the-loop and full traceability. CrewAI/AutoGen only for rapid prototyping; in production LangGraph wins for loop control and checkpointing.
  5. Control Plane: OpenTelemetry + Prometheus/Grafana + LangSmith/Langfuse for end-to-end observability: latency, cost per trace, embedding drift, hallucination detected via self-consistency sampling.

Case 1: Mid-size bank — Automating complaints with RAG agents

Context: 12,000 complaints/year, average SLA of 3 weeks, 14 dedicated analysts. Scattered documentation: regulatory PDFs, case-law history, transcribed voice notes.

BAOSS solution (Q2 2025):

  • Continuous ingestion into Iceberg (Kafka Connect → MinIO → Trino).
  • Hybrid RAG: 2.4M chunks, re-ranking with bge-reranker-v2-m3.
  • 4-node LangGraph agent: classify → retrieve → draft → human-review. MCP exposes tools: “query BdE regulations”, “search case law”, “calculate legal interest”.
  • Human-in-the-loop policy: only 23% of drafts require editing; the rest are auto-approved with a confidence threshold > 0.92.

Results after 4 months:

  • SLA: 3 weeks → 2.1 days (90% reduction).
  • Unit cost: €47 → €3.8 (12.4× factor).
  • Legal accuracy validated by committee: 96.3% vs 91% manual.
  • ROI: 3.2× in 6 months (projected 5.8× at 12 months).

Case 2: International logistics — Demand Sensing with multimodal forecasting

Context: 400 SKUs, 18 countries, chaotic post-COVID seasonality. Classic ARIMA forecasting: MAPE 28%. Simultaneous stockouts and overstock.

BAOSS solution (Q3 2025):

  • Feast feature store on Iceberg: 340 features (weather, holidays, macro, web signals, truck IoT telemetry).
  • Temporal Fusion Transformer model (PyTorch Forecasting) served with Triton + vLLM for overnight batch inference.
  • CrewAI what-if agent for planners: “what happens if fuel rises 15% in Q4?” → simulation in 8 s.

Results after 3 months:

  • MAPE: 28% → 14.2% (49% reduction).
  • Stockouts: -63%; valued overstock: -41%.
  • Scenario simulation time: 4 h (Excel) → 8 s (agent).
  • Estimated net saving: €1.7M/year.

Case 3: Public sector — Citizen assistant with RAG + voice with guarantees

Context: 2.3M queries/year, 11 co-official languages, strict GDPR, zero tolerance for hallucinations on social benefits.

BAOSS solution (Q4 2025, in progress):

  • RAG index per autonomous community + competency area. NeMo Guardrails: deny-list of sensitive topics, consistency self-check (3 samples, temperature 0.3).
  • On-premise ASR/TTS (Whisper-large-v3 + Piper) for voice; e2e latency < 1.8 s.
  • MCP exposes the “query BOE/BOJA/DOGC” tool with exact citation (article, paragraph, date).
  • Immutable audit: every trace signed with KMS and stored in WORM S3.

Target KPIs (go-live Q1 2026):

  • Handoff to human agent < 8% (today 34%).
  • Detected hallucination rate < 0.4% (today 2.1% in PoC without guardrails).
  • Cost per query < €0.018 (today €0.12 in the call centre).

Hard lessons learned in 2026-2027

  • RAG is not “index and pray”. Semantic chunking + re-ranking + query rewriting + continuous evaluation (RAGAS) are mandatory. Without them, accuracy < 65%.
  • Agents ≠ prompt chains. State, checkpointing, idempotency keys and human-on-the-loop are software engineering, not prompt engineering. LangGraph + MCP is today the only combination that delivers production-ready.
  • GPU poor is the new technical debt. vLLM + prefix caching + continuous batching saves 3-5× GPU vs naive serving. Ollama is fine for dev; in prod, vLLM or Triton.
  • Observability without cost per trace is a lie. Self-hosted Langfuse + native OpenTelemetry in LangGraph: marginal cost < 0.3% of the inference bill.
  • Governance is designed on day 0, not day 90. OPA policies over MCP traces, automatic data lineage (OpenLineage + Iceberg), GDPR/AI Act-ready audit.

    • BAOSS tech stack 2026-2027 (short version)

    Layer Primary choice Alternative / complement Why
    Transactional lake Apache Iceberg (Tabular/Cloudera) Delta Lake (Databricks) ACID, time travel, hidden partitioning, multi-engine
    Federated SQL engine Trino / StarRocks DuckDB (local), ClickHouse (heavy analytics) Pushdown, cost-based optimizer, native connectors
    LLM serving vLLM (PagedAttention) Triton + TensorRT-LLM, Ollama (edge/dev) Throughput 2-5× HF TGI, native prefix caching
    Embeddings / re-ranking bge-m3 + bge-reranker-v2-m3 E5-mistral, Nomic-embed-text-v1.5, ColBERTv2 Multilingual, SOTA on MTEB, Apache-2.0 licence
    Agent orchestration LangGraph + MCP CrewAI (prototype), AutoGen (simple multi-agent) Native state, loops, checkpointing, human-in-the-loop
    Observability Langfuse (self-hosted) + OpenTelemetry LangSmith (SaaS), Phoenix (Arize) Controlled cost, full traceability, automatic evaluation
    Guardrails NeMo Guardrails + OPA Guardrails AI, LlamaIndex response synthesizers Declarative policies, runtime validation, explainable
    Feature Store Feast (Iceberg backend) Tecton (managed), Hopsworks GitOps, point-in-time correctness, multi-environment
    Infra / GitOps Terraform + ArgoCD + Kustomize Pulumi, FluxCD Deterministic deployments, rollback in seconds

    2026 roadmap: what is already coming

  • Agents with long-term memory: Mem0 / Letta / Zep for persistent cross-session context without stuffing the context window.
  • Small specialised models (< 8B) beating GPT-4o on vertical tasks: LoRA/QLoRA fine-tuning + DPO on proprietary data. Inference cost 1/20.
  • MCP as de facto standard: Already adopted by Anthropic, Microsoft, LangChain, Grafana. Any internal tool exposed as MCP = immediately usable by any agent.
  • AI Act compliance by design: Model registries, technical documentation and decision traceability built in from the design, not bolted on afterwards.

    • All of these trends point in the same direction: smaller, more specialised, better-governed systems connected through open standards. Whoever builds on these foundations today will reach 2026 with an advantage.

    Shall we talk? At BAOSS we help you land these ideas in your organisation. Request an assessment and let’s discuss your case.

    Prefer to talk to us directly? Contact the BAOSS team and tell us about your challenge.

    Related articles: Kafka and ksqlDB: Real-Time Data Streaming / AI agent risk: how to control what your company is already deploying.