5 Big Data predictions (Updated 2026)

February 2, 2025 · Baoss

Big Data 2025: 5 real-world challenges and how we solve them with generative AI

In 2016 we published five predictions about Big Data: HTAP, distributed processing, ubiquitous flash, the end of tech hype and vendor quality. Nine years later, the landscape has changed radically. Therefore, the problem is no longer storing petabytes —storage is a commodity—. What matters now is having data ready for generative AI in milliseconds, with governance, traceability and predictable cost.

At BAOSS we have guided some twenty clients (banking, retail, industry, telco, insurance) through 2024-2025. All of them have made the jump to AI-ready architectures. This article does not repeat analyst trends. Instead, it describes five real problems we have found in production. It also collects the solutions we have implemented, with anonymized business metrics.

1. Decision-to-data latency: the real HTAP bottleneck

The 2025 problem: A European bank with 12M customers needed real-time risk scoring (<50 ms). The goal was next-best-action offers in its app. However, its architecture separated OLTP (PostgreSQL) and OLAP (Snowflake) with nightly ETL. As a result, offers arrived 24h late. Conversion stalled at 3.2%.

BAOSS solution: StarRocks + CDC + unified feature store

We deployed StarRocks (native HTAP OLAP) on Kubernetes. We also configured Debezium CDC to capture PostgreSQL changes in under 200 ms. Then we created a shared feature store (Feast + Redis). This feeds both the rules engine and XGBoost + LightGBM models served with vLLM. That is how we achieved low-latency inference.

  1. Scoring P99 latency: 38 ms (before: 24h batch)
  2. Offer conversion: 3.2% → 7.8% (+144%)
  3. Infra cost/month: -38% vs Snowflake + separate ETL
  4. Time-to-market for a new model: 3 weeks → 2 days (CI/CD with GitOps ArgoCD)

Key stack: StarRocks 3.1, Debezium 2.5, Feast 0.38, vLLM 0.5, Kubernetes 1.29, ArgoCD.

2. Distributed industrial IoT: edge-to-cloud without reinventing the wheel

The 2025 problem: An automotive components manufacturer (14 plants, 4,200 sensors/plant) sent raw telemetry to Azure IoT Hub → ADLS Gen2 → Databricks. The monthly ingestion cost: €42K. Also, average alert-to-maintenance latency was 6.2h. OT teams, meanwhile, lacked Spark skills.

BAOSS solution: MQTT → Kafka Edge (Strimzi) → Iceberg + federated Trino

We implemented Kafka at the edge (Strimzi operator on OpenShift 4.16) with Tiered Storage to S3-compatible (on-prem MinIO). The data is then compacted into Apache Iceberg (open format, ACID, time-travel). We also added federated queries with Trino 451 over Iceberg + PostgreSQL (ERP) + Redis (last-state cache). Data engineers use pure SQL. Therefore, there is no Spark code to maintain.

  1. Ingestion cost/month: €42K → €14.8K (-65%)
  2. Alert latency: 6.2h → 4.3 min (Flink SQL rules at the edge)
  3. New plant rollout: 8 weeks → 3.5 days (Helm charts + GitOps)
  4. Data incident MTTR: 4.1h → 22 min (OpenTelemetry + Grafana observability)

Key stack: Strimzi 0.41, Iceberg 1.5, Trino 451, Flink SQL 1.18, OpenTelemetry, MinIO, OpenShift.

3. Lakehouse economics: flash + object storage + open formats

The 2025 problem: An omnichannel retailer (850 stores, 3.2M transactions/day) was paying €180K/month on Redshift RA3 + S3. 68% of the storage was “cold data” (>180 days) queried less than once/month. Moreover, data scientists were duplicating datasets on laptops (GDPR risk).

BAOSS solution: Automatic Iceberg tiering + local DuckDB + Polars

We migrated to Iceberg on S3 Standard-IA / Glacier Instant. Then we configured automated compaction and snapshot expiration policies (AWS Lambda + Step Functions). In addition, DuckDB (embedded, zero-config) lets analysts query recent partitions locally at 2-3 GB/s with no network. Meanwhile, Polars (Rust, multi-threaded) replaces Pandas in feature engineering pipelines. The result: 12x speed and 1/4 the memory.

  1. Storage cost/month: €180K → €52K (-71%)
  2. Feature engineering time: 4.2h → 22 min (Polars vs Pandas)
  3. Zero local copies: DuckDB reads remote Iceberg via HTTP range requests
  4. GDPR compliance: Full audit trail with Iceberg time-travel + AWS CloudTrail

Key stack: Iceberg 1.5, DuckDB 1.1, Polars 1.12, AWS S3 Intelligent-Tiering, Lambda, Step Functions.

4. Tool fatigue: standardize the “AI-ready” stack with governance

The 2025 problem: A telco had 47 data tools in its internal catalog (Airflow, Dagster, Prefect, dbt, SQLMesh, 3 catalogs, 4 ML frameworks, 6 visualization libraries). Onboarding a new data engineer took 6-8 weeks. Only 12% of developed models made it to production. There was also Shadow IT with hardcoded credentials in notebooks.

BAOSS solution: Internal platform (IDP) with Backstage + Terraform + OPA policies

We designed an Internal Developer Platform (IDP) on Backstage (Spotify). It includes versioned Terraform templates for each pattern: batch ETL, Flink streaming, ML training, RAG pipeline, API serving. In addition, OPA/Gatekeeper policies enforce: secrets in Vault, lineage in DataHub, Great Expectations tests, deployment only via ArgoCD. Finally, we documented golden paths with maturity scorecards.

  1. Data engineer onboarding: 6-8 weeks → 3.2 days
  2. Models in production: 12% → 67% (in 9 months)
  3. Credential security incidents: 14/year → 0 (Vault + OPA)
  4. Platform ROI (9 months): 3.2x (savings from duplicate licenses + productivity)

Key stack: Backstage 1.28, Terraform 1.9, OPA/Gatekeeper, DataHub 0.13, Great Expectations 0.18, ArgoCD, HashiCorp Vault.

5. Data quality for RAG: the new “garbage in, garbage out”

The 2025 problem: An insurer launched a claims assistant with RAG (Retrieval-Augmented Generation) over 2.4M scanned PDF/Word/image documents. Answer accuracy: 61%. However, hallucinations appeared in exclusion clauses. Naïve chunking (500 tokens, 50 overlap) broke tables and legal definitions. Also, there was no automated evaluation.

BAOSS solution: Semantic chunking + reranking + continuous eval with RAGAS

We implemented semantic chunking (LangChain + sentence-transformers + legal heuristics: keep articles intact, tables as markdown). We then generated embeddings with BGE-M3 (multilingual, dense + sparse + colbert) and indexed them in Qdrant (HNSW, metadata filtering). We applied reranking with Cohere Rerank 3.5, from top-50 to top-5. Generation uses Claude 3.5 Sonnet (prompt engineering: chain-of-thought + mandatory citations). Finally, we run continuous evaluation with RAGAS (faithfulness, answer_relevancy, context_precision) in a nightly pipeline. The system alerts if faithfulness < 0.92.

  1. Answer accuracy (blind human eval): 61% → 94.7%
  2. Hallucinations in critical clauses: 18% → 0.3%
  3. Query P95 latency: 3.8s → 1.2s (async reranking + semantic Redis cache)
  4. Query cost: €0.042 → €0.018 (cache + smaller model for intent classification)

Key stack: LangChain 0.2, BGE-M3, Qdrant 1.11, Cohere Rerank 3.5, Claude 3.5 Sonnet, RAGAS 0.2, Redis, Prometheus/Grafana.

Summary: what actually matters in 2025-2026

2016 Challenge2025 RealityBAOSS Lever
HTAP convergenceNative HTAP OLAP (StarRocks, Doris, ClickHouse) + feature storeDecision latency <50 ms
Distributed processingKafka edge + Iceberg + federated Trino (single SQL)-65% cost, minutes not hours

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

Related articles: Big Data Trends for 2026 / Big Data 2026: from the data lake to the AI-ready data product.