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Mobiloitte delivers cloud data platforms with lakehouse or warehouse-first architecture, streaming/batch pipelines, dbt semantic layer, governance, and cost optimisation for AI, BI, and real-time analytics.
Durable, low-cost storage with fast engines for BI, ML, LLMs, and streaming.
Streaming data pipelines with Kafka, Flink, and Spark; Airflow/Prefect for reliable batch and ELT.
One shared truth with tested, versioned transformations and a clear metrics layer.
Find issues early with freshness checks, schema-drift alerts, and anomaly detection.
Shared feature definitions for training and serving to keep ML/MLOps in sync.
Stacks prepared for RAG, vector databases, hybrid search, and LLMOps.
Field-level lineage, masking, RBAC, and audit logs for trust and compliance.
Reliability as an SLO: QA, alerts, and clear incident playbooks.
Partitioning, compaction, z-ordering, caching, vectorization, and FinOps dashboards
Portable, sovereign, and zero-trust designs across environments.
Real-time reverse ETL for activation into CRMs, CDPs, marketing, ops, and product tools.
Support pods, operating rules, and a living platform roadmap.
Mobiloitte focuses on a strong data backbone lakehouse/warehouse core, dbt-modeled transforms, streaming and batch parity, and observability-first operations. The result is fast, reliable data for LLMs, ML models, BI dashboards, and decision engines without re-platforming or hidden surprises.
Well coded Contract-driven pipelines, dbt CI/CD, lineage, and tests
Responsive Embedded pods share SLOs, costs, and roadmap outcomes.
Fast growing Built for billions of events, multi-TB tables, and many teams.
Multipurpose One platform for BI, ML, LLM, and activation.
Mobiloitte Defines Domains, Data Contracts, Storage/Compute Layers, Governance, SLAs, Cost Policies, and Compliance Controls So the Platform Stays Stable for Years.
Domain-driven architecture (mesh where it fits)
Lakehouse/warehouse plus streaming plan
Data contracts, schema change rules, SLOs
Access, retention, masking, and lineage plan
FinOps model and budget limits
Teams Get Streaming and Batch Pipelines, dbt Models, Semantic Layers, Data Quality and Observability, Reverse ETL, and ML-Ready Datasets and Feature Stores.
Airflow/Prefect orchestration with Kafka/Flink/Spark
dbt tests, transforms, docs, and CI/CD
Lineage and observability (freshness, anomalies, drift)
ML feature stores (Feast, Tecton, or custom)
Reverse ETL into real-time operational tools and APIs
Mobiloitte Runs the Platform With SRE Discipline: Incident Playbooks, SLOs, Error Budgets, Drift Detection, Retraining, and Ongoing Cost/Performance Tuning.
Data SRE and incident management
FinOps dashboards, alerts, and tuning cycles
Model/data drift checks and retraining schedules
Governance rituals and audits
24/7 SLAs and roadmap evolution
Define domains, contracts, platform layers, governance, SLAs, and budgets; link them to business KPIs.
Stand up pipelines, models, observability, activation, and ML-ready datasets with tests and CI/CD.
Monitor the platform, manage incidents, retrain models, lower costs, and improve as data and teams grow
Databricks • Snowflake • BigQuery • Redshift • Delta/Iceberg/Hudi • Kafka/Flink/Spark • Airflow/Prefect • dbt • Great Expectations/Monte Carlo/Bento • MLflow/W&B • Feast • Pinecone/Weaviate/Milvus/pgvector • ClickHouse • DuckDB
Compliance comes by default: SOC2, GDPR, HIPAA, and PCI alignment with consent-aware pipelines, PII minimisation/masking, RBAC, lineage, retention, and audit trails. Policy is enforced in code, not only on paper.
A warehouse excels at structured BI. A lakehouse blends low-cost lake storage with warehouse-style reliability and ACID, which fits BI, ML/LLM, and streaming. Teams choosing multi-modal analytics with long-term flexibility usually prefer a lakehouse.
Data contracts, CI/CD tests, and observability catch changes early. Producers follow contracts; breaking changes trigger automatic failures and escalation. Quality shifts left, so defects are fixed before they spread.
The platform runs with SLOs, error budgets, runbooks, and incident playbooks just like production apps. Freshness, completeness, drift, latency, and job failures are treated as first-class incidents with rapid response.
Yes. FinOps sets budgets and alerts, tracks unit economics, and drives tuning cycles. Storage formats, partitioning, compaction, vectorisation, caching, and capacity planning keep spending in control as data scales.
Yes, where they fit. Domains get ownership, SLAs, contracts, and shared platform tools so each team moves fast without rebuilding basics. The result is federated data with common rules.
Feature stores standardise training and serving, while vector-ready indexes prepare for RAG and hybrid search. PII rules, lineage, and MLOps (drift checks, retraining) keep models reliable and compliant.
Yes. Spark, Flink, Kafka, Delta/Iceberg/Hudi, dbt, ClickHouse, DuckDB, and vector DBs can run on-prem or air-gapped. Teams keep full control and auditability without losing modern features.
A semantic metrics layer, curated marts, and trust dashboards show ownership, freshness, and quality. Documented lineage explains where numbers came from, reducing reliance on shadow spreadsheets
Exports use role-scoped access, masking, consent checks, and retention guards. Every push is logged; source models are tested and monitored to prevent risky automation.
Integration, separation, or migration is chosen by ROI and governance needs. The platform is designed to be composable, so no single tool creates lock-in.
A visible MVP with pipelines, dbt, and lakehouse typically takes 6–10 weeks. Full streaming, feature store, FinOps, and large-scale activation follow in 10–16+ weeks.
Fewer incidents, higher data-trust scores, faster model delivery, and less duplicated logic. Costs remain predictable, and the platform becomes the standard backbone for BI, ML, LLM, and real-time decisions.
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Mobiloitte helps organisations build a data operating system not just a toolchain. The platform is governed, observable, cost-aware, and ready for the next decade of AI, BI, and real-time apps.