Consulting
Consulting
From data portals and catalogs to big data and AI platform architecture — we help you define your enterprise data and AI strategy and design the systems to realize it.
01
Self-service data access
Data Portal
Design a self-service data portal that lets every data consumer across the organization discover, access, and use data with ease.
| Data portal strategy | Assess current data access patterns, define portal goals and KPIs, build a roadmap |
| User experience (UX) design | Design data search, exploration, and visualization UI/UX; persona-specific dashboards |
| Data service API design | REST/GraphQL-based data delivery API design, API Gateway architecture |
| Access control & governance integration | Role-based access control (RBAC), approval workflows, usage log tracking |
| Data marketplace design | Internal data productization, dataset registration, publishing, and subscription process |
| Monitoring & usage analytics | Portal usage analysis, dataset popularity and utilization reporting |
02
Metadata-driven governance
Data Catalog
Systematically manage scattered data assets and implement trustworthy, metadata-driven data governance.
| Metadata management strategy | Define metadata collection scope and methods; design business, technical, and operational metadata taxonomy |
| Data lineage design | Source-to-report end-to-end lineage tracking architecture |
| Data quality framework | Define quality metrics (completeness, accuracy, timeliness, etc.) and automated validation rules |
| Data classification & tagging | Automated PII detection, business glossary design |
| Data ownership & stewardship | Define Data Owner and Data Steward roles and accountability framework |
| Catalog platform selection | Compare and select from Apache Atlas, Unity Catalog, DataHub, and others |
| Legacy system integration | Integration architecture for DW, data lakes, and BI tools with the catalog |
03
Scalable data platform design
Big Data Platform Architecting
Design a scalable, reliable big data platform tailored to your enterprise requirements.
| As-is assessment | Diagnose existing data infrastructure, pipelines, and governance |
| Target architecture design (To-Be) | Lakehouse, data lake, and DW hybrid architecture design |
| Technology stack selection | Requirements-based selection and PoC for Cloudera CDP, Databricks, open source combinations |
| Data ingestion architecture | Batch, real-time, and CDC ingestion pipeline design (NiFi, Kafka, Flink, etc.) |
| Storage design | Storage tier design for HDFS, Ozone, S3, ADLS; format selection (Iceberg/Delta/Parquet) |
| Compute architecture | YARN and K8s-based compute separation design, serverless transition strategy |
| Network & security design | VPC/VNet design, Private Link, firewall rules, encryption policies |
| Medallion architecture design | Bronze, Silver, Gold layer definitions and data modeling standards |
| HA/DR design | High-availability and disaster recovery architecture, RTO/RPO definitions |
| Sizing & capacity planning | Workload-based hardware and cloud resource sizing, TCO analysis |
| Migration strategy | Phased migration roadmap from legacy systems (CDH/HDP/traditional DW) to next-gen platform |
04
Enterprise AI platform design
AI Platform Architecting
Design an enterprise AI platform covering model training, serving, and monitoring end-to-end.
| AI/ML maturity assessment | Evaluate current AI/ML capabilities, infrastructure, and processes; define target maturity |
| MLOps architecture design | Train → validate → deploy → monitor pipeline design, CI/CD for ML |
| Feature Store design | Offline and online feature store architecture, feature registration, versioning, and serving |
| Model Registry design | Model version, stage, and metadata management framework; approval workflows |
| Model serving architecture | REST/gRPC endpoint design, A/B testing, canary deployments, autoscaling |
| Generative AI / RAG architecture | LLM selection and fine-tuning strategy, Vector DB design, RAG pipelines, Agent Framework |
| GPU infrastructure design | GPU cluster configuration, K8s GPU scheduling, multi-GPU training environments |
| Data & model governance | Training data lineage, model bias verification, model drift monitoring |
| AI security & compliance | Explainability (XAI), privacy protection, AI ethics guidelines |
| PoC & pilot design | Business-impact-based PoC target selection, success criteria, pilot execution plan |