Data Strategy &
Architecture
AI is only as good as your data foundation. Build modern, scalable data architectures that deliver quality, governance, and accessibility at enterprise scale.
AI's Data Imperative
Your data architecture determines what's possible with AI. Legacy data warehouses, siloed data lakes, and batch-first pipelines simply can't support modern AI workloads—especially generative AI that demands fresh, contextualized, multi-modal data at scale.
We've architected data platforms for 60+ organizations spanning financial services to healthcare to manufacturing. The pattern is consistent: modern data architectures built on lakehouse patterns, with streaming-first design, comprehensive governance, and AI-optimized storage deliver 10x faster time-to-insight and 40% lower TCO while enabling capabilities impossible with legacy systems.
Modern Data Architecture Capabilities
Building blocks of AI-ready data platforms
Lakehouse Architecture
Combine the flexibility and cost-efficiency of data lakes with the performance and ACID transactions of data warehouses. Lakehouse architectures using Delta Lake, Iceberg, or Hudi enable unified analytics, ML, and AI workloads on a single platform.
- • Store structured, semi-structured, and unstructured data in open formats
- • ACID transactions enable reliable data quality and governance
- • Time travel and versioning for reproducibility
- • Direct ML/AI access without ETL to separate warehouse
- • 40-60% cost savings vs. traditional warehouses
Technologies We Implement:
Databricks Lakehouse, Snowflake, Delta Lake, Apache Iceberg, AWS Lake Formation, Azure Synapse, Google BigLake
Data Mesh & Domain Ownership
Traditional centralized data teams become bottlenecks at scale. Data mesh decentralizes ownership—domain teams own their data as products while a platform team provides self-service infrastructure and governance guardrails.
Four Principles of Data Mesh:
- • Large organizations (1000+ employees, multiple business units)
- • Complex data landscape (100+ data sources)
- • Central data team is bottleneck
- • Need for domain-specific data semantics
Real-Time Streaming & Event-Driven Architecture
Batch processing creates data latency measured in hours or days. Modern AI applications demand real-time or near-real-time data. Streaming architectures using Kafka, Pulsar, or Kinesis enable event-driven patterns with millisecond latency.
- • Fraud detection (detect anomalies as transactions occur)
- • Personalization (update recommendations based on behavior)
- • RAG systems (fresh context for LLM responses)
- • Monitoring & alerting (real-time operational intelligence)
- • IoT & sensor data (process device telemetry at scale)
Streaming Stack:
Kafka/Confluent, AWS Kinesis, Azure Event Hubs, Apache Flink, Spark Streaming, Stream processing with Materialize or RisingWave
Vector Databases for AI
Traditional databases weren't designed for AI workloads. Vector databases store high-dimensional embeddings and enable semantic search, powering RAG systems, recommendation engines, and similarity-based applications essential for modern AI.
Vector DB Use Cases:
Data Quality & Observability
AI models are only as good as their training and inference data. Data quality issues—missing values, drift, schema changes, anomalies—directly impact model performance. Modern data observability platforms provide automated monitoring, alerting, and lineage tracking.
Observability Stack:
Monte Carlo, Great Expectations, Soda, dbt tests, Datafold, Bigeye - automated data testing, drift detection, and lineage
Impact
50% reduction in data incidents, 80% faster issue resolution, automated data contracts
Multi-Cloud Data Strategy
Avoid vendor lock-in while leveraging best-of-breed services
Cloud-Agnostic Foundations
Build on open formats (Parquet, Delta, Iceberg) and standards (SQL, Python) that work across clouds. Enables portability and negotiation leverage.
Best-of-Breed Services
Use specialized services from each cloud: Snowflake for warehousing, Databricks for lakehouse, AWS for cost, Azure for Microsoft integration.
Unified Governance
Implement consistent policies, catalogs (Unity, Purview), and observability across clouds. Single pane of glass for data management.
Data Architecture Transformation
Phased approach to modernizing your data platform
Assessment
2-3 weeks
Current state analysis, data landscape mapping, pain point identification, technology evaluation.
Key Deliverables
- •Data inventory
- •Architecture assessment
- •Gap analysis
- •Tech recommendations
Strategy & Design
4-6 weeks
Target architecture design, migration strategy, governance model, platform selection.
Key Deliverables
- •Reference architecture
- •Migration roadmap
- •Governance framework
- •Tech stack
Foundation Build
8-12 weeks
Core platform setup, initial migrations, governance implementation, team enablement.
Key Deliverables
- •Production platform
- •Initial data domains
- •Governance tools
- •Documentation
Scale & Optimize
Ongoing
Expand data domains, optimize performance and costs, continuous improvement, capability building.
Key Deliverables
- •Scaled platform
- •Cost optimization
- •Best practices
- •Centers of excellence
Ready to Modernize Your Data Platform?
Let's assess your current data architecture, identify opportunities for modernization, and design a roadmap to AI-ready data foundations.