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Coud Data Platform & Architecture

Modern Data Platforms Built to Power Analytics, AI, and Operations at Scale.

We do not stitch tools together. We architect cloud-native data platforms, lakehouses, and warehouses that unify enterprise data, serve every workload, and scale without breaking.
We do not build ML models in isolation. We deploy systems that drive real business decisions.
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The Difference

Stitched cloud stacks vs engineered data platforms.

Fragmented cloud data stacks

  • Disconnected warehouses, lakes, and marts running in silos
  • Tool-by-tool stitching with brittle integrations and duplicate data
  • Storage and compute sized for peak load, billed every hour
  • Lift-and-shift migrations that recreate legacy bottlenecks in the cloud
  • Performance tuning by trial and error after problems surface
  • Vendor lock-in through proprietary formats and closed ecosystems

Production cloud data platform

  • Unified lakehouse and warehouse architecture serving every workload
  • Single source of truth with governed access across BI, ML, and operations
  • Elastic, decoupled compute and storage tuned for cost-per-query
  • Re-architected platforms that unlock cloud economics and AI readiness
  • Engineered for query patterns, concurrency, and growth from day one
  • Open table formats, Iceberg and Delta, with full vendor portability
Core Capabilities

Three platform patterns we build.

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Data Lakehouse Engineering

Unified storage and compute for analytics, ML, and AI on a single foundation.

We design and implement lakehouses on Databricks, Delta Lake, Apache Iceberg, and Snowflake that serve analytics, ML, and operational workloads from one platform without trading off governance or performance.
  • Medallion architecture, Bronze, Silver, and Gold tiers, with clean lineage from raw ingestion to business-ready data
  • Delta Lake and Apache Iceberg implementation with ACID transactions, time travel, schema evolution, and unified batch and streaming access
  • Databricks Lakehouse platform deployment with MLflow, Unity Catalog, and feature store integration on a single workspace
  • Open table format migration from proprietary formats to Iceberg or Delta for full vendor portability and future flexibility
  • Lakehouse governance with Unity Catalog, column-level security, row-level filtering, and end-to-end data lineage
  • Hybrid lakehouse deployments balancing cloud storage economics with high-performance query engines for mixed workloads

Cloud Data Warehouse Architecture

High-performance analytical infrastructure built for SQL-first scale and concurrency.

When workloads demand consistent performance, mature governance, and SQL accessibility, cloud warehouses deliver what lakehouses trade off. We architect Snowflake, BigQuery, and Azure Synapse environments tuned to your query patterns, concurrency profile, and cost targets.
  • Snowflake implementation with virtual warehouse sizing, clustering strategies, secure data sharing, and Cortex AI integration
  • Google BigQuery architecture covering partitioning, clustering, BI Engine, slot management, and BigQuery ML for in-database training
  • Azure Synapse Analytics with dedicated and serverless SQL pools, Spark integration, and Synapse Link for Dynamics 365 analytics
  • Warehouse performance tuning through query optimisation, materialised views, result caching, and cost-per-query engineering
  • Multi-warehouse topology separating ETL, BI, and data science compute to prevent contention and contain runaway costs
  • Data sharing and marketplace architecture for secure, governed exchange of data across business units and external partners
CF services
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Data Platform Migration & Modernisation

From legacy on-premise infrastructure to cloud-native data platforms.

Legacy on-premise warehouses, Oracle, SQL Server, Teradata, Netezza, Informatica, are expensive to maintain and architecturally incompatible with modern AI and analytics demands. We execute structured, zero-data-loss migrations to cloud-native platforms with preserved business logic and minimal disruption.
  • Migration assessment and architecture design covering workload profiling, dependency mapping, and target-state platform selection
  • Teradata, Netezza, and Oracle to Snowflake, Databricks, or BigQuery migrations with parallel-run validation and zero data loss
  • ETL modernisation from Informatica, SSIS, or Talend to dbt, Fivetran, and modern cloud-native transformation patterns
  • Hadoop and on-premise data lake migration to cloud object storage with full lineage and security boundary preservation
  • Business logic translation, stored procedures, complex SQL, and scheduling, into modern transformation frameworks
  • Cutover orchestration with parallel running, reconciliation, and rollback safety nets for zero downtime business continuity
Production Architecture

The six layers of an enterprise-grade cloud data platform.

Layer

What It Does

Representative Tooling

Storage Layer

Object storage and open table formats holding raw, refined, and serving data with full lineage and time travel
S3, ADLS Gen2, GCS, Delta Lake, Apache Iceberg, Hudi

Compute Layer

Decoupled, elastic compute engines tuned for SQL analytics, ML training, and ad-hoc workloads
Databricks, Snowflake, BigQuery, Synapse, Spark, Photon

Catalog & Metadata

Unified governance, lineage, classification, and access control across the entire platform
Unity Catalog, Snowflake Horizon, Purview, Atlan, Collibra

Serving Layer

BI, semantic models, feature stores, and real-time APIs delivering data to every downstream consumer
dbt Semantic, Power BI, Tableau, Feast, Tinybird

Orchestration

Pipeline scheduling, job dependencies, and workflow management connecting ingest to serving
Airflow, Dagster, Prefect, Databricks Workflows, ADF

Security & Cost

Encryption, network isolation, role-based access, and continuous cost observability across the platform
Private Link, IAM, Lake Formation, CloudZero, Unravel
Platform Selection

Lakehouse, warehouse, or hybrid. we help you choose.

DECISION CRITERIA LAKEHOUSE DATA WAREHOUSE HYBRID PLATFORM
Primary Workload ML, AI, and large-scale analytics Enterprise BI and SQL analytics Mixed BI, ML, and operational analytics
Data Variety Structured, semi-structured, unstructured Mostly structured, modelled data Full spectrum across both engines
Concurrency Profile Moderate concurrency, heavy compute High concurrency, predictable queries Workload-segmented concurrency
Governance Maturity Governed via catalogs and lineage tools Native, mature SQL governance Federated governance across both
Cost Optimisation Storage-cheap, compute-elastic Tuned per-warehouse, per-query economicsRight-sized compute by workload type
Best Fit Industries Healthcare research, pharma, manufacturing Financial services, retail, BFSI BI Multi-line-of-business enterprises
Outocome In Production

Measurable impact from modern cloud data platforms.

60%

Lower total cost of ownership vs legacy platforms

10x

Faster query performance for analytical workloads

5x

Faster onboarding of new data sources and consumers

99.9%

Platform availability with multi-region resilience

Why CaliberFocus?

Four reasons enterprises choose us to architect their platform.

Engineered for Production
We architect platforms that survive scale, concurrency, and cost pressure. Every layer is sized, tuned, and monitored from day one, not retrofitted after problems surface in production.
Open and Vendor-Portable
We build on open table formats, Apache Iceberg, Delta Lake, and Parquet, so your platform stays portable. You own the architecture, the data, and the right to switch engines without rewrites.
Cost Engineering Built In
We treat cost as a first-class platform requirement. Compute clusters are sized per workload, query patterns are optimised, and continuous cost observability is wired into every deployment.
AI-Ready by Design
Our platforms serve BI, ML, and AI workloads from a single foundation. Feature stores, vector indexes, and real-time serving are part of the architecture, not bolted on after the fact.
Connected Services

Cloud data platform connects to the rest of the stack.

Data Engineering & Pipelines

Pipelines, DataOps, and serving infrastructure that runs on top of your cloud data platform.
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Real-Time Data Streaming

Live data flows from operational systems into your platform for instant decision-making.
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Data for AI & Feature Engineering

AI-ready feature stores, embeddings, and ML pipelines built on your platform foundation
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Build a data platform that powers the next decade.

Whether you are migrating off legacy infrastructure, designing a new lakehouse, or unifying fragmented platforms, we architect cloud data systems that serve every workload and scale without breaking.
Schedule a consultation
Talk to an ML engineer

Industries we serve

healthcare industry

Healthcare

manufacturing industry

Industrial Manufacturing

banking industry

Banking and Finance

retail industry

Retail and Ecommerce

Pharma & Life Sciences

logistic industry

Logistics and Supply Chain

energy industry

Energy and Utilities

media industry

Media and Entertainment

travel industry

Travel and Hospitality

Education & EdTech

Application innovation backed by deep engineering..

cf difference
Measurable Results

50% reduction in technical debt for enterprise clients

True Partnership Model

Dedicated teams integrated with your workflow

Rapid Innovation Velocity

Ship features 3X faster with our DevSecOps pipeline

Enterprise-Grade Security

SOC 2 compliant engineering practices

Partnering for innovation & growth

We collaborate with global technology leaders to deliver secure and scalable growth-driven digital solutions. Our partnerships strengthen our ability to innovate, accelerate transformation, and drive measurable business impact for our clients.

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Azure
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Case Studies

Case Study

Enhancing
Clinical Care,
Fewer Readmits!

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Automating docs, coding & compliance

We used generative AI to automate documentation, compliance checks, and medical coding. The solution improves accuracy, cuts manual effort, speeds turnaround, and ensures regulatory compliance in clinical use.
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Global Partnership

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Years Proven Success

200 +

Global Associates

What our clients say about our work?

When patient data was summarized clearly, documentation felt less burdensome. With CaliberFocus, clinician satisfaction rose from 58% to 81% without changing how teams work.

Dr. Rebecca HallCMIO, Healthcare

Better documentation and fewer audit issues delivered real savings. With CaliberFocus, billing compliance improved to 98.6%, reducing risk while easing the burden on clinicians.

Sophia WilliamsCTO, Clinical care

We gained clear visibility into student performance. Engagement rose, scores improved, and administrative effort dropped by nearly 30 percent, giving educators time to teach.

Andrew MillerDirector, Education Technology

Thoughts and Insights

AI In Workforce Planning
June 3, 2026

AI in Healthcare Workforce Planning: What Scheduling Software Can’t Do 

The opportunity AI creates in healthcare workforce planning isn’t about doing new things. It’s about fixing what already isn’t working, with tools current systems were never designed to be. Scheduling platforms got upgraded. Labor dashboards exist. Workforce analysts were hired. Some…

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radiologists-team-analyze-x-rays-discuss-treatment-options-medical-office
June 3, 2026

Clinical Workflow in Healthcare: Eliminating the 7 Most Common Bottlenecks 

Clinical workflow in healthcare is the backbone of every patient interaction inside a hospital. It determines how fast a patient moves from intake to diagnosis, how accurately information transfers between care teams, how completely a record is documented before it reaches…

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top-ai-healthcare
January 29, 2026

How Can AI Patient Intake Transform Your Healthcare Operations

AI patient intake is the use of custom automation, intelligence, and workflow design to collect, validate, and route patient information accurately across the intake patient journey, reducing operational friction, improving compliance, and accelerating access to care at scale making it one…

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Why choose CaliberFocus for ML & Deep Learning?

CaliberFocus delivers AI and machine learning development services that combine deep machine learning and deep learning expertise with production-grade MLOps. As a trusted machine learning service provider, we help organizations move models from experimentation to scalable production, delivering measurable business impact, accuracy, and long-term value.

  • Production ready ML and DL models
  • Business first model design and ROI focus
  • Scalable MLOps and automation pipelines
  • High accuracy, reliability, and governance

Security & Compliance

caliberfocus certification

Ready to transform your business? Contact us today.