Lakehouse & streaming at scale
Medallion architectures on Fabric, Databricks, and S3 — with reliable batch, event-driven, and change-data-capture pipelines your operators can run with confidence.
- Lakehouse / OneLake
- Streaming & MQ
- Data contracts
Shard-native data & cloud engineering
Governed data estates that scale on Azure and AWS
We design and run modern data platforms — lakehouse architectures, internal developer platforms, real-time pipelines, and responsible AI — for organizations that need auditability, reliability, and room to grow.
- 25+ years in data & cloud delivery
- Dual-cloud Azure & AWS depth
- Sector-ready public & private programs
Capabilities
Four planes of a modern data estate
We engineer across the full stack — not isolated tools — so ingestion, platform, intelligence, and trust reinforce each other as your estate matures.
Cloud foundations & IDP
Landing zones, Kubernetes, Terraform, and CI/CD — platform engineering that shortens lead time without sacrificing guardrails.
- AKS / EKS
- GitOps
- FinOps
Analytics & responsible AI
Semantic models, executive dashboards, and production ML — including RAG and GenAI patterns with governance built in from day one.
- Power BI · Tableau
- MLOps
- Azure OpenAI · Bedrock
Security, lineage & compliance
Role-based access, encryption, cataloguing, and audit trails — essential for regulated environments and cross-shard analytics without compromising control.
- Microsoft Purview
- Policy-as-code
- Zero-trust patterns
About SDS
Named for how enterprise data actually scales
Shard is more than our name — it is how modern estates are built: partitioned for performance, joined for insight. For over 25 years we have helped public and private sector teams move from fragmented sources to governed platforms that survive leadership changes, audits, and exponential growth.
We are specialists, not generalists — deep on Microsoft Azure and Amazon Web Services, opinionated about engineering quality, and accountable for what we put into production.
- Outcome-first delivery
- Roadmaps tied to measurable business KPIs — not vanity migrations.
- Engineering discipline
- Versioned infra, tested pipelines, documented runbooks, observable workloads.
- Transferable capability
- Your teams leave stronger — with patterns, playbooks, and skills retained.
Delivery approach
How we partner from discovery to run-state
Flexible engagement — advisory sprints, embedded squads, or managed operations — aligned to procurement realities in enterprise and government.
Discover & design
Current-state assessment, target architecture, TCO modelling, and a phased roadmap with clear decision gates.
Build & integrate
Lakehouse foundations, pipeline automation, platform hardening, and integration with identity, security, and ops tooling.
Operate & optimize
SLOs, cost governance, incident response, and continuous improvement — FinOps and reliability engineering included.
Scale & evolve
New domains, geographies, and AI workloads — extending the estate without re-architecting from scratch.
Practices
Depth where it matters — on the tools you run
Four disciplines, one delivery standard. Every practice shares the same bar for documentation, observability, and handover.
Data engineering
Production-grade ingestion and transformation — idempotent pipelines, schema evolution, and lakehouse patterns that analysts and ML teams can consume safely.
- Microsoft Fabric · Azure Data Factory · Azure Databricks
- AWS Glue · Lambda · EMR · Kinesis · MSK · Lake Formation
- Medallion layers on OneLake and Amazon S3
Platform engineering
Landing zones, AKS / EKS, Terraform, Azure DevOps, and GitHub Actions — internal platforms that accelerate delivery while meeting security baselines.
Analytics & BI
Power BI, Tableau, Fabric semantic models, QuickSight, Synapse, Redshift, and Athena — self-serve analytics with a governed semantic layer underneath.
AI & machine learning
Azure OpenAI, Azure ML, SageMaker, and Bedrock — from proof-of-value through MLOps, model monitoring, and responsible-AI guardrails.
Cloud ecosystem map
Platforms we implement today across engineering, analytics, and AI workloads.
Microsoft Azure
Data engineering
- Microsoft Fabric
- Azure Data Factory
- Azure Databricks
- Azure Synapse
- Azure Event Hubs
- Microsoft Purview
Platform engineering
- Azure Kubernetes Service
- Terraform
- Azure DevOps
- GitHub Actions
- Azure Landing Zones
Analytics
- Power BI
- Tableau
- Fabric Lakehouse
- Azure SQL
- Fabric Data Warehouse
AI & ML
- Azure OpenAI Service
- Azure Machine Learning
- Azure AI Search
- Microsoft Copilot Studio
Amazon Web Services
Data engineering
- Amazon S3
- AWS Glue
- Amazon EMR
- AWS Lambda
- Amazon Kinesis
- Amazon MSK
- AWS Lake Formation
Platform engineering
- Amazon EKS
- Terraform
- AWS Step Functions
- AWS CloudFormation
- AWS Control Tower
Analytics
- Amazon Redshift
- Amazon Athena
- Amazon QuickSight
- Tableau
- AWS Clean Rooms
AI & ML
- Amazon SageMaker
- Amazon Bedrock
- Amazon Q
Proof
Trusted where stakes are high
Long-horizon programs across regulated industries — where delivery quality and architectural clarity outlast individual projects.
Public sector
Financial services
Healthcare
Insurance
Energy & utilities
Retail
Manufacturing
Technology
They did not treat our estate as a one-off migration. SDS mapped how data actually moved through the organization, designed for sharded scale, and left us with platforms we still extend — years after go-live.
- 25+ Years shipping data & cloud programs
- 4 Integrated practices — data, platform, analytics, AI
- 2 Hyperscaler ecosystems — Azure & AWS
Microsoft Fabric
Databricks
Terraform
Kubernetes
Power BI
Tableau
Azure OpenAI
Amazon Bedrock
Contact
Scope your next data initiative
Whether you are modernizing a warehouse, standing up a lakehouse, or operationalizing GenAI — share your context and we will come back with a clear, no-fluff next step.
- Architecture reviews & discovery workshops
- Fixed-scope builds and embedded squads
- Azure, AWS, and dual-cloud strategies