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Microsoft Fabric Consulting
Architecture and delivery across OneLake, Lakehouse, Warehouse, Data Factory, semantic models, Power BI and governance.
Microsoft Partner & Databricks Partner · Data Engineering Consultancy
Build a data platform your business can trust, scale and use.
Saish Infotech Ltd is a UK-based Microsoft Partner and Databricks Partner helping organisations modernise legacy data estates, improve platform performance, strengthen governance and deliver scalable Microsoft Fabric, Azure Databricks and Power BI solutions — from architecture through hands-on production delivery.
20+ yearssoftware & data delivery experience
~4 TB/daylarge-scale platform experience
End-to-endarchitecture, engineering & governance
Microsoft Partner & Databricks Partner delivering senior engineering expertise for organisations that need practical delivery, not just strategy slides.
Services
Modern data-platform consulting focused on business outcomes.
We help organisations modernise legacy data estates, improve platform performance, strengthen governance and deliver scalable Microsoft Fabric, Databricks and Azure data solutions that are secure, maintainable and ready for production.
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Databricks Engineering
Delta Lake, Unity Catalog, PySpark, Structured Streaming, Workflows, CI/CD and production performance optimisation.
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Azure Data Platforms
ADF, ADLS Gen2, Event Hubs, Synapse, Key Vault and secure cloud-native integration and orchestration patterns.
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Data Architecture
Target architecture, platform selection, medallion patterns, dimensional modelling and migration roadmaps.
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Governance & Data Quality
Purview, Unity Catalog, lineage, access-control models, quality rules, ownership and trusted reusable data products.
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Performance & Cost
Workload profiling, Spark tuning, data layout, compute utilisation and prioritised platform optimisation plans.
Microsoft Fabric
Move from Fabric experimentation to a production platform.
Microsoft Fabric supports end-to-end data workflows across ingestion, transformation, real-time processing, analytics and reporting over OneLake. We help clients turn that capability into an architecture matched to real workloads, governance and operating requirements.
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Fabric readiness & architecture
Workload discovery, target-state design, workspace/domain strategy and adoption roadmap.
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Lakehouse, Warehouse & OneLake
Storage patterns, medallion layers, data-product boundaries and analytics serving design.
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Power BI & semantic models
Trusted models and reporting datasets built on governed enterprise data.
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Governance & DevOps
Access controls, Purview, deployment, source control, testing and operational standards.
Azure Databricks
Databricks architecture and engineering for demanding workloads.
From greenfield lakehouse delivery to performance remediation, we focus on scalable processing, secure governance and production engineering practices.
Lakehouse architecture
Medallion layers, Delta Lake, data-product patterns, workload boundaries and serving architecture.
PySpark & streaming
Scalable transformations, joins, incremental processing, Event Hubs/Kafka, checkpoints and recovery patterns.
Unity Catalog
Catalog/schema strategy, least-privilege access, lineage and governed data sharing.
CI/CD & testing
Source control, automated tests, Databricks Asset Bundles and repeatable environment promotion.
Performance optimisation
Spark plans, shuffles, skew, data layout, clustering, compute sizing and cost efficiency.
Azure integration
ADLS, Event Hubs, Data Factory, Key Vault, managed identities and Azure DevOps integration.
Batch + streamingScheduled and near-real-time workloads
Governed by designSecurity and catalogue patterns included early
Production focusedTesting, deployment and operations built in
Cost awareOptimisation tied to workload behaviour
How we work
Start with the problem. Build only what creates value.
Engagements can start with a focused architecture or health-check review, then move into delivery where needed.
01 · DISCOVER
Understand context
Business outcomes, current platform, workloads, pain points and constraints.
02 · ASSESS
Find the gaps
Architecture, performance, governance, quality and operational risks.
03 · DESIGN
Create a roadmap
Target architecture, decision records, priorities and measurable outcomes.
04 · DELIVER
Implement
Engineering, migration, CI/CD, optimisation and technical leadership.
Selected experience
Production delivery, not just platform theory.
Anonymised examples show how we approach scale, reliability, governance and reusable data delivery. Where client confidentiality prevents publishing exact commercial metrics, we focus on the technical outcomes and engineering improvements delivered.
Azure Databricks · Large-scale data
Greenfield platform processing multi-terabyte daily workloads
- Designed and delivered Azure Databricks capabilities for approximately 4 TB of daily data.
- Combined Event Hubs streaming with batch processing and Delta Lake storage.
- Applied Unity Catalog, CI/CD, testing and reusable engineering patterns to improve maintainability, deployment consistency and operational control.
- Worked with stakeholders to shape trusted customer-oriented data models designed for consistent downstream analytics and reuse.
Scale~4 TB/day
PatternBatch + streaming
FocusGovernance & delivery
Azure Analytics · Data Engineering
Cloud pipelines and dimensional analytics models
- Built orchestration with Azure Data Factory and processing with Databricks/PySpark.
- Developed facts and dimensions for trusted downstream reporting.
- Used reusable engineering patterns and production-focused delivery practices to improve consistency across pipelines and releases.
- Collaborated with business and technical stakeholders to translate reporting requirements into maintainable, analytics-ready data products.
CloudAzure
ProcessingPySpark
OutcomeReusable data products
Why Saish Infotech
Senior technical capability without heavyweight consultancy overhead.
Work directly with an experienced data engineer and technical architect who can engage with business stakeholders, architects, analysts and engineering teams — and remain hands-on when delivery requires it.
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Architecture + engineering
One technical partner from design decisions through implementation.
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Microsoft Partner & Databricks Partner expertise
Workload-led architecture and engineering across Microsoft Fabric, Azure and Databricks rather than forcing a single technology.
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Stakeholder communication
Translate business requirements into clear, implementable technical outcomes.
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Production mindset
Security, governance, testing, deployment and supportability designed in early.
Core technology coverage
Microsoft FabricOneLakePower BIAzure DatabricksUnity CatalogDelta LakePySparkStructured StreamingADFADLS Gen2Event HubsSynapsePurviewAzure DevOpsTerraformSQL
Free Data Platform Assessment
What should you improve first?
Answer three quick questions and receive a recommended first step for your platform, whether you are considering Fabric, Databricks, migration, governance or performance optimisation.
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Fabric readiness
Understand whether your current workloads and operating model are ready for Microsoft Fabric.
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Databricks optimisation
Identify likely areas for performance, governance and cost improvement.
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Modernisation roadmap
Clarify the first practical steps for moving from legacy ETL or fragmented data platforms.
Start a conversation
Planning a Microsoft Fabric, Databricks or Azure data initiative?
Tell us your current platform, key challenge and target outcome. We can help with architecture reviews, platform builds, migration, optimisation, governance and technical delivery leadership.