Data warehousing

Comprehensive data engineering solutions tailored for Microsoft Dynamics 365 and enterprise systems

DATA WAREHOUSING

Supported platforms

We design and implement modern data platforms on Azure to support enterprise-scale analytics, BI reporting, and AI/ML initiatives. Our focus is on building scalable, secure, and governed Data Warehouses or Lakehouses that integrate deeply with Microsoft’s ecosystem, particularly D365 F&O, Fabric, and Power BI.

Azure Synapse analytics

Enterprise data warehouse with integrated analytics

Best for:
  • Large-scale data warehousing
  • Complex analytics
  • Real-time insights
Key capabilities:
  • Massively parallel processing
  • Real-time analytics
  • Integrated ML
  • Serverless queries
Pricing model:

Pay-per-use model

Microsoft Fabric

Unified analytics platform with lakehouse architecture

Best for:
  • Modern data platform
  • Self-service analytics
  • Unified data estate
Key capabilities:
  • OneLake storage
  • Real-time analytics
  • Integrated Power BI
  • AI/ML integration
Pricing model:

Capacity-based pricing

Azure Data Lake storage Gen2

Scalable data lake with hierarchical namespace

Best for:
  • Data lake architecture
  • Big data analytics
  • Archive storage
Key capabilities:
  • Unlimited scale
  • Fine-grained security
  • POSIX compliance
  • Multi-protocol access
Pricing model:

Storage + transaction costs

Azure Databricks

Apache Spark-based analytics platform

Best for:
  • Data science
  • ML workflows
  • Streaming analytics
Key capabilities:
  • Collaborative notebooks
  • Delta Lake
  • MLflow integration
  • Auto-scaling
Pricing model:

Compute + Databricks units

Core offerings

Comprehensive data platform capabilities for enterprise-scale analytics

Architecture & platform design

Define the right architecture based on volume, velocity, and variety of data

  • Choose between Azure Synapse Analytics, Microsoft Fabric, or Azure Data Lake Storage Gen2
  • Layered architecture with Raw, Cleaned, and Curated zones for traceability and agility
  • Scalable design patterns for petabyte-scale data processing
  • Cost-optimized storage and compute strategies

Analytical data modeling

Build Star Schemas and Data Vaults for fast query performance and historical tracking

  • Develop conformed dimensions and fact tables across Finance, Supply Chain, Projects, HR
  • Implement time-partitioned or snapshot-based data strategies
  • Design for performance and auditability requirements
  • Create reusable data models across business functions

ETL / ELT pipelines

Use Azure Data Factory, Synapse Pipelines, or Fabric Data Pipelines

  • Orchestrate complex workflows with error handling, retries, and dependency tracking
  • Integrate structured (SQL-based) and unstructured (JSON, XML, files) data sources
  • Implement incremental loading and change data capture
  • Performance-optimized data transformation processes

Monitoring & observability

Implement robust data pipeline logging, alerting, and health checks

  • Use Azure Monitor and Log Analytics for comprehensive monitoring
  • Establish data quality frameworks and reconciliation mechanisms
  • Enable lineage tracking with Purview or Fabric lineage
  • Real-time alerting and automated issue resolution

6-layer lakehouse architecture

Modern data architecture for enterprise scale

01.

Data sources

ERP systems, e-commerce platforms, IoT devices, external APIs

  • D365 F&O
  • SAP
  • Shopify
  • IoT hub

02.

Ingestion layer

Real-time and batch data ingestion with error handling

  • Azure data factory
  • Event hubs
  • Stream analytics
  • Logic apps

03.

Storage layer (raw zone)

Immutable data storage with lifecycle management

  • Data lake gen2
  • Delta lake
  • Parquet
  • JSON

04.

Processing layer (cleaned zone)

Data transformation, cleansing, and enrichment

  • Azure synapse
  • Databricks
  • Fabric pipelines
  • Azure functions

05.

Analytics layer (curated zone)

Business-ready data models and aggregations

  • Star schema
  • Dimensional models
  • Semantic layer
  • KPI calculations

06.

Consumption layer

Self-service analytics and reporting

  • Power BI
  • Tableau
  • Excel
  • Custom apps

Governance & compliance

Enterprise-grade security and compliance controls

01

Data governance

  • Data classification and sensitivity labels
  • Automated data discovery and cataloging
  • Business glossary and metadata management
  • Data stewardship workflows

02

Security & compliance

  • Role-based access control (RBAC) and data masking
  • Encryption at rest and in transit
  • Audit logs and retention policies
  • Compliance with GDPR, SOX, HIPAA regulations

03

Data quality

  • Automated data quality monitoring
  • Data profiling and anomaly detection
  • Business rule validation
  • Data reconciliation frameworks

04

Lineage & impact

  • End-to-end data lineage tracking
  • Impact analysis for changes
  • Dependency mapping
  • Automated documentation generation

Typical deliverables

Complete data platform with operational handover

Infrastructure
  • Azure landing zone for data warehouse/lakehouse
  • Scalable compute and storage architecture
  • Network security and connectivity setup
  • Disaster recovery and backup strategies
Data integration
  • Integrated datasets from D365 F&O, legacy ERP, flat files, APIs
  • Real-time and batch data ingestion pipelines
  • Data transformation and cleansing processes
  • Change data capture implementation
Analytics models
  • Analytics-ready models for reporting and AI
  • Star schema and dimensional modeling
  • Pre-built business metrics and KPIs
  • Historical data preservation strategies
Operations
  • Documentation for architecture, pipelines, and data flows
  • Operational handover with runbooks and monitoring guides
  • Performance tuning and optimization
  • User training and knowledge transfer

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