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Data Factory · Synapse · Fabric · Databricks · Data Lake · Power BI · Oracle Data

Azure Data
Engineering & Analytics

Enterprise data engineering on Azure spans ingestion, transformation, storage, and analytics. eAppSys designs and delivers the right Azure data architecture, with Oracle EBS and Fusion Cloud data as a first-class source.

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Azure Data Engineering & Analytics

Azure Data Engineering & Analytics — Service Areas

“Azure Data Engineering & Analytics” eAppSys delivers Azure Data Engineering & Analytics services across data ingestion, transformation, storage, business intelligence, and AI-ready data platforms. We use Azure Data Factory, Microsoft Fabric, Synapse Analytics, Databricks, Azure Data Lake and Power BI to build scalable data solutions, with Oracle EBS and Oracle Fusion Cloud data integrated as first-class enterprise sources.

Data Ingestion — Azure Data Factory

  • ADF pipeline development — batch and incremental ingestion
  • Oracle EBS data extraction — direct Oracle DB connector via SHIR
  • Oracle Fusion Cloud REST API and HCM Extract ingestion
  • Self-hosted Integration Runtime (SHIR) for on-premises connectivity
  • Trigger types — scheduled, tumbling window, event-based
  • Copy Activity, Mapping Data Flows, and Wrangling Data Flows

Data Lake & Storage Architecture

  • Azure Data Lake Storage Gen2 — hierarchical namespace, RBAC
  • Medallion architecture — Bronze (raw), Silver (cleansed), Gold (curated)
  • Delta Lake format — ACID transactions, schema enforcement, time travel
  • Data lifecycle — hot/cool/archive tiering and retention policies
  • Storage account design — per environment, private endpoints
  • Partitioning strategy for query performance optimisation

Transformation — Synapse, Fabric & Databricks

  • Azure Synapse Analytics — dedicated and serverless SQL pools
  • Microsoft Fabric Lakehouse and Warehouse — unified analytics on OneLake
  • Azure Databricks — PySpark and Scala for complex transformation
  • dbt — SQL-based transformation with lineage, testing, documentation
  • Spark streaming — real-time transformation for event-driven pipelines
  • T-SQL stored procedures and Synapse pipelines for batch processing

Analytics & Power BI

  • Power BI semantic model design — star schema, measures, calculations
  • Report and dashboard development for business self-service
  • Power BI Embedded — surfacing analytics in operational portals
  • Row-level security (RLS) — dynamic models based on Entra ID identity
  • Fabric Direct Lake — semantic models on Delta Lake, no import refresh
  • Power BI Gateway for on-premises and Oracle source connectivity

Oracle Data Integration

  • Oracle EBS GL, AP, AR, HR data in Azure Data Lake via ADF
  • Oracle Fusion Cloud HCM and Finance data via REST and HCM Extracts
  • Oracle Database direct connection via ADF self-hosted Runtime
  • GoldenGate CDC — near-real-time Oracle change capture into Azure
  • DMOne™ data quality — profiling and cleansing before landing
  • Oracle data in Power BI alongside Azure and Microsoft 365 sources

AI-Ready Data & Analytics

  • Azure AI Search — vector indexing for RAG applications
  • Semantic models as AI context for Azure OpenAI grounding
  • Microsoft Fabric Copilot — AI-powered data preparation and notebooks
  • Real-time analytics — Fabric Eventstream and KQL Database
  • Azure Machine Learning feature store from Gold layer data
  • Microsoft Purview — data catalogue, lineage, and classification

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Delivery Approach

Delivery Approach

eAppSys delivers Azure engagements in a structured phased approach — clear outputs and decision gates at every stage.

01

Design

Data architecture, source mapping, medallion design, semantic model, security

02

Build

ADF/Fabric pipelines, Delta Lake, transformation logic, Power BI reports

03

Test

Data quality validation, reconciliation to source, performance, RLS testing

04

Deploy

CI/CD pipeline, monitoring, Power BI governance, documentation handover

Why eAppSys

Your Azure Partner

Oracle Data First-Class

eAppSys treats Oracle EBS and Fusion Cloud data as primary sources — ADF connectors, HCM Extracts, Oracle REST APIs, and GoldenGate CDC all feeding Azure Data Lake natively.

Fabric-Forward Architecture

eAppSys designs new data platforms on Microsoft Fabric — OneLake, Lakehouse, Warehouse, and Direct Lake semantic models — Microsoft's strategic direction for unified Azure analytics.

AI-Ready by Design

Every eAppSys data platform is AI-ready from the start — Delta Lake for vector indexing, Purview for governance, and semantic models that ground Azure OpenAI responses in enterprise data.

Business-Facing Analytics

eAppSys delivers Power BI analytics that business users actually use — star schema semantic models, self-service report design, and row-level security based on Entra ID identity.
Common Questions

Frequently Asked Questions

What Azure data engineering services does eAppSys provide?
eAppSys provides six areas: Data Ingestion (ADF, Oracle EBS/Fusion extraction, SHIR, triggers); Data Lake Architecture (ADLS Gen2, medallion architecture, Delta Lake, lifecycle); Transformation (Synapse, Fabric, Databricks, dbt, Spark streaming); Analytics and Power BI (semantic models, reports, RLS, Embedded, Direct Lake); Oracle Data Integration (EBS/Fusion data via ADF, GoldenGate CDC, DMOne™); and AI-Ready Data (Azure AI Search, Fabric Copilot, Purview, ML feature store).
Microsoft Fabric is Microsoft’s unified analytics platform — combining Data Factory, Synapse, Power BI, Data Activator, and Real-Time Intelligence on OneLake storage. eAppSys designs new data platforms on Fabric — Lakehouse for Bronze/Silver/Gold Delta Lake storage, Warehouse for SQL transformation, Notebooks for PySpark, and Direct Lake semantic models querying Delta Lake without import refresh. Fabric is Microsoft’s strategic direction for Azure analytics.
Via several patterns: Oracle EBS via ADF self-hosted Integration Runtime (direct Oracle DB connector for on-premises EBS); Oracle Fusion Cloud via HCM Extracts, Finance REST APIs, and BICC extract to Azure Data Lake; GoldenGate CDC for near-real-time change capture from Oracle Database into Azure; and DMOne™ for data quality profiling and cleansing of Oracle data before landing in Azure.
The medallion architecture organises data in three layers. Bronze (raw): data landed exactly as received — Oracle EBS tables, Fusion REST responses — in Delta Lake, no transformation. Silver (cleansed): deduplicated, validated, type-cast data with business rules applied. Gold (curated): business-facing aggregated datasets in star schema serving Power BI semantic models and AI applications. eAppSys implements medallion using Delta Lake on ADLS Gen2 or Microsoft Fabric OneLake.
Four approaches: Vector indexing — Azure AI Search indexes enterprise data as vectors for RAG applications. Semantic model grounding — Power BI semantic models surface structured data to Azure OpenAI via Fabric Copilot. Data governance — Purview classifies sensitive data and builds lineage so AI applications know what they are consuming. Feature store — Azure ML feature store curated from the Gold medallion layer for ML model training.
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