Service Stream 02
Data Engineering & Analytics
apricot jam designs and delivers enterprise data platforms on Microsoft Fabric — enabling organizations to unify data engineering, warehousing, business intelligence, and AI capabilities on a single governed platform. We move clients from fragmented data infrastructure to production-grade analytics that drive decisions at scale.
StaffingManaged ServicesConsultingTraining
Key Capabilities
What We Deliver
01
Platform Migration & Modernization
Migration from legacy data warehouses and on-premise infrastructure to Microsoft Fabric — including OneLake architecture, Lakehouse design, and zero-ETL database mirroring.
02
Data Engineering & Pipeline Factory
Data Factory pipelines, Dataflows Gen2, Spark/Python notebooks, Eventstream real-time ingestion, and integration runtimes — building reliable, scalable data movement at enterprise scale.
03
Enterprise Data Warehousing
Synapse Data Warehouse, T-SQL endpoints, cross-database query, dbt integration, and zero-copy cloning — delivering high-performance analytical storage with full SQL compatibility.
04
Business Intelligence & Reporting
Power BI Premium, Direct Lake semantic models, paginated reports, calculation groups, and DevOps deployment pipelines — delivering governed, high-performance BI at enterprise scale.
05
Real-Time Intelligence
KQL-based real-time analytics, Data Activator alerting, and Eventstream processing — enabling operational intelligence on live data streams.
06
AI & Machine Learning
AutoML, Copilot for Fabric, Copilot for Power BI, AI Skills NLP querying, and AI embedding frameworks — embedding intelligence directly into the data platform.
07
Governance & Security
Purview Hub data governance, row-level and object-level security (RLS/OLS), capacity management, and data lineage — ensuring compliant, auditable data operations.
08
Advisory & Architecture Strategy
Data platform roadmap design, architecture assessments, and governance framework development — aligning data investment with enterprise analytics maturity goals.
Business Value
Outcomes That Matter
Infrastructure Cost Reduction
Consolidating fragmented data tools onto Microsoft Fabric eliminates redundant licensing, reduces data movement costs, and simplifies the operational footprint.
Faster Time-to-Insight
Direct Lake semantic models and real-time intelligence capabilities compress the time from data ingestion to business decision — from hours to minutes.
Scalable Data Architecture
Lakehouse architecture and OneLake virtualization support growing data volumes and new use cases without platform re-architecture.
Governed Self-Service Analytics
Purview-governed semantic models and role-based security enable business users to access trusted data independently — reducing IT bottlenecks.
AI-Ready Data Foundation
Unified, governed data in OneLake provides the clean, structured foundation required for reliable machine learning and AI model deployment.
Operational Risk Reduction
Real-time alerting via Data Activator and proactive data quality monitoring reduce the risk of decisions made on stale or incorrect data.
Use Cases
Where We Operate
Chief Data Officer
Fragmented data tools driving high cost and low trust
An enterprise running separate ETL tools, data warehouses, and BI platforms was spending heavily on integration and experiencing low data trust. apricot jam designed a Microsoft Fabric migration — consolidating the stack onto OneLake, reducing tooling cost, and establishing a governed data foundation.
VP of Analytics
Power BI reports running slowly on large datasets
Executive dashboards were timing out on large datasets, limiting adoption. apricot jam implemented Direct Lake semantic models and optimized the data model — delivering sub-second query performance on datasets that previously required import mode with scheduled refresh.
Operations Director
No real-time visibility into operational KPIs
A logistics operation was making decisions on day-old data. apricot jam implemented Eventstream real-time ingestion, KQL analytics, and Data Activator alerting — giving operations teams live visibility into throughput, exceptions, and SLA performance.
Data Engineering Lead
Complex ELT pipelines failing under data volume growth
Legacy ETL pipelines were failing under increasing data volumes and requiring constant manual intervention. apricot jam redesigned the pipeline architecture using Data Factory and Spark notebooks on Fabric — delivering reliable, scalable data movement with automated monitoring.
Compliance & Governance Lead
No data lineage or access control across the analytics estate
Regulatory requirements demanded data lineage and access governance that the existing platform could not provide. Apricot jam implemented Purview Hub governance, RLS/OLS security, and data lineage tracking — enabling audit-ready data operations.
Delivery Approach
How We Engage
01
Assess
Evaluate current data architecture, tooling landscape, data quality, governance maturity, and analytics use case priorities.
02
Architect
Design the target Fabric architecture — Lakehouse topology, ingestion patterns, semantic model strategy, and governance framework.
03
Build
Implement pipelines, Lakehouses, data warehouses, semantic models, and BI reports — with DevOps deployment pipelines from day one.
04
Validate
Execute data quality validation, reconciliation against source systems, and business acceptance testing of reports and dashboards.
05
Govern
Implement Purview governance, security policies, capacity management, and data lineage — establishing compliant, auditable operations.
06
Optimize
Monitor platform performance, expand use case coverage, and evolve AI capabilities through ongoing managed services and advisory.
Technologies
Platform & Tooling
Core Platform
Data Engineering
Data Warehousing
Business Intelligence
AI & Real-Time
Governance & Security