Data scattered across applications
Finance, sales and operations data sit in separate systems, so every combined view is built by hand.
We build reliable data pipelines, lakehouse and warehouse architectures and Microsoft Fabric solutions that bring your data together, keep it accurate and make it ready for reporting and analysis.
Data pipelines Lakehouse & Warehouse Microsoft Fabric Data quality
Good reporting depends on good data engineering: sources connected, data cleaned once, definitions agreed and loads monitored. Microsoft Fabric is one way to do this, and we recommend it only where it fits.
Illustrative pipeline run with sample figures, not client data.
The problems that usually sit behind slow, inconsistent or untrusted reporting.
Finance, sales and operations data sit in separate systems, so every combined view is built by hand.
Analysts export, clean and merge files themselves before any report can be produced.
Scripts and jobs that only one person understands, and that fail silently when a source changes.
The same customer, product or revenue figure differs between systems and reports.
Reports are out of date or fail to refresh, so people stop trusting them.
Each team keeps its own copy of the data, which drift apart and multiply storage and effort.
Older warehouses and ETL tools are hard to change, slow to load and costly to maintain.
When a number looks wrong, nobody can quickly trace where it came from or which step failed.
Approaches that worked for a few reports struggle as data volumes, sources and users grow.
From assessment to a running, monitored platform. Most projects combine several of these, scaled to your data and team.
Assess your current platform, sources, requirements and dependencies, and recommend a practical target architecture.
Reliable ingestion, transformation and loading, orchestrated and scheduled, with failures surfaced instead of hidden.
Fabric workspaces and items chosen for your requirements: lakehouses, warehouses, pipelines, notebooks and semantic models.
Storage and serving layers designed for how the data will be used, often in bronze, silver and gold layers.
Bringing data together from databases, business applications, files and APIs using suitable connectors and patterns.
Maintainable transformation logic in SQL, Power Query or Spark, documented and tested.
Structured, reusable models with business-friendly names and agreed definitions, ready for reporting.
Validation rules, reconciliation and monitoring, so problems are found before they reach a report.
Assess existing warehouses and ETL, then plan and carry out a move to a modern platform where it makes sense.
Access control, lineage, documentation and capacity awareness, so the platform stays manageable as it grows.
Not sure where to start? A short call is usually enough to identify the first sources and pipelines to tackle.
Book a Free ConsultationFabric brings data integration, engineering, warehousing, real-time analytics and Power BI onto one platform with shared storage in OneLake. These are the parts we use most, and when each one helps.
Fabric is a strong fit for organisations that already use Microsoft 365 and Power BI and want one managed platform. It is not the answer to every workload: your existing infrastructure, data volumes, governance needs, team skills and licensing all affect the choice, and we will say so when another approach fits better.
A reference pattern we adapt to each project. Not every solution uses every layer or component.
Databases, ERP and CRM applications, files, SharePoint and APIs.
Pipelines and dataflows, with incremental loads where the source supports them.
Lakehouse and warehouse layers on OneLake: raw (bronze), cleaned (silver), modelled (gold).
SQL, Power Query or Spark, producing conformed, documented tables.
Validation rules, reconciliation, lineage and access controls at each layer.
Power BI semantic models and reports, and other downstream uses of curated data.
Across every layer:OrchestrationIncremental loadingMonitoring and alertsData lineageAccess controlsDocumentation
Illustrative scenarios showing the problem, the technical approach and the benefit. They are examples, not client results.
How we assess and plan a move from an existing warehouse, database or ETL setup to a modern platform such as Microsoft Fabric.
Inventory workloads, jobs, sources, volumes and pain points.
Find which reports, jobs and teams depend on each dataset.
Choose lakehouse, warehouse and pipeline patterns for each workload.
Move in phases, starting where the value is highest and the risk lowest.
Compare row counts, totals and key figures between old and new.
Size capacity, tune heavy workloads and plan for growth.
Recreate access rules, labels and ownership in the new platform.
Run in parallel where needed, switch over and document operations.
Migration is rarely a straight copy. Some workloads move with little change; others need redesign to work well on the new platform. We assess each workload first, migrate in phases, and validate the new results against the old before switching over.
Planning a move from a legacy warehouse or ETL tool? Talk it through with us before you commit to an approach.
Book a Free ConsultationDesign and implementation practices we apply. They support your organisation's own security, privacy and compliance requirements; they do not replace them.
Microsoft Entra ID identities with workspace roles and item permissions.
Users and service principals get only the access they need.
Sensitivity labels and Purview where your organisation uses them.
Rules for completeness, uniqueness and valid values at each layer.
Lineage views and documented sources, owners and definitions.
Run history, failure notifications and quality dashboards.
Development, test and production workspaces with deployment pipelines or Git.
Runbooks and a named owner for every production pipeline.
Retention rules and personal data handled to your applicable requirements.
Capacity usage monitored and heavy workloads scheduled and tuned.
Seven stages, scaled to the size of the work. Timelines depend on your sources and requirements, so we agree them after the assessment.
Understand business objectives, current systems, data sources and constraints.
Agree the target architecture and the components that fit.
Define workstreams, dependencies, priorities, validation criteria and release approach.
Build ingestion, transformation, modelling and orchestration.
Check data quality, correctness, performance, security and failure behaviour.
Release with controls and document how the platform is run.
Track reliability, cost and performance, and adapt to new requirements.
We start from the decisions and reports the data must support, then design the platform around them.
We also build the Power BI layer, so the data foundation is shaped for reporting. Business Intelligence services
We build on your existing Microsoft 365, Azure and Fabric setup rather than adding unrelated tools.
Validation, lineage and access design are part of the build, not an afterthought.
Clear structure, naming and documentation, so your team can run and extend the platform.
Monitoring, tuning and support after go-live. Serving businesses since 2020 from our India-based team.
Our team holds role-based Microsoft certifications covering Fabric data engineering, Fabric analytics engineering and Power BI. These are held by members of our team, not necessarily by every team member, and they are individual credentials, not a Microsoft partnership.
Ingesting and transforming data, and securing, monitoring and optimising analytics solutions in Microsoft Fabric, using SQL, PySpark and KQL.
View the certification on Microsoft LearnDesigning, creating and managing analytical assets in Microsoft Fabric, such as semantic models, warehouses and lakehouses.
View the certification on Microsoft LearnPreparing, modelling and visualising data in Power BI, where most curated data is finally used.
View the certification on Microsoft LearnTwo recent projects with a data pipeline at their core. Client names are withheld for confidentiality.
Data engineering: Python standardises each customer's forecast file, files land in SharePoint by forecast date, and Power Query appends them into one versioned table mapped to internal part numbers.
Reported outcome: a forecast cycle went from almost five days to less than one day, as reported by the project team.
PythonSharePointPower QueryPower BIDAX
Read the case studyData engineering: daily machine sensor data is collected into SharePoint, keeping a full history, and modelled in Power BI to calculate hours per shift.
Outcome: the daily reporting cycle runs with no manual steps.
Power AutomateSharePointPower BIDAX
Read the case studyMicrosoft Fabric is Microsoft's software-as-a-service analytics platform. It brings data integration (Data Factory), data engineering, data warehousing, data science, real-time intelligence and Power BI together, all storing data in OneLake.
Typically: assessing your data sources and platform, building pipelines to ingest and transform data, designing lakehouse or warehouse storage, modelling data for reporting, adding data quality checks and monitoring, and documenting and supporting the result.
A lakehouse stores files and Delta tables and suits raw, semi-structured and large-scale data, usually processed with Spark. A warehouse stores structured, modelled tables queried with SQL. In Fabric both store data in Delta format in OneLake, and many solutions use both.
Power BI is part of Fabric. Semantic models can read lakehouse and warehouse tables, including in Direct Lake mode, which reads Delta tables in OneLake without importing a separate copy.
OneLake is Fabric's single, logical data lake for the whole organisation, built on Azure Data Lake Storage Gen2. All Fabric workloads store data in it, and shortcuts let you reference data in other storage without copying it.
When you want data integration, engineering, warehousing and Power BI on one managed platform, especially if you already use Microsoft 365 and Power BI. It may not be the right fit for every workload; existing investments, skills, governance needs and licensing all matter.
Often, but not always as-is. Some pipelines move with little change; others need redesign to fit Fabric patterns. We assess each workload and validate the results against the original before cutover.
ETL transforms data before loading it into the target. ELT loads raw data first and transforms it inside the platform, which suits lakehouses and modern warehouses. Many solutions use a mix of both.
Pipelines include retries and failure paths, send alerts to the owner, and log every run. We add data quality checks so that loads which succeed technically but produce bad data are also caught.
By defining validation rules for key tables, reconciling totals with source systems, quarantining failed rows, and documenting sources, transformations and owners so lineage can be traced.
The number and type of sources, data volumes, transformation complexity, security and governance needs, the reports to support, your team's skills and the capacity size. We estimate after an assessment.
Fabric runs on capacity (F SKUs bought through Azure), sized in capacity units. On capacities below F64, people who view Power BI content need a Pro or Premium Per User licence; on F64 and above, viewers with a free licence and a viewer role can view it. Premium Per User alone does not provide Fabric capacity.
Data Factory provides a large set of connectors for cloud and on-premises sources, on-premises data can be reached through a gateway, and mirroring and shortcuts can bring in data from some platforms without full copies. We confirm support for each of your sources during assessment.
Product and licensing details are based on Microsoft's What is Microsoft Fabric and Fabric licenses and capacity documentation and can change; confirm them for your tenant.
Discuss your data engineering challenges, modernisation goals and Microsoft Fabric requirements with our team, and get a clear view of the next steps.
Team certified in DP-700, DP-600 and PL-300. Serving businesses since 2020 from our India-based team.