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Data platform optimisation
Data platform optimisation for Microsoft Fabric, Azure and Power BI
Fix slow reports, failing pipelines and rising capacity costs. Konsolute finds the real bottleneck across Microsoft Fabric, Azure and Power BI, then makes measured improvements to refresh times, query performance and consumption, without changing the numbers your teams rely on.
WHAT THIS HELPS YOU ACHIEVE
Improvements grounded in workload evidence and service needs
Find the constraints behind slow, unreliable or expensive data services, then validate the effect of targeted changes.
Identify the real bottleneck
Distinguish source, processing, model, query and capacity issues before changing the platform.
Improve reliable delivery
Address failures, delayed data and repeated manual recovery so consumers receive the expected outcome.
Match resources to demand
Review workload timing, concurrency and consumption against the business need and operating costs.
Keep improvements maintainable
Introduce monitoring, release controls and ownership that help teams detect regression and prioritise future work.
OUR DATA PLATFORM OPTIMISATION SERVICES
Optimise Fabric, Azure and Power BI across the workload
Commission a focused performance review, stabilise an unreliable data service or establish a repeatable improvement programme across the platform.
Platform health & workload assessment
Build an evidence baseline across the agreed Fabric, Azure and Power BI estate. Review topology, dependencies, usage and incidents, measuring current performance before recommending any change.
Pipeline reliability & refresh
Investigate failed, delayed or wasteful ingestion, transformation and refresh. Assess dependencies, incremental loading, retries and schedules, checking completeness and recovery as well as run time.
Query & processing optimisation
Profile SQL queries, Spark jobs and transformations against realistic workloads. Review joins, data layout and statistics, targeting unnecessary scans, expensive operations and repeated work.
Power BI performance improvement
Assess semantic models, DAX, report interactions and refresh dependencies. Find whether delays come from the model, source, storage mode or visuals, then test changes and confirm results still match.
Capacity & consumption planning
Use platform and workload metrics to assess utilisation, concurrency, throttling and contention. Compare tuning, scheduling, isolation and capacity options, with licensing and Azure costs identified.
Operational controls & lifecycle
Improve deployment, monitoring and support around the platform. Set alerts for failures that matter to the business, strengthen release and recovery, and assign owners for changes and credentials.
CAPABILITIES & DELIVERY SCOPE
The evidence behind a faster, more dependable data service
Optimisation follows the complete path from source extraction to user consumption. Each workstream connects a measured problem to a controlled change and validation.
Workload baseline & service expectations
Map the critical paths, consumers and the freshness or response times they depend on. Measure representative durations, failure patterns and demand, recording how volume, concurrency and caching affect each comparison.
- Dependencies and service requirements
- Representative workload measurements
- Demand and baseline assumptions
Pipelines, orchestration & recovery
Trace execution across source access, copying, transformation and refresh. Assess dependency order, retries, overlapping schedules and repeat loads, and check completeness so shorter runs never hide missing data.
- Run and dependency analysis
- Incremental processing and schedule review
- Completeness and recovery validation
SQL, Spark & data access patterns
Profile expensive queries and jobs before changing compute. Review joins, filtering, data types, scans and table maintenance, and for Spark separate inefficient processing from queueing or capacity limits.
- Query and job profiling
- Platform-specific tuning opportunities
- Representative result and performance checks
Semantic models, DAX & report experience
Investigate model size, relationships, calculations and visual interactions with the source path in view. Assess storage mode and refresh, test key filters and concurrent use, and keep definitions and access intact.
- Model and DAX bottlenecks
- Storage, refresh and visual interactions
- Business-result and access validation
Fabric capacity & workload contention
Use capacity and item-level evidence to understand consumption and throttling alongside schedules. Treat interactive demand, background jobs, Spark concurrency and source limits separately, then fix the actual cause.
- Capacity and item-level metrics
- Contention, throttling and concurrency analysis
- Scheduling, isolation and sizing options
Storage, retention & processing efficiency
Assess data copies, retention, file and table layout and repeated preparation against workload needs. Agree maintenance and lifecycle options with data owners, recording recovery and audit dependencies first.
- Storage and duplicated-work review
- Layout and maintenance assessment
- Retention ownership and recovery requirements
Release control & regression prevention
Define how pipeline, model and platform changes are tested and promoted. Record the expected improvement and how it is measured, with rollback criteria, and compare results and permissions before accepting a change.
- Versioning and controlled deployment
- Before-and-after validation
- Recovery and regression criteria
Monitoring, incidents & continuous review
Align alerts and reviews with freshness, failed processing and user impact. Assign incident, configuration and capacity owners, and keep runbooks and a backlog, checking that growth has not undone earlier decisions.
- Business-relevant telemetry and alerts
- Operational ownership and runbooks
- Review cadence and improvement backlog
TECHNICAL DESIGN DECISIONS
Protect the data outcome while improving performance
The best change is the one that addresses the constraint and preserves the required behaviour, access and recovery.
Evidence before capacity
Establish where work is waiting or consuming resources. More capacity will not fix inefficient queries, source limits or a poorly designed refresh on its own.
Correctness alongside speed
Validate records, measures and access after every change. A faster query or refresh is no improvement if it changes the result or exposes information.
Cost alongside service requirements
Assess consumption against freshness, concurrency and resilience needs. Record assumptions and measure the effect rather than promising savings up front.
Controlled change and ownership
Make changes through an agreed release process with recovery and acceptance criteria. Name the people who maintain configuration, capacity and evidence.
OUR APPROACH
How we deliver data platform optimisation
Six stages take you from a measured baseline to validated improvements, with before-and-after evidence for every change we make.
Define the service priorities
Identify the data services and user journeys affected, with freshness, performance and recovery expectations.
Establish the workload baseline
Collect configuration, usage, processing and incident evidence over a representative period.
Diagnose the constraints
Trace bottlenecks across sources, pipelines, compute, models, reporting and shared capacity.
Prioritise and implement changes
Rank opportunities by impact, confidence, effort and dependencies, then deliver agreed changes.
Validate outcomes and trade-offs
Compare performance, consumption, correctness and access against the baseline and acceptance criteria.
Embed monitoring and review
Hand over findings, runbooks and configuration, with ownership and a maintained improvement backlog.
Measure.Tune.Validate.
WHERE PLATFORM OPTIMISATION CAN HELP
Start with the performance or reliability problem affecting users
A focused review can establish the cause and delivery scope before a wider platform change is considered.
- Diagnose slow or unreliable pipelines and delayed reporting refreshes
- Investigate Fabric capacity contention, throttling or unexpected consumption
- Tune SQL queries, Spark jobs and transformation bottlenecks
- Improve Power BI models, DAX or report interactions
- Review duplicated storage, repeated full loads and unnecessary processing
- Establish meaningful monitoring, release validation and clearer support ownership
WHAT WE DELIVER
Prioritised changes with evidence of their effect
The scope identifies the assessment, implementation and verification outputs for the workloads under review.
Workload and health baseline
Dependency views, service requirements, incident patterns and representative performance and consumption evidence.
Prioritised improvement plan
Diagnosed constraints, recommended changes, assumptions, effort and expected effects with decision ownership.
Implemented and validated changes
Agreed tuning or configuration assets, comparison results and documented trade-offs or remaining limits.
Operating improvement pack
Monitoring configuration, runbooks, ownership and a review cadence for continuing platform improvement.
FAQ
Data platform optimisation FAQs
Fabric capacity, pipelines, SQL and Spark, Power BI and cost.
We can assess and improve pipeline reliability, query and job performance, Power BI behaviour, capacity use and operational practices. The scope identifies the workloads, service requirements and evidence needed before implementation and validation.
Yes. We review the current configuration, dependencies and operating evidence before recommending what to tune or change. The engagement can focus on selected workloads rather than assume a platform migration or wholesale redesign is required.
Not necessarily. We assess the source of contention, consumption and delays first. Query tuning, workload scheduling, model changes or isolation may be relevant. Capacity changes are compared against those options and the service requirements.
We examine execution history, source behaviour, retries, concurrency, credentials and downstream dependencies. The aim is to distinguish data, configuration and resource causes, with recovery and completeness checks included in the remediation.
We profile representative queries or jobs and assess platform-specific tuning options. Data layout, filtering, joins, statistics, transformations and compute behaviour can all matter. Results are checked for correctness as well as duration or consumption.
We review job submission patterns, resource demand and relevant platform metrics. Spark admission and concurrency, broader capacity pressure and inefficient processing are not treated as interchangeable problems. The diagnosis informs scheduling, configuration or capacity recommendations.
Yes. We assess source access, model structure, DAX, storage mode, refresh dependencies and visual interactions. Important user scenarios and filters are tested before and after changes, with business definitions and permissions preserved.
We identify opportunities to reduce unnecessary processing or storage and match resources to demand. Estimated effects depend on usage, licences and service requirements. We distinguish recommendations from savings observed after approved changes, without guaranteeing a percentage reduction.
No. Storage candidates need an ownership and dependency review. Data may support recovery, history, audit or infrequent business use. Retention or deletion changes require an agreed decision and validation of their consequences.
We agree a baseline and representative workloads, then compare relevant durations, freshness, failures, consumption and user response. Demand, data volume and test conditions are recorded so the comparison is not based on unrelated before-and-after runs.
Yes, where included in scope. We can establish controlled changes, regression checks and business-relevant alerts, with runbooks and ownership that help the team maintain improvements and detect new constraints.
We can scope periodic reviews, incident investigation and enhancement work, with monitoring coverage and response commitments agreed explicitly. Handover defines responsibilities across platform administrators, data engineers, reporting owners and source-system teams.
TAKE THE NEXT STEP
Where is your data platform losing time or resources?
Tell us which pipeline, query, capacity or report is creating problems. Konsolute will help establish the evidence, prioritise the changes and validate improvements against the way your business needs the platform to operate.