Performance Sized to Your Workloads
We assess query patterns, concurrency, data volumes, and refresh needs, then configure the selected platform's compute and workload controls. Testing helps balance response times and operating cost.
Analytics
Enterprise Data Warehouse
One Governed Home for the Numbers Your Business Runs On
We design and build cloud data warehouses that bring business data into shared reporting models. Working with your finance, operations, and technology teams, we define metrics, validate source data, and deliver subject areas in manageable phases.
What We Deliver
Where We Make the Difference
Consistent reporting depends on agreed definitions and reliable data flows. We work with business owners to reconcile sources, model the information they need, and validate results before expanding to additional subject areas.
Models Built Around Reporting Needs
We select dimensional or other modeling approaches to suit your reporting requirements, source data, and maintenance needs. Definitions, relationships, and transformation rules are documented for the team that will operate them.
Reliable Source Integration
We build monitored ingestion and transformation pipelines from agreed business systems. Incremental loading or change data capture is used where supported and appropriate, with reconciliation and recovery procedures.
Shared Metric Definitions
We agree on KPI definitions with business owners and implement them in the selected modeling or semantic layer. Tests and documented integrations help keep supported reports consistent.
Our Approach
How We Deliver Enterprise Data Warehouse
Step 1: Scope by Subject Area
We analyze source systems, data volumes, reporting needs, and service levels, then choose a platform and phase the build by subject area, such as finance first.
Step 2: Model the Business, Then Load It
We design the reporting model for each subject area and build ingestion and transformation pipelines that load source data reliably and repeatably.
Step 3: Validate Data and Reporting
We apply agreed data-quality checks, reconcile key measures against approved baselines, and benchmark representative workloads. Findings are reviewed before user acceptance and rollout.
Step 4: Plan and Manage the Cutover
Where the migration calls for it, we run old and new reporting in parallel. Acceptance criteria, rollback arrangements, and operational ownership are agreed in advance, and we support user acceptance testing and hand over documentation your team can maintain.
Snowflake, Synapse, Redshift, or BigQuery: which should we choose?
It depends on the cloud you already run, your existing licenses, your team's skills, and cost, along with your security, governance, integration, and workload requirements. We compare the options against those needs and recommend one, with the reasoning written down.
Do we still need a data warehouse if we have a data lake?
Not necessarily. We assess whether your existing lake or lakehouse can meet reporting, governance, performance, and concurrency requirements. A separate warehouse may help, but it should solve a defined need rather than duplicate capabilities.
Can you migrate our on-premises warehouse to the cloud?
Yes. We assess schemas, pipelines, reports, and dependencies, then agree what to retain or redesign. Migration phases, reconciliation criteria, cutover, and rollback arrangements are planned around your operating requirements.
How do you keep costs under control after launch?
We configure the selected platform's available workload and cost controls, with usage monitoring and agreed review responsibilities. Optimization may include query tuning, scheduling, storage management, or compute adjustments. Ongoing reviews are included only where agreed in scope.
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