Logistics & Transportation/Software Development
Driver Performance and Fleet Management Solution
Telematics platform for fleet, driver, and vehicle management
View projectData Warehouse
Client Challenges
Reporting And Data Challenges That Lead To Warehouse Initiatives
Companies rarely start by asking for a specific platform. They usually start with reporting that takes too much time to produce, puts unnecessary load on operational systems, or generates numbers that different teams interpret differently. These are common situations that lead companies to consider data warehouse services.
Finance, sales, and operations may pull data from different sources and apply different filters or calculation rules, so the same metric reaches leadership with different values. Management meetings then turn into reconciliation sessions. The disagreement often comes from extraction logic and definitions rather than from the underlying source data itself.
Analytical queries run against databases designed primarily for transactional workloads, and complex reports can affect application performance during business hours. Teams respond by limiting report complexity, reducing refresh frequency, or moving reporting into overnight windows. Analytical capabilities then remain constrained by what the operational system can support.
Analysts spend significant time exporting, joining, cleaning, and correcting data before analysis can begin. Recurring reports may depend on manual steps understood by only one or two people. As reporting demand grows, more of the team’s capacity goes into preparation rather than analysis.
Transformation logic may be distributed across scripts, spreadsheet formulas, database queries, and calculations embedded inside BI reports. When a figure looks wrong, tracing it back to the source can take substantial time. During an audit, financing round, or due diligence process, missing lineage becomes a business issue rather than only a technical inconvenience.
Service Overview
The Work That Makes A Warehouse Usable In Practice
Solbeg designs, builds, migrates, and supports data warehouse solutions for companies that need a governed analytical layer instead of multiple disconnected reporting flows. An engagement can combine several of these areas depending on the existing environment and reporting requirements.
Review of source systems, reporting requirements, data volumes, refresh expectations, existing infrastructure, and current bottlenecks. The output is a target architecture, platform-selection criteria, and a recommended delivery sequence.
Design of warehouse layers, business entities, relationships, historization rules, and metric definitions agreed with the people responsible for using and approving them. The model establishes how data will be interpreted before pipelines and reporting are built around it.
Development of ingestion and transformation pipelines with orchestration, incremental processing, validation, error handling, and controlled reprocessing. Source systems can include databases, files, applications, and external APIs.
Data warehouse development services that turn the approved architecture and model into a working platform. The scope can include migration of existing workloads, parallel operation, reconciliation against current reports, and staged cutover.
Preparation of governed datasets, reusable business definitions, and semantic models that BI platforms and analytical applications can consume without requiring a separate custom extract for every recurring question.
Validation and reconciliation rules, freshness and completeness checks, pipeline monitoring, and optimization of storage and query patterns so reliability, performance, and operating cost remain visible as the environment grows.
Problem & solution
The problem
Definitions and ownership matter as much as storage technology
Platform selection often receives the most attention at the beginning of a warehouse initiative, but it is rarely the only factor that determines whether the project succeeds. The same business entity may be represented differently across source systems. A customer in the CRM, a payer in billing, and an account in the ERP can refer to overlapping real-world entities without sharing the same identifiers or definitions. No storage platform resolves those differences automatically.
The people who understand these systems and can clarify the business meaning of the data are usually also responsible for running day-to-day operations. Warehouse work therefore competes with releases, reporting deadlines, and operational issues. When decisions are made only in short intervals between other responsibilities, context can be lost and progress becomes difficult to maintain.
Another risk is choosing a platform before the data model, workload profile, integration requirements, and refresh expectations are understood. Pipelines and reports may then need to be redesigned after implementation has already started, and the actual scope becomes visible only after the budget and timeline have been set.
All of this has to happen while existing reporting remains operational. Finance still needs month-end reporting, operational teams still need dashboards, and management still needs established figures. The new warehouse therefore has to be introduced without removing the reporting processes the organization already relies on.
Solbeg's approach
Source behavior and business definitions are established before final platform decisions
Data warehouse consulting at Solbeg begins with source profiling and documented definitions of the core entities and metrics required by the business. Where multiple systems represent the same entity differently, matching and ownership rules are agreed explicitly so the model reflects defined business logic rather than whichever source happens to be integrated first.
The engineering work can be carried by a dedicated Solbeg team while the client’s specialists provide system knowledge, confirm definitions, and review outputs at defined points. This allows the project to progress without requiring the people who operate critical source systems to become the primary warehouse implementation team.
Platform selection follows the model, workload profile, refresh requirements, security constraints, and operational expectations. Data volumes, concurrency, cost model, existing licenses, and internal operating capabilities are evaluated together. This creates a clearer basis for choosing enterprise data warehouse solutions than selecting a platform first and adapting the project to it afterwards.
Delivery can proceed domain by domain. Each dataset and reporting area is reconciled against the existing output it is intended to replace before users are asked to switch. Current reporting remains available during the transition, and the old path is retired only after the new one has been validated.
When this service fits
When Building Or Restructuring A Warehouse Can Be The Right Next Step
Companies commonly consider data warehousing solutions when data integration, historical analysis, reporting consistency, or platform reliability become difficult to address efficiently within the current setup.
Two platforms may hold overlapping customer, product, order, or financial records while both remain operational. Consolidated reporting requires matching and transformation rules that neither application provides on its own. A warehouse can create a combined analytical view while the source systems continue operating independently.
A hardware refresh, license change, data center exit, cloud program, or end-of-support deadline can force a decision about where analytical data will live. Migration provides an opportunity to review modeling and pipeline decisions that accumulated over time instead of automatically reproducing them in the new environment.
Dashboards may already exist, but users find numbers that conflict with operational systems or with one another. In these cases, the issue often sits below the BI platform in inconsistent extracts, transformations, and metric definitions. A governed data layer gives reporting a more consistent foundation.
Auditors, investors, regulators, or internal stakeholders may need to know what a figure looked like at a particular point in time, while transactional systems retain only the current state. Warehouse historization can preserve relevant changes according to defined business and retention requirements.
A model may work on a manually assembled dataset but still lack a repeatable production source for training, feature generation, inference, and monitoring. Versioned pipelines and governed historical data can provide the stable supply layer needed for operational analytics or machine learning.
An existing environment may have been built by a contractor or employee who is unavailable, with limited documentation of pipelines, dependencies, models, and business logic. Work then begins with mapping the current state before major changes are introduced.
Cloud or infrastructure spend may increase even when data growth and user counts remain relatively stable. Common causes can include repeated full processing, unsuitable partitioning, inefficient storage layouts, and expensive query patterns. In these situations, the engagement may focus on optimization rather than replacement.
Scope of Work
Roles And Responsibilities Inside A Warehouse Delivery Team
Team composition depends on whether the engagement involves assessment, a new build, migration, optimization, or ongoing development. Solbeg’s data warehousing services can cover architecture, modeling, engineering, migration, analytics, platform operations, documentation, and lineage, with responsibilities divided according to the client’s internal capabilities and the agreed scope.
Data warehouse architecture
Assessment of the current environment, target architecture, platform-selection criteria, and migration or implementation sequence. Architecture decisions are documented together with their rationale, making data warehousing consulting easier to review and maintain after the original project team changes.
Data modeling
Design of warehouse layers, business entities, relationships, keys, grain, and historization rules. Metric definitions are established with relevant business owners so reports across departments can reuse the same calculation logic.
Data engineering
Development of ingestion and transformation pipelines, orchestration, dependency handling, incremental loads, validation, and reprocessing logic for supported source systems.
Warehouse migration
Migration of warehouse objects, scheduled processing, datasets, and dependent workloads to a new environment. The scope can include parallel operation, reconciliation with the current system, and a defined cutover plan for individual domains.
BI and analytics engineering
Data analytics services can include the development of semantic models, governed datasets, and reporting-ready data structures on top of the warehouse. Priority reports can be rebuilt or re-pointed against the new model, with previously implicit definitions documented during the process.
Data quality engineering
Reconciliation between source systems and warehouse output, validation of transformation logic, and testing of freshness, completeness, uniqueness, and referential integrity where relevant. Monitoring behavior can also be verified for failed or delayed loads.
Platform and DevOps engineering
Environment setup, deployment automation for warehouse code, access controls, monitoring, backup, and operational configuration. Compute and storage consumption can be tracked so platform cost remains visible during and after implementation.
Documentation and lineage
Model documentation, data lineage, operational runbooks, and supporting information that allows the client’s team to understand how source data becomes warehouse output. This part of data warehouse services is important for making ongoing operation and future changes less dependent on undocumented knowledge.
Why Solbeg
What Changes When Warehouse Work Is Run By An Engineering Partner
Companies comparing data warehouse consulting companies often find similar high-level service lists. Practical differences appear in how the project is sequenced, how source-system issues are handled, how engineering responsibilities are divided, and what the client’s team receives after delivery.
Engineering capability can extend into source systems
For companies evaluating a data warehousing company, Solbeg brings broader software engineering capabilities to the applications and integrations that produce warehouse data as well as to the analytical platform itself. When a data-quality problem originates upstream, the engagement can investigate whether correcting it at the source is more maintainable than permanently compensating for it in transformation logic.
Delivery can be organized by data domain
The project can be sequenced so that one business domain reaches a usable and reconciled state before the next is completed. This creates review points during the program and allows priorities to change between domains without invalidating work that is already in production.
Platform decisions are tied to the actual workload
Selection criteria can be documented against data volumes, query patterns, concurrency, refresh requirements, security constraints, operating model, and cost expectations. The reasoning remains available to the client for future architecture reviews instead of being dependent on undocumented assumptions.
Documentation and handover are treated as deliverables
Model documentation, lineage, pipeline behavior, and runbooks are produced alongside implementation rather than reconstructed only at the end. For companies evaluating a data warehouse consultancy, this provides the client’s team with a clear understanding of the platform and the ability to maintain and extend it after handover, while Solbeg can continue supporting further development and optimization under the agreed engagement model.
Development can continue after the first production release
A warehouse evolves as new sources, business units, metrics, and reporting requirements appear. The same engineering team can continue with development, optimization, and support when required, allowing technical context to carry forward instead of being rebuilt for every new phase.
Comparison
Build A New Warehouse Or Restructure The Reporting Layer You Already Have
Companies with an existing analytical environment may need to decide whether to build a new warehouse alongside the current one or improve the existing platform incrementally. The appropriate approach depends on the condition of the current model, platform constraints, migration requirements, and how much change the business can absorb at once.
Engagement Process
Warehouse Delivery Across Assessment, Build, And Production Rollout
Data warehouse consulting services at Solbeg begin with understanding the data, workloads, and reporting requirements. The depth and sequence of delivery are then adapted to a new warehouse build, migration, or major restructuring program before final platform decisions are made.
-
Assessment
Solbeg architects and engineers review source systems, current reporting, data volumes, refresh requirements, known failures, access constraints, and relevant operating requirements together with the client’s data and system owners. The result is a documented current state, identified risks, and the main gaps the target solution needs to address.
-
Model and architecture definition
Core business entities, warehouse layers, metric definitions, historization requirements, and relevant data-quality rules are designed and reviewed with the stakeholders who own those definitions. Platform options are then evaluated against the workload profile, and the delivery sequence is agreed.
-
First domain build
One business domain is implemented end to end, including ingestion, transformation, the analytical model, quality checks, and the required reporting or consumption layer. Results are reconciled against existing trusted outputs so differences can be explained before rollout. Engineering and documentation practices are established during this stage.
-
Rollout and migration
Remaining domains are delivered in the agreed sequence. Reports and downstream consumers are moved as datasets pass validation and reconciliation. Where an existing warehouse is being replaced, old and new paths can operate in parallel until the relevant domain is approved for cutover.
-
Handover or continued development
Model documentation, lineage, runbooks, monitoring, access information, and operating procedures are transferred to the team that will own the platform. Solbeg can then hand over responsibility or continue with additional sources, metrics, optimization, and platform development under an agreed engagement.
Collaboration model
How Warehouse Work Is Divided Between Your Team And Ours
Warehouse development depends on both technical implementation and business ownership of the underlying data. Responsibilities, communication, and ongoing operation are organized according to the agreed delivery and operating model.
Completed projects
View all projectsSome of our recent work
Real products our team has designed, developed, and launched together with clients, from idea to release.
Finance & Banking/Software Development
DocEx Secure Document Distribution
SaaS platform for secure distribution of confidential documents
View project
Healthcare/Mobile Development/QA
Mental Health Assessment Platform
Mobile app for mental health assessment and personalized support
View projectFAQ
Questions Companies Ask Before Starting Warehouse Work
No. The platform decision depends on data volumes, refresh requirements, workload patterns, cost model, security constraints, residency requirements, existing infrastructure, and what the organization is prepared to operate. Cloud, private-cloud, and on-premises options can all be evaluated where they are technically and commercially appropriate.
Yes, in many cases. A data warehouse sits underneath the reporting layer, so replacing the BI platform is not automatically required. What often changes is the source the BI tool uses: direct extracts and operational-system queries can be replaced with governed warehouse models. Existing reports may still need to be re-pointed and their calculations checked against agreed metric definitions.
The starting point can be profiling the data and observing how the source system actually behaves. Field usage, value distributions, update patterns, relationships, and relevant edge cases are reviewed and then confirmed with people who understand the system operationally. Findings are documented as part of the engagement so previously implicit behavior becomes easier to maintain.
They can remain operational while corresponding domains are implemented and validated. Existing reporting is only retired after the replacement dataset and reports have been reconciled according to the agreed rollout plan. This is one reason warehouse programs are often delivered domain by domain instead of through one platform-wide cutover.
That depends on the complexity of the platform and how frequently it is expected to change. A relatively stable environment still requires monitoring and occasional maintenance, while a warehouse that continuously adds sources, metrics, and workloads needs ongoing engineering capacity. The operating model can involve handover to the client’s team, continued Solbeg involvement, or a combination of both.
Yes. Beginning with one domain such as finance, sales, or operations can establish the modeling, pipeline, reconciliation, and documentation approach before the scope expands. The wider architecture should still be considered so the first domain does not create constraints for later areas.
Cost is treated as an architecture and workload concern from the beginning. Incremental processing, appropriate partitioning, storage lifecycle policies, query optimization, workload monitoring, and visibility into compute and storage consumption can all contribute to a more understandable cost profile. Tracking these drivers from the first production domain makes it easier to explain why spend changes as the warehouse grows.
Related Services
You may also need
End-to-end services to build, launch, and grow your digital product.