Ecommerce/Web Development
Ecommerce Web Portal
Ecommerce platform for selling and managing premium wear
View projectBig Data
Client Challenges
Challenges That Appear When Data Volume Outgrows The Current Platform
Companies usually start looking at big data consulting services when the existing platform still works but has become difficult to scale predictably. The issues are often operational rather than purely analytical and can affect processing windows, infrastructure cost, historical access, and the effort required to add new data sources.
Processing that once completed overnight starts running into the working day, and a failed job can delay everything that depends on it. Business users receive reports that are hours or a day behind the source systems, while reruns compete with the next scheduled load instead of clearing the backlog.
Compute and storage spend increases as new sources, reports, and workloads are added, but it is difficult to attribute that cost to a specific pipeline, team, dataset, or use case. Without workload-level visibility, budgeting becomes harder and cost concerns can begin limiting what the data team is able to build.
Older data may be moved into colder storage or archives to keep the active environment manageable. As a result, year-over-year, cohort, and other long-range analysis can require restoration work or engineering support before the question can even be answered.
Event streams, telemetry, logs, and external feeds are integrated one at a time, each with its own ingestion logic, schema handling, and failure behavior. Without reusable patterns, the engineering effort required for each additional source stays high and the maintenance burden grows with every integration.
Service Overview
Platform Engineering For High-Volume And High-Frequency Data Workloads
Solbeg’s big data services cover the design, implementation, and operation of platforms built for large volumes, high-frequency ingestion, and mixed data formats. Work can begin with an existing environment that is approaching its limits or with a new platform designed around distributed processing from the start.
Definition of storage layout, processing architecture, partitioning strategy, file or table formats, serving layers, and environment topology based on data volume, latency requirements, query patterns, cost constraints, and the operating capabilities available on the client side.
Batch and streaming ingestion for event feeds, application logs, device telemetry, transactional extracts, and external data sources, including schema evolution, replay, validation, and recovery from failed processing.
Implementation and optimization of transformation workloads that run across distributed compute environments, including incremental processing, resource allocation, backfills, execution tuning, and handling of large joins or other resource-intensive operations.
Retention policies, storage tiering, compaction, partitioning, and query optimization designed to keep historical data accessible without treating every dataset as if it required the same performance and storage characteristics.
Preparation of governed datasets, reusable business models, feature inputs, and serving layers so that BI tools, data science teams, and downstream applications can work from consistent definitions instead of maintaining separate private extracts.
Big data software development services can include the software surrounding the platform, such as APIs over large datasets, data-facing application components, internal tooling, and integration with existing products. These components are delivered as part of broader big data solutions rather than as isolated scripts.
Problem & Solution
The problem
At scale, data volume changes which engineering decisions become critical
At moderate data volumes, inefficient joins, poor partitioning, or unnecessary full reprocessing may initially appear as performance constraints that can be managed by adding resources. At larger scale, the same design decisions can determine whether a workload finishes within the required processing window and whether its infrastructure cost remains sustainable.
These constraints often become visible while the current environment is still in active use. Adding compute or storage can provide additional capacity, but if the underlying constraint is caused by processing design, partition strategy, data layout, or repeated full scans, increasing resources alone may not address it.
Distributed processing also introduces operational requirements that do not appear in the same way in smaller systems. Jobs can fail partially, data can arrive late or out of order, schemas can change, and reruns need to avoid duplicating or corrupting results. These behaviors have to be considered as part of platform design rather than added only after failures occur in production.
Operating the platform also requires experience with large distributed workloads. Architecture decisions that appear reasonable at smaller scale can expose performance, reliability, or cost constraints only after data volumes and workload concurrency increase.
Solbeg's approach
Architecture decisions are based on measured workloads
We begin with the workloads themselves: data volumes, growth rates, processing times, query patterns, failure history, resource use, and available cost information. Big data consulting at this stage is diagnostic, helping distinguish between constraints caused by insufficient capacity and those caused by architecture or workload design.
Where the existing environment is being scaled mainly by adding resources, we first identify workloads that can be improved through changes such as partitioning, incremental processing, storage format, data layout, or execution configuration. This establishes a more realistic baseline before a larger platform migration or redesign is considered.
Operational behavior is defined alongside the pipeline. Retry rules, safe reruns, late and out-of-order data handling, schema evolution, monitoring, and alerting are specified according to the role and criticality of each workload, so expected behavior is clear before incidents occur.
Our big data development services can also be structured so the client’s engineers remain involved in the platform rather than receiving it only at handover. Architecture decisions, engineering conventions, and operating procedures are documented, and the operating model for enterprise big data solutions is defined together with the team that will maintain the environment after delivery.
When this service fits
Situations Where A Big Data Platform Can Become The Appropriate Engineering Choice
A big data platform becomes relevant when data volume, processing latency, retention requirements, workload diversity, or product demands make a conventional single-database architecture increasingly difficult to operate. Solbeg assesses these constraints before defining whether distributed infrastructure is required and where it should be applied.
Pricing, allocation, fraud detection, monitoring, routing, or other workflows may require data within minutes rather than after the next batch cycle. In these cases, selected data flows can move toward event-driven or streaming processing while workloads that do not require low latency can remain batch-based.
An on-premises or self-managed cluster may be running on aging hardware, unsupported software versions, or an operating model that has become expensive to maintain. Migration then needs to account for the workloads, dependencies, and business processes that already rely on the environment rather than focusing only on the target technology.
Devices, vehicles, sensors, applications, or connected products may generate more event data than the transactional system should retain or process. A separate ingestion and storage path can preserve analytical value without forcing the operational database to carry workloads it was not designed for.
Regulatory, contractual, audit, or analytical requirements may require long-term retention while users still need practical access to historical records. Balancing storage cost with queryability becomes an architecture challenge and is a common reason to involve a big data services company.
A model may perform well on manually prepared extracts while the production pipelines required for training, historical recomputation, feature generation, or inference do not yet exist. Building a repeatable data supply becomes a prerequisite for moving the ML workload beyond experimentation.
A software product may need to serve dashboards, reports, or analytical functionality over large multi-tenant datasets. Unlike internal BI, these workloads are part of the product experience and have to account for response time, concurrency, tenant isolation, and predictable operation under user-driven demand.
An experiment may already demonstrate value while lacking orchestration, monitoring, access control, cost management, deployment automation, or documented operations. The next stage is to preserve the useful logic while adding the engineering required for production use.
Scope of Work
Responsibilities Involved In A Big Data Engagement
Team composition depends on the scope, existing platform, workload types, and the responsibilities retained by the client’s internal team. Solbeg’s big data development services can cover architecture, batch and streaming engineering, platform operations, analytics, application engineering, QA, and delivery coordination.
Big data architecture
Definition of storage architecture, processing engines, partitioning and file or table formats, ingestion patterns, environment topology, and integration with existing systems. The architecture also establishes engineering standards for pipelines and platform components.
Batch data engineering
Implementation and optimization of distributed transformation workloads, including incremental processing, backfills, dependency management, schema evolution, and tuning for runtime, resource use, and infrastructure cost.
Streaming data engineering
Event ingestion and continuous processing, including delivery semantics, late and out-of-order events, windowing, state management, replay, and the interaction between streaming pipelines and persisted datasets.
Data platform and infrastructure engineering
Environment management, deployment automation, CI/CD for data workloads, compute and cluster configuration where applicable, monitoring, alerting, backup, security controls, and visibility into workload resource usage.
Analytics engineering
Transformation of processed data into documented, tested, reusable business models with consistent definitions for metrics and dimensions used by BI, reporting, and downstream consumers.
Application engineering
Development of APIs over large datasets, data-facing product components, integrations, and internal tools. For teams comparing a big data software development company with a pure analytics vendor, Solbeg’s broader software engineering capabilities allow the platform scope to extend into the applications that consume and expose the resulting data.
Data quality and QA
Validation of transformation logic against source systems, reconciliation and volume checks, testing of failure and rerun behavior, and verification that monitoring and alerting behave according to the agreed requirements.
Delivery coordination
Management of scope, backlog, technical dependencies, release sequencing, stakeholder communication, and the working interface between Solbeg specialists and the client’s engineering and business teams.
Why Solbeg
What Companies Get From Solbeg On High-Volume Data Programs
Solbeg can combine architecture, data engineering and analytics, platform operations, QA, and application development within one engagement. This keeps the data platform, its pipelines, and the software that consumes the data within the same engineering context.
One engagement can cover the full data delivery chain
Ingestion, transformation, modeling, data quality, platform engineering, and the applications that consume the resulting data can be coordinated within the same delivery scope. This reduces handoffs between teams responsible for different layers of the platform.
Platform and software engineering are available within the same company
Large data programs often require application work alongside pipelines, including APIs, integrations, user-facing analytics, and internal tools. For teams looking for a big data development company with broader software engineering capabilities, Solbeg can support both the platform and the software built around it within the same engagement.
Work can begin from the environment you already have
An existing platform can be assessed, stabilized, optimized, or extended before a full rebuild is considered. This allows improvements to be introduced incrementally while current reporting and business processes continue to operate.
Cost and scalability are considered together
Storage layout, processing strategy, retention, query behavior, and infrastructure choices affect both performance and operating cost. Evaluating these factors together helps avoid architectures that meet a performance target but create an unsustainable cost profile as data volume grows.
The engagement can end in handover or continue
Pipelines, models, dependencies, architecture decisions, and operating procedures can be documented so the client’s internal team is able to take ownership. If ongoing support is required, Solbeg can continue developing or operating the platform under an agreed engagement model. This is one practical difference between a big data consulting company focused only on architecture and a delivery partner that can remain involved with big data solutions in production.
Comparison
Managed Cloud Services Or A Self-Operated Big Data Stack
One of the architecture decisions is how much of the underlying platform should be operated by a cloud provider and how much responsibility should remain with the client’s own team or delivery partner. The choice depends on operating capacity, workload profile, cost structure, data requirements, and the level of control required over the runtime.
Engagement Process
How A Big Data Engagement Moves From Assessment To Production
Solbeg structures a big data consultancy engagement around the existing environment, priority workloads, and the scope agreed at the start, with the depth and sequence of work adapted to the platform’s current state.
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Workload assessment
We review data sources, current and expected volumes, processing times, failure history, query patterns, available cost information, security requirements, and other known constraints. Solbeg engineers work with the relevant data and platform owners to establish the current state and identify the main bottlenecks.
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Architecture and scope definition
The target architecture, processing approach, storage strategy, retention model, and delivery sequence are defined against the findings from the assessment. Priorities, acceptance criteria, dependencies, team composition, and trade-offs between cost, latency, scalability, and operational effort are made explicit.
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Validation on representative workloads
Where appropriate, the proposed design is tested using representative data volumes and priority workloads before wider implementation. This provides evidence for performance, scalability, and cost assumptions while major architecture decisions can still be adjusted.
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Incremental implementation
Pipelines, storage layers, models, platform automation, monitoring, testing, and documentation are introduced in working increments. Existing processes can remain operational while new components are validated and adopted.
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Migration and cutover
Workloads move to the new platform in a defined sequence. For business-critical outputs, migration can include parallel running, reconciliation, and agreed rollback conditions before the previous implementation is retired.
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Handover or continued operation
Architecture decisions, pipelines, dependencies, runbooks, monitoring, support responsibilities, and operating procedures are documented for the team that will own the platform. Solbeg can transfer responsibility to the client’s engineers or continue platform development and operation under a separately agreed model.
Completed projects
View all projectsSelected projects we’ve delivered
A few examples of digital products we’ve built for our clients and the results they brought to their business.
Logistics & Transportation/Software Development
Driver Performance and Fleet Management Solution
Telematics platform for fleet, driver, and vehicle management
View project
Mobile Development/Public Sector/QA/Software Development
Digital Visa Application and Verification Platform
Mobile and web platform for vehicle service and ownership management
View projectFAQ
Questions Clients Ask Before Starting A Big Data Project
Not necessarily. The existing environment should first be assessed to determine whether its limits come from architecture and workload design or from genuine capacity constraints. Constraints such as poor partitioning, repeated full processing, inefficient storage formats, or unsuitable query patterns may be addressed without replacing the platform. A larger architectural change becomes more relevant when the required volume, latency, concurrency, retention, or workload mix cannot be supported reasonably by the current design.
Yes, work can begin within the client’s existing cloud or infrastructure environment. Moving between providers is a separate architecture and business decision with its own cost, migration effort, and operational risk. If a requirement cannot be met within the current environment, the constraint and available alternatives can be evaluated explicitly rather than assuming that migration is required.
Cost becomes easier to model once workload frequency, data volumes, query patterns, retention policies, storage tiers, and infrastructure choices are understood. The assessment can identify the main cost drivers and establish a baseline against which architecture alternatives are compared, but actual spend will still depend on how workloads evolve over time.
That depends primarily on how quickly the data has to influence a decision or process. If a delay of several hours does not affect the outcome, batch processing can be simpler to implement and operate. Streaming is usually introduced for flows where lower latency changes business or product behavior rather than applied automatically to the entire platform.
Existing reporting can continue while corresponding workloads are migrated incrementally. For important datasets and reports, outputs can be compared and reconciled before the new implementation becomes the primary source. Cutover can therefore happen workload by workload instead of through a single platform-wide switch.
Yes. Documentation, architecture decisions, runbooks, monitoring, and engineering standards can be produced during delivery, while the client’s engineers can participate throughout implementation and handover. Depending on the agreed engagement model, platform operations and ongoing support can either transfer to the client’s team or remain with Solbeg after the initial project is complete.
Look at the scope of responsibility beyond the target architecture. When comparing big data consulting companies, useful questions include how workloads are assessed before redesign, how migration and rollback are handled, who owns production operation after launch, and whether the same team can support both data engineering and the application layer around the platform. When evaluating big data services companies, it is also useful to clarify which responsibilities are delivered directly and which depend on separate suppliers.
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