Monolithic Architectures

Sphere 10 builds modular monoliths with ASP.NET Core, C#, Blazor or MVC and a deliberately owned SQL data model. We structure orders, billing, scheduling and inventory into modules inside one deployable application, using EF Core, NHibernate or provider-level data access for the selected SQL Server or PostgreSQL database. The delivery has explicit module contracts, coherent local transactions, background-work ownership and a release process that operates the application as one system.

Our .NET delivery combines browser or Windows interfaces, application services, persistence and background processing in one deliberately composed host. The design keeps business decisions independent of presentation and database details, while making cross-module operations explicit. We modernise existing monoliths as well as building new systems, using working vertical slices to establish boundaries without losing current behaviour. You receive tested business workflows, a maintainable code structure and the deployment, migration and support material needed to keep the whole application moving forward.

💡 Choose Boundaries Before Projects — Start with business responsibilities and the data each module owns. The source folders and assemblies should express those decisions, rather than define them accidentally.

Structure the Application Around Business Capabilities

Modules own their business rules, write operations and tables or schemas. C# project/assembly references, dependency-injection registration and application-service interfaces express the permitted dependencies, while DTOs keep the public contract distinct from persistence entities. An order requests a billing decision through the billing module's interface. Shared identity, logging and configuration are composed at the host, with cyclic dependencies and cross-module table access examined in code review.

Map capabilities, module owners and the business rules each module enforces.
Define application contracts that avoid uncontrolled access to another module’s data.
Separate presentation, orchestration, domain decisions and persistence responsibilities.
Introduce architecture checks that make unintended dependency growth visible.

Compose One Host With Explicit Transaction Ownership

ASP.NET Core and the .NET hosting model provide application entry points, dependency injection and managed startup. We compose module services deliberately and keep request-specific state within the correct lifetime. Sphere10 Framework .Data offers IDAC and DACScope mechanisms for explicit connection and local transaction ownership where appropriate. A business operation commits the related database changes together; external calls and long-running jobs receive their own completion policy. This keeps transaction boundaries understandable and avoids holding database locks while waiting for another system.

Define service lifetimes, configuration ownership and startup dependencies.
Keep related consistency rules inside an explicit database transaction boundary.
Move slow external work into durable, inspectable background operations.
Provide cancellation, graceful shutdown and useful failure reporting for hosted jobs.

Modernise Through Working Vertical Slices

Modernisation starts with a complete MVC, Blazor or API journey and its SQL effects. We retain its calculations and authorization behaviour while introducing an application-service contract, bounded EF Core/NHibernate loading and a versioned database migration. Background processing runs through owned .NET hosted services with explicit durable state. Each slice is checked for query counts, transaction duration and interface compatibility before another module is changed.

Capture representative workflows and important legacy edge cases before refactoring.
Deliver complete user outcomes with compatible data and interface changes.
Measure query counts, payload size and transaction duration during each slice.
Keep migration and rollback procedures aligned with the application release.

Accept and Support the Application as One System

Acceptance covers the integrated release, including permissions, cross-module workflows, reports, background work and recovery. We exercise realistic data volumes and simultaneous users, then retain the evidence behind agreed performance targets. The delivery includes source, build instructions, database migration scripts, operational configuration and support diagnostics. We define how your team deploys a new version, recognises an unhealthy installation and restores service. The resulting monolith has visible internal ownership and a manageable operating model as its capabilities expand.

Test whole workflows across modules, including rejection and partial-failure paths.
Rehearse installation, upgrade, data migration and restoration procedures.
Deliver logs and health signals that identify the affected business operation.
Hand over module contracts, source, tests and an actionable maintenance backlog.

Technologies & Integration

ASP.NET Core application architecture — Compose a single deployable application with internal capability boundaries, entry points and owned dependencies.
EF Core — Implement explicit persistence units of work and selected-provider transaction behaviour inside application operations.
NHibernate — Configure mappings, session lifetime, concurrency and transaction ownership where NHibernate is the selected ORM.
.NET Generic Host — Compose dependency injection, configuration, logging and startup/shutdown for the application and hosted work.
OpenTelemetry .NET — Correlate requests, module operations and database work while measuring the delivered system as one application.

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Broker Architectures

Sphere 10 builds message-driven applications with RabbitMQ, Apache Kafka, Azure Service Bus or Amazon SQS according to the delivery and ordering contract. C# services publish and consume business commands or events with explicit message identity, routing, persistence and failure handling. The architecture covers order fulfilment, document processing and integration workflows from the database change through broker delivery to the consumer's committed result.

We implement producers, consumers, persistence and operator controls together. A broker can hold and route work, but the application still owns consistency, idempotency and recovery. Our C# services make those responsibilities explicit through message identities, durable processing records and controlled retry policies. The delivery includes the message contracts, deployment configuration, observability and failure exercises required to run the system. Your team can investigate an individual operation, resume interrupted work and evolve the participating applications with a clear compatibility model.

💡 Define the Business Effect of Acknowledgement — Agree which durable state must exist before a consumer acknowledges a message, and how the system recognises the same message when it is delivered again.

Design Message Contracts and Routing Deliberately

RabbitMQ exchanges and queues, Kafka topics and partition keys, Service Bus queues/topics, and SQS queue types have different routing and ordering models. We choose the topology around the owner of each command or event, define consumer groups or subscriptions, and version the message schema. Correlation, duplicate-detection identity and large-payload object references are part of that contract. Required ordering is stated per business key and verified with concurrent producers and consumers.

Assign each command one responsible handler and define event subscribers explicitly.
Document message identity, correlation, schema version and compatibility rules.
Identify ordering requirements at the business-key level rather than assuming global order.
Set payload limits, expiry rules and handling for unknown contract versions.

Join Database Changes to Message Delivery

We use an application outbox where a business update and an outgoing message must be recorded in one local database transaction. A relay publishes that recorded work and handles publisher confirmation. Consumers retain inbox or processing identifiers so redelivery can be recognised before repeating the business effect. Sphere10 Framework .Data supplies explicit local transaction mechanisms where selected; the outbox, inbox and retry policy are implemented and tested parts of the application. Broker acknowledgement and business completion remain separate, observable steps.

Commit the business change and its outbox record within one local transaction.
Track publishing outcomes and retain enough state to retry uncertain delivery safely.
Record consumer message identity alongside the committed business result.
Acknowledge only after the durable processing boundary required by the contract.

flowchart TD
  U["Business application"] --> A["ASP.NET Core API"]
  A --> D["SQL transaction + outbox"]
  D --> B["Service Bus or RabbitMQ"]
  B --> W["Idempotent worker"]
  W --> E["ERP or CRM integration"]
  W --> R["Retry or exception queue"]
  R --> W
  classDef default fill:#eaf2ff,stroke:#2f77cb,color:#17365d,stroke-width:1.5px

Make Backlog and Failure Operable

The operating configuration defines RabbitMQ prefetch and manual acknowledgements, Kafka consumer offsets and lag, Service Bus locks and dead-letter handling, or SQS visibility and redrive policy. .NET worker concurrency follows downstream capacity, with durable retry decisions and inspected poison messages. Dashboards join queue or partition backlog, oldest work, consumer throughput and business-operation failures. A replay procedure states which identifier and recorded outcome prevent a repeated delivery from repeating its effect.

Set concurrency and buffering limits from measured downstream processing capacity.
Separate transient retry, permanent rejection and operator-review outcomes.
Expose queue age, failure counts and correlation-based processing diagnostics.
Provide controlled replay that preserves message identity and original failure evidence.

Prove the Gaps Between Every Durable Step

Acceptance deliberately interrupts the path between database commit, publication, consumer processing and acknowledgement. We verify that a restarted relay republishes safely and a redelivered message does not duplicate a payment, reservation or other business action. Load tests include a slow consumer and a temporary dependency outage. The handover contains contract definitions, topology configuration, deployment instructions, operational dashboards and recovery exercises. It equips your team to support the complete distributed workflow, including work that is delayed, rejected or awaiting investigation.

Stop producers and consumers at defined points and verify correct resumption.
Exercise duplicate delivery, expired messages and incompatible schema handling.
Measure backlog recovery under representative traffic and dependency constraints.
Hand over topology, contracts, source, tests and replay operating procedures.

Technologies & Integration

RabbitMQ confirms and acknowledgements — Separate publisher confirmation from consumer acknowledgement and commit the business result before completing delivery.
Apache Kafka — Design topics, partition keys, consumer groups, offsets and retention around replay and ordering requirements.
Azure Service Bus — Configure queues/topics, subscriptions, message locks and the selected dead-letter and duplicate-handling policy.
Amazon SQS — Define visibility, consumer deletion, redrive and standard/FIFO queue behaviour for the actual workflow.
OpenTelemetry .NET — Carry correlation across producer, broker and consumer work and expose useful latency and failure evidence.

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Fast SQL Databases

Sphere 10 tunes SQL Server, PostgreSQL, MySQL and embedded SQLite together with the C# application using them. We investigate transaction-heavy services, ingestion and reporting through actual query plans, waits, locking, I/O and application call counts. EF Core, NHibernate and direct ADO.NET paths are inspected at the generated SQL boundary, with before/after results tied to representative data, concurrency and correct business outcomes.

We examine execution plans, indexes, locks, round trips, object loading and data movement together. A slow screen may need a narrower projection; an import may need better batching; a report may need a different aggregation or storage strategy. Our BlockchainSQL product provides a concrete reference for Bitcoin ingestion and SQL-facing analysis. We bring that end-to-end perspective to your database, then deliver measured changes, regression checks and operational evidence your team can use to maintain the improvement.

💡 Choose a Representative Workload — Name the operations, data volumes and concurrent users that matter. Record both result correctness and response-time distributions before tuning.

Find the Expensive Work Before Changing the Design

SQL Server Query Store and actual execution plans expose changing plans, expensive queries and workload variation; PostgreSQL EXPLAIN/ANALYZE and statement statistics show estimates, buffers and execution work. MySQL plan and Performance Schema evidence is selected for its engine, while SQLite query plans and file concurrency are examined in the embedded workload. We correlate that evidence with parameters, connection use and .NET request traces before prioritising an index, query or application change.

Record representative data distribution, query parameters and concurrency.
Measure latency percentiles, logical reads, CPU and relevant wait conditions.
Inspect execution plans alongside actual row counts and runtime behaviour.
Separate database execution time from application and network round trips.

Optimise Queries, Indexes and the Application Contract

We shape queries around the result a caller needs, including filtering, ordering, pagination and aggregate calculation. Index choices account for selectivity, joins and write cost rather than accumulating speculative indexes. In C# applications, we inspect generated SQL, entity loading and projection boundaries so object mapping does not hide repeated database calls. Sphere10 Framework .Data IDAC and SQL-builder abstractions are examined against the selected provider; batching and command execution remain explicit choices verified on the actual engine.

Project the required columns and avoid loading unused object graphs.
Choose indexes that support measured predicates, joins and ordering patterns.
Replace repeated small calls with appropriate batching or set-based work.
Retest write overhead, storage growth and parameter-sensitive query behaviour.

Engineer Ingestion and Concurrency as Complete Workflows

For imports, SqlBulkCopy on SQL Server or Npgsql COPY on PostgreSQL can provide a deliberate bulk path after parsing and validation. We tune batch and transaction size with log/WAL growth, lock duration, restart checkpoints and duplicate handling in view. Concurrent updates receive an explicit isolation and retry policy. Reporting changes such as filtered or covering indexes, precomputed aggregates or partitioning are evaluated against their write and maintenance cost.

Measure the complete ingest path, including source reading and transformation.
Balance batch size against lock duration, log pressure and restart cost.
Verify concurrent reader and writer behaviour under realistic business operations.
Make derived reports and aggregates reproducible from their retained source data.

Accept the Improvement and Keep It Observable

We rerun the same workload against the changed system and compare both performance and results. Totals, ordering, duplicates and transactional behaviour are checked alongside timing. The delivery records the query and schema changes, configuration assumptions and test data characteristics behind the improvement. We provide repeatable benchmarks, deployment and rollback instructions, and monitoring recommendations that detect regressions. Your team receives evidence of the achieved behaviour under stated conditions and a clear way to evaluate future changes against that baseline.

Compare before and after using the same workload and correctness checks.
Retain plans, runtime measurements and the reason for each schema change.
Rehearse index or migration deployment with its rollback and maintenance implications.
Hand over benchmarks and monitoring that expose later performance regressions.

Technologies & Integration

SQL Server Query Store — Compare plan and runtime history for the measured workload, then verify targeted query or index changes.
PostgreSQL EXPLAIN — Inspect estimates and execution work, selecting ANALYZE and buffer evidence deliberately for the query under test.
MySQL optimisation — Review query plans, indexing and engine behaviour against the application's actual read/write pattern.
EF Core — Check generated commands, loading boundaries, transaction size and concurrency together with the selected database provider.
Npgsql COPY — Build a measured PostgreSQL bulk-import/export path with explicit completion, transaction and restart behaviour.

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Cloud-Based Solutions

Sphere 10 delivers .NET cloud applications on Azure App Service, Azure Container Apps or AWS ECS/Fargate, with virtual machines or Kubernetes selected where their operating model fits. SQL Server/PostgreSQL databases, Azure Blob Storage or S3, and Service Bus or SQS are integrated as owned data and work boundaries. The delivered solution includes identity, configuration, release automation, useful telemetry and a tested recovery procedure for the business service.

Our .NET delivery covers the application and its environment together: hosted services, persistence, configuration, deployment automation and observability. We use cloud capabilities where they simplify a defined responsibility, then make the remaining application responsibilities visible. A managed database still needs a data model, migration plan and tested recovery procedure. You receive a working environment, repeatable releases and an operating handover that explains how to recognise failure, restore service and evolve the workload without depending on undocumented setup.

💡 Define the Workload Before the Service List — Agree the users, traffic, data ownership, recovery objectives and operating responsibilities first. Those requirements determine the cloud components and deployment model.

Select an Operating Model That Fits the Workload

We map each application component to its execution and state requirements. An ASP.NET Core API receives its managed host, scaling and ingress configuration; a durable worker receives the queue, concurrency and restart behaviour needed for its work. Azure SQL or PostgreSQL/RDS stores transactional records, with Blob Storage or S3 for identified objects. Managed identities or IAM roles, private connectivity and secret access are configured for the selected resource path.

Document expected traffic, burst behaviour and the resources each operation consumes.
Separate durable application state from replaceable runtime instances.
Define identity, network access and environment-specific configuration ownership.
Choose availability and recovery mechanisms against agreed business objectives.

Build Application Lifetime and State Deliberately

C# services use the .NET hosting model for dependency injection, configuration and managed startup and shutdown. We design request processing and background jobs with explicit cancellation, concurrency and persistence boundaries. Work that must survive an instance replacement is recorded durably rather than retained only in memory. Database migrations are versioned with the release, and external operations receive clear retry and reconciliation rules. Where Sphere10 Framework is selected, its application and data-access foundations support the implementation inside the host.

Implement graceful shutdown and interruption-safe background processing.
Keep secrets outside source code and ordinary business content.
Coordinate schema changes with compatible application deployment steps.
Use stable work identifiers to make retried operations inspectable and safe.

Make Releases Repeatable and Health Observable

Bicep or Terraform records the selected environment, while the application pipeline produces a versioned .NET package or container image and compatible database migration. Managed-host health signals and OpenTelemetry data distinguish failed requests, saturated workers and unavailable dependencies. Azure Monitor or CloudWatch alerts point to an owned response. We exercise configuration rollout, instance replacement, denied access and restoration of the records needed by a representative business operation.

Produce versioned build artefacts and environment-specific deployment configuration.
Separate liveness, readiness and business-level processing health signals.
Correlate requests and background work across application dependencies.
Define rollout checks, rollback conditions and responsible operational owners.

Exercise Recovery Before Operational Handover

Acceptance includes a realistic deployment, an unavailable dependency, an interrupted worker and restoration from the chosen backup mechanism. We measure recovery against the agreed scope and retain the configuration and data assumptions behind that evidence. The handover provides source, infrastructure definitions, dashboards, access responsibilities and support procedures. Our Local Notion work is a concrete reference for a .NET pipeline with retained local data and generated publications; cloud projects receive their own tested storage and deployment design.

Rehearse data restoration and verify useful application behaviour afterwards.
Test instance replacement and queued-work recovery with representative workloads.
Review cost and capacity evidence under the intended operating profile.
Deliver runbooks, deployment ownership and a prioritised operational maintenance plan.

Technologies & Integration

Azure App Service — Deliver the selected managed .NET web host with application settings, deployment and operating checks.
Azure Container Apps — Configure container applications/jobs and the selected ingress, scale and environment boundary.
Amazon ECS — Deploy versioned tasks and services with the agreed Fargate or EC2 capacity and persistence requirements.
Amazon RDS — Select and operate a managed relational database with explicit connectivity, backup and recovery ownership.
OpenTelemetry .NET — Connect cloud request, dependency and background-work evidence to the business operation and support runbook.

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Microservices Architectures

Sphere 10 builds microservices with ASP.NET Core REST or gRPC APIs and durable messaging through RabbitMQ, Kafka or Azure Service Bus. Each service owns a business capability, its write model and its release contract, using SQL Server, PostgreSQL or another selected store for that boundary. Docker and Kubernetes deployment, optional Dapr building blocks and OpenTelemetry are integrated around the actual consistency, scaling and support requirements of the workflow.

Our implementation covers APIs, asynchronous messaging, persistence, release automation and observability as parts of the same system. C# and ASP.NET Core provide service hosts and business interfaces, while each service retains explicit local transaction boundaries. Cross-service completion is handled through designed workflows, durable state and reconciliation. You receive deployable services and the evidence needed to change them independently, alongside contract documentation, recovery procedures and operational signals that let your team understand the complete business journey.

💡 A Service Needs a Responsible Owner — Define the capability, records, public contract and operating responsibility that make a service complete. Splitting code into processes is only the deployment step.

Find Boundaries With Independent Business Meaning

A service boundary starts with records and invariants that can be owned together. The C# application service commits its own SQL Server or PostgreSQL changes and exposes a versioned HTTP/gRPC operation or business event. Database credentials and deployment permissions reflect that ownership; consumers receive contracts or projections for their work. We test a vertical slice through its API, messages and data migration before separating the next capability from an existing application.

Assign business operations, data and acceptance responsibilities to service owners.
Keep private persistence behind versioned APIs or published business events.
Identify invariants requiring one local transaction before splitting their owner.
Plan extraction and migration around a complete, observable user outcome.

Design Contracts and Distributed Completion Explicitly

An HTTP response, an event publication and a completed business workflow represent different boundaries. We define request identity, timeout behaviour, error contracts and compatibility rules for synchronous calls. Asynchronous work uses durable messages, idempotent processing and appropriate outbox or inbox mechanisms. Where multiple owners must complete an operation, we implement a visible workflow with recorded progress and compensation or reconciliation rules. Each service manages its own local database transaction; the cross-service policy remains application code with explicit tests.

Version interfaces and test compatibility with the actual consuming applications.
Define timeout, retry and duplicate-request behaviour at every remote boundary.
Record workflow progress and the action required after partial completion.
Keep cross-service ownership out of direct shared-table updates.

Operate Independent Releases as One Observable System

Kubernetes Deployments, Services, resource limits, probes and rollout settings are delivered with the container image and compatible configuration. Helm values or the selected infrastructure definitions record each environment. Dapr pub/sub components, where selected, have explicit broker, retry and message-handling ownership. OpenTelemetry connects the services and messages to a business correlation, while contract tests and release checks prove coexistence with the current consumers.

Expose readiness and processing health independently of process liveness.
Propagate correlation identifiers and meaningful business operation context.
Use staged contract evolution and compatible schema migration procedures.
Define capacity, deployment and incident ownership for each service.

Prove Independent Change and Partial Failure

Acceptance includes releasing one service while its consumers remain on the previous compatible contract. We stop a dependency, delay a message, repeat a request and restart a participant during an active workflow. Tests verify that progress remains explainable and that recovery does not duplicate business effects. Delivery includes source, contracts, deployment configuration, integration fixtures and operational dashboards. Your team receives a service map and recovery runbooks that connect local responsibilities to the complete system’s behaviour.

Demonstrate a compatible independent deployment with real consuming clients.
Exercise outages, duplicate messages and delayed completion in representative workflows.
Verify reconciliation and compensation against the intended business outcome.
Hand over dependency maps, contract tests and practical incident procedures.

Technologies & Integration

gRPC on .NET — Define typed service contracts, deadlines and compatibility alongside the selected REST/OpenAPI endpoints.
RabbitMQ — Configure durable message publication/consumption and verify the application's outbox, acknowledgement and idempotency boundaries.
Apache Kafka — Use explicit event schemas, partition keys, consumer groups and replay/offset ownership for the selected workflow.
Kubernetes Deployments — Package independently released services with rollout, readiness, capacity and configuration requirements.
Dapr pub/sub — Integrate the selected messaging abstraction with explicit component configuration and application completion rules.

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MERN Architectures

Sphere 10 builds MERN applications that connect a clear React user experience to well-defined Express APIs, Node.js processing and a MongoDB model shaped around actual work. We deliver customer portals, operational dashboards, marketplaces and workflow applications with attention to the full path from a user action to a durable business result. The architecture makes validation, identity, data access and background processing explicit from the first working slice.

We use the native strengths of the stack while giving integrations and operating responsibilities a precise contract. TypeScript helps express application interfaces; runtime validation checks the data that actually arrives. MongoDB modelling follows access patterns and consistency needs, and Node workloads are designed around their concurrency and resource profile. Where your business also uses .NET services, we build an intentional API or messaging boundary. You receive a coherent MERN system with tested workflows, repeatable releases and practical support and recovery documentation.

💡 Design One Complete User Journey — Choose a real task and carry it through React state, API validation, authorised persistence and its visible completion. Use that slice to establish the architecture.

Shape React State Around the User’s Task

React and TypeScript components separate editable form state from server-owned query results. We define route parameters, request cancellation and cache invalidation around the operation, using a selected query/state library where it fits. Forms handle validation, submission and version conflicts explicitly, with runtime-validated API responses and usable loading or failed states. Keyboard navigation, responsive content and the result of returning from authentication are tested through the same workflow.

Map component responsibilities and ownership of editable versus server-derived state.
Define loading, validation, conflict and recovery behaviour for each important action.
Test keyboard navigation, responsive layouts and meaningful error presentation.
Keep business permissions enforced by the server as well as represented in the UI.

Model MongoDB Around Access and Consistency

MongoDB document boundaries follow the operations that must update together. We select embedding or references, unique and compound indexes, stable pagination and schema validation against realistic queries and document growth. Single-document atomic updates or a deliberately scoped multi-document transaction enforce the chosen invariant. Express middleware supplies authentication, authorization, request validation and error handling; TypeScript types are paired with runtime checks at the incoming JSON boundary.

Define document ownership, lifecycle and the queries each workflow requires.
Choose embedded data or references with explicit growth and consistency assumptions.
Validate requests and version persisted document shapes deliberately.
Measure query plans, index use and update cost with representative records.

Integrate Background Work and Existing Business Systems

Node.js request handlers remain bounded and asynchronous; CPU-heavy work uses a selected worker-thread or separate-worker design, with durable jobs for work that must survive a process restart. The application records the external-operation identifier and result before retrying a payment, notification or import. ASP.NET Core domain services are integrated through versioned OpenAPI or message contracts, and the MongoDB/Node and SQL/.NET sides retain explicit ownership of their records.

Set concurrency, payload and timeout limits for external and background work.
Move expensive processing away from the interactive request path.
Define identity propagation, schema compatibility and duplicate handling across runtimes.
Record correlation and status so operators can trace an individual business operation.

Deliver and Accept the Complete MERN Application

We exercise the workflow from the browser through the API and database, including unauthorised requests, invalid data, dependency failure and restarted jobs. Performance testing uses realistic document sizes and concurrent users. The release package includes source, environment configuration, database indexes or migration procedures, build instructions and operational monitoring. Handover explains how to deploy, rotate configuration, restore data and investigate a failed user action. The resulting application is ready to be maintained as a complete product across its browser, server and data components.

Test end-to-end business outcomes and the important failure states.
Verify data access and responsiveness under representative concurrency.
Rehearse deployment, configuration changes, backup and restoration.
Hand over component contracts, integration tests and practical support runbooks.

Technologies & Integration

MongoDB data modelling — Select document boundaries, indexes, validation and transaction scope around the actual read/write invariants.
Express middleware — Compose authentication, validation, routing and error handling into an explicit HTTP request pipeline.
React state — Separate component drafts, navigation state and authoritative query results with deliberate update and invalidation rules.
Node.js execution — Keep request work responsive and assign expensive or durable processing to the appropriate worker boundary.
TypeScript — Maintain typed application and API contracts while independently validating untrusted runtime input.
ASP.NET Core OpenAPI — Integrate an owned .NET business domain through versioned operations, errors and concurrency contracts.

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Data Lakes & Warehouses

Sphere 10 engineers analytical platforms with Microsoft Fabric/OneLake, Azure Data Factory, Databricks/Delta Lake, Snowflake or AWS Athena according to the data and operating model. We connect SQL Server, PostgreSQL, business APIs and files through owned ingestion and transformation pipelines, with Parquet or Delta storage where appropriate. The delivered lake or warehouse includes source lineage, validated data, business-grain models and Power BI or other reporting whose figures can be reconciled.

Our delivery combines C# ingestion services with the storage and query platform suited to the workload. We define source identities, checkpoints, schema changes and reconciliation before building dashboards. Analytical tables then express the grain, dimensions and measures your team actually needs. BlockchainSQL provides a concrete Bitcoin-focused reference for transforming specialised source records into SQL-facing data. We apply that discipline to your pipeline, delivering reproducible transformations, quality checks and operating procedures that keep the analytical model aligned with its sources.

💡 Define the Grain of the Result — State what one row represents and which source facts establish it. That decision anchors joins, measures, history and reconciliation.

Retain Source Evidence and Establish Ownership

We identify the authoritative source for each dataset and the extraction contract it exposes. Raw landing records retain useful source identifiers, extraction time and schema context so transformations can be rerun and investigated. We distinguish a complete snapshot from an incremental change feed and define how deletions or late arrivals are represented. Access and retention policies follow the sensitivity and purpose of the data. The resulting source catalogue explains what was received, when it was received and who owns its meaning.

Record source identifiers, extraction boundaries and dataset ownership.
Define snapshot, incremental update and deletion handling explicitly.
Retain source lineage and the schema version used to interpret each batch.
Set access, retention and handling rules for sensitive or restricted fields.

Engineer Repeatable Ingestion and Quality Controls

Azure Data Factory or the selected orchestration platform coordinates connector extraction and repeatable batches; C# workers handle the specialised APIs and business transformations that need application code. Each batch records its source watermark, schema generation, accepted/rejected records and completion. Databricks/Spark or the chosen warehouse engine writes the selected Parquet or Delta tables, with business-key merge and rerun rules. Schema changes and late or deleted records are tested against the published model.

Persist checkpoints and source-range identity for each processing run.
Quarantine invalid records with useful diagnostics and a controlled correction path.
Test duplicate input, partial batches and late-arriving updates.
Version transformation rules and schema assumptions alongside pipeline code.

Build an Analytical Model With Agreed Business Meaning

We design the warehouse or lakehouse around fact grain, dimensions, effective dates and agreed measures. Fabric, Snowflake or the selected query platform receives the partitions, access roles and transformation ownership appropriate to its workloads. Power BI relationships and DAX measures are reviewed with business examples, including row-level access where required. Reconciliation checks totals and record counts back to the retained source generation before the dataset is accepted.

Agree the grain, business keys and calculation rules of each published table.
Model historical dimensions and corrections according to reporting requirements.
Test joins for duplicated facts, missing matches and unintended filtering.
Measure query behaviour using representative data volumes and reporting patterns.

Prove the Numbers and Hand Over the Pipeline

Acceptance reconstructs selected results from their source range and compares counts, totals and business examples with agreed expectations. We interrupt ingestion, rerun a partition and apply a schema change to verify the recovery design. The delivery package includes pipeline source, orchestration configuration, dataset definitions, quality rules and operational dashboards. Your team receives procedures for investigating a failed batch, correcting rejected records and rebuilding a derived dataset. The platform is supported by an explainable chain from source evidence to business decision.

Reconcile record counts and material totals across pipeline stages.
Rebuild representative partitions and verify reproducible analytical results.
Exercise failed extraction, schema drift and controlled correction workflows.
Hand over lineage, data definitions, source code and operational runbooks.

Technologies & Integration

Microsoft Fabric lakehouse — Select OneLake/lakehouse tables and ownership boundaries for engineering and analytical consumption.
Azure Data Factory — Orchestrate connector extraction, dependencies and identifiable pipeline runs with explicit rerun and failure handling.
Databricks Delta Lake — Implement table transformations and merge/update behaviour with schema, batch and transaction history understood.
Power BI star-schema modelling — Define fact grain, dimensions, relationships and business measures that reconcile to the source records.
Apache Parquet — Choose a columnar interchange/storage layout with partition, schema and reader compatibility checked for the selected tools.

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On-Premise Solutions

Sphere 10 delivers installed .NET applications on Windows Server/IIS or Windows Services, and Ubuntu/RHEL-family Linux with systemd. SQL Server, PostgreSQL or SQLite is selected for the local transaction, concurrency and recovery requirement. We integrate the site's Active Directory or chosen identity provider, storage, network and operational procedures, including environments with controlled internet access. The system is packaged, commissioned and handed over with owned configuration, durable data and a tested update/restore path.

Our delivery includes the application and the installation your team must maintain: versioned packages, configuration, service registration, durable data, diagnostics and tested recovery. C# services can provide local APIs, scheduled processing and operator interfaces with clear responsibility for credentials and stored records. Local Notion offers an inspectable reference for retaining Notion content on local storage and producing offline HTML or publications. We bring that attention to owned data and repeatable operation to the specific requirements of your installed business system.

💡 Design for the Actual Site — Identify the operating systems, accounts, network paths, storage locations and support access available at the installation before selecting its deployment pattern.

Map the Site’s Dependencies and Responsibilities

We map Windows/Active Directory identities, local service accounts or the selected installed OpenID Connect provider to the application's actual permissions. Database authentication, SMB or local file access, certificates and administrative entry points receive explicit owners. The dependency record distinguishes local DNS/identity/storage from required external feeds or services. An isolated-site acceptance test exercises the functions expected to remain available and the queued work that resumes when the dependency returns.

Document host requirements, supported environments and external dependencies.
Separate operator, administrator and application service-account responsibilities.
Choose storage paths, permissions and capacity thresholds deliberately.
Define behaviour during network loss and recovery of delayed integration work.

Package Application Lifetime and Durable State Together

Windows delivery registers the selected IIS application or .NET Windows Service with the matching runtime, account and NTFS permissions; Linux delivery includes the systemd unit, service user and working directory. SQL Server or PostgreSQL data and uploaded files live outside replaceable application packages. Embedded SQLite receives a deliberately scoped connection/concurrency and backup policy. Versioned migrations and retained release packages establish the update and recovery decision points.

Separate application binaries, configuration, logs and durable business records.
Implement graceful shutdown and restart-safe scheduled or queued work.
Version database changes with a tested installation and upgrade sequence.
Retain diagnostic context that identifies the affected host and business operation.

Make Maintenance and Access Practical for Operators

PowerShell or the selected configuration automation records installed versions, service settings, permissions and operating checks. Operators can inspect Event Viewer or journalctl evidence, validate the active configuration and collect application diagnostics through the agreed access model. Restricted sites receive the required installation prerequisites and an explicit package-transfer procedure. Maintenance separates OS/runtime changes, application releases and data migrations so the support team can verify and recover each generation.

Provide a version inventory and a repeatable configuration health check.
Define update approval, maintenance windows and operational communication responsibilities.
Document log retention, capacity monitoring and secret-management procedures.
Supply support diagnostics and controlled access arrangements suited to the site.

Accept Installation, Upgrade and Restoration

We test the deployment on the intended class of host and exercise the operational journeys alongside the business features. Acceptance includes restarting the service, recovering interrupted work and restoring data into a clean installation. Backup testing covers the configuration and keys required to make restored records usable. Delivery includes source or agreed distribution artefacts, installer instructions, migration scripts and runbooks. Your team receives a system it can install, update and recover using documented steps and retained evidence from the actual acceptance exercises.

Rehearse a clean installation and a representative version upgrade.
Interrupt processing and verify restart and work-resumption behaviour.
Restore a backup and validate useful application operations afterwards.
Hand over package identities, configuration ownership and recovery instructions.

Technologies & Integration

.NET Windows Services — Package managed background work with the service account, runtime, restart and operating checks required by the site.
Linux systemd — Deliver the installed process identity, execution context, supervision and shutdown behaviour.
SQL Server backup and restore — Own the local database recovery model, retained backup chain and complete restore procedure.
SQLite concurrency — Select embedded connection, write/read and checkpoint behaviour with an engine-supported backup method.
Local Notion — Reference an owned .NET product with locally retained source/media and reproducible offline HTML/publication outputs.

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