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DataEngineering.

Data engineering consulting from source integration to governed lakehouses. We build batch and streaming pipelines, quality controls and the data foundations your reporting and AI systems depend on.

01 / Paradigm Shift

Target vs Reality

Current

Fragmented Data

Disconnected source systems, inconsistent definitions and fragile pipelines leave reporting and AI teams reconciling data before they can use it.

Target

A Trusted Foundation

A governed platform connects ingestion, Bronze–Silver–Gold processing and serving. Tested contracts, monitored runs and clear ownership make the data usable and the platform operable.

02 / Architecture

Built for scale

  1. 01Ingest

    Every source lands raw and immutable. Nothing is lost, nothing is trusted yet.

  2. 02Refine

    Cleansed, deduplicated, modelled. One schema the whole company can read.

  3. 03Serve

    Dashboards, models and agents draw from the same governed products.

  4. 04Govern

    Metadata drives orchestration. Every run is traceable, every change is versioned.

Modern Data Platform

Control Plane
Data Plane
Control PlaneUnified Data PlaneInternal databases, external APIs, and IoT streams providing raw data ingestion.Source Systems REST / JDBCImmutable landing zone. Stores history in original format with append-only strategy.Raw Layer Parquet / JSONCleansed, deduplicated, and validated data. Enforces enterprise schema and data quality.Curated Layer Delta LakeBusiness-level aggregates and dimensional models optimized for high-performance querying.Semantic Layer Star SchemaDashboards, ML models, and downstream applications consuming trusted data products.Analytics & AI BI / APICentralized repository for pipeline configurations, watermarks, and lineage tracking.Metadata Store Relational DBWorkflow manager that triggers processing jobs based on metadata and events.Orchestration Engine Pipeline Runner
Architecture notes

A platform-neutral medallion reference. Solid arrows follow source data through Bronze, Silver and Gold into Analytics & AI; dashed connections show metadata and orchestration dependencies.

Layers
Bronze retains source history within an agreed retention policy. Silver applies types, deduplication and quality rules; Gold publishes business models and aggregates. These are persisted data layers. A BI semantic model is a separate consumption component.
Execution
Metadata records configuration, load progress and lineage. The orchestrator coordinates jobs; processing engines perform the transformations. Full or incremental loads, source deletions and validation need explicit contracts. Safe retries depend on the source and write strategy, not on scheduling alone.
Access
SQL endpoints, semantic models and application APIs are distinct serving paths. Each needs an authorized identity, access rules and a freshness policy. Gold does not automatically enforce the same permissions in every consumer.
03 / Processing

Streaming core

  1. 01Capture

    Keyed events enter a durable log, retained for replay.

  2. 02Process

    Stateful processing applies event-time and late-event rules.

  3. 03Serve

    Query-ready results feed applications and analytics.

  4. 04Recover

    Contracts guide validation. Checkpoints preserve recovery state.

Streaming Platform

Support
Events
Contracts & recoveryStateful event processingApplications, change-data-capture connectors and sensors publish keyed events with timestamps and schema versions. Stable event IDs support downstream deduplication.Event Producers APPS / CDC / IOTA replicated, partitioned log decouples ingestion from processing. Retained events can be replayed. Ordering is per partition, rather than across the whole stream.Durable Log RETAIN / REPLAYValidates events and maintains keyed state for joins and aggregations. Event-time windows use watermarks to track progress; explicit lateness rules determine how late events are handled.Stream Processor EVENT TIMEHolds query-ready results. Idempotent writes or checkpoint-coordinated transactions handle retries. End-to-end delivery guarantees depend on the source, processor and sink.Serving Store KEYED RESULTSApplications and reporting query the serving store. Freshness depends on processing, sink commits and the consumer’s query or refresh cadence.Apps & Analytics API / BIDefine event structure, meaning and compatibility across versions. Producers and processors apply these contracts when serializing or validating events.Schema Contracts VERSIONED EVENTSPersists consistent snapshots of operator state and source positions. Recovery restores a completed checkpoint, then replays the retained log. Working state belongs to the processor.Checkpoint Store STATE + POSITIONS
Architecture notes

A platform-neutral reference for stateful event processing. Solid arrows follow events and derived results; dashed arrows show schema dependencies and checkpoint recovery.

Replay
Recovery requires a replayable source with sufficient retention. Checkpoints contain consistent state and source positions. They are separate from the processor’s working state.
Event time
Watermarks track event-time progress. The application still needs a policy for events that arrive late.
Delivery
Exactly-once state processing alone does not guarantee exactly-once external effects. Source replay and an idempotent or transactionally coordinated sink determine the end-to-end result.
04 / Capabilities

Core Systems

01 / Storage01 / 04

Lakehouse Architecture

Raw data, validated tables and business-ready models. A medallion architecture that gives every layer a clear purpose.

  • Delta Lake / Iceberg
  • Table maintenance
Three stages of a lakehouseAn illustrative sequence of retained source material, conformed tables and business-ready data represents Bronze, Silver and Gold. The marks describe refinement, not data volumes or quality scores. Conceptual marks, not measured quantities or production records.

Keep raw history. Publish trusted data.

Bronze
Retain source records and ingestion metadata within the agreed retention policy.
Silver
Apply types, deduplication and quality rules before publishing conformed tables.
Gold
Model business measures and reusable datasets for reporting and AI.
02 / Ownership02 / 04

Data Mesh

Domain-owned data products with explicit contracts. Independent teams publish trusted interfaces under shared governance.

  • Data products
  • Federated governance
Domain ownership with shared standardsThree independent data-product signatures retain separate owners. Paired contract marks connect the domains, while a common rule above them represents shared governance without a central data store. Conceptual marks, not measured quantities or production records.

Make ownership explicit.

Domain
An accountable team owns each data product and its operation.
Contract
A published interface defines schema, meaning and freshness expectations.
Standards
Shared governance sets rules while domains retain ownership.
03 / Models03 / 04

Model Lifecycle

An engineered path from reusable features to evaluated model versions and monitored inference. Training and serving each have their own lifecycle.

  • Feature pipelines
  • Model lifecycle
Candidate models and an evaluation gateThree illustrative model candidates reach an evaluation boundary. A selected version continues only after release approval; the other candidate paths stop before the boundary. Conceptual marks, not measured quantities or production records.

Release with evidence.

Train
Version the training data, feature definitions and candidate model.
Evaluate
Check evaluation results against agreed criteria before approving a release.
Serve
Deploy an approved version and monitor quality, latency and failures.
04 / Trust04 / 04

Data Governance

Access policies, lineage and data quality controls across the data estate. The right information, visible to the right people.

  • Policy enforcement
  • PII masking
Permitted and held data pathsA field of access paths reaches a policy boundary. Only the illustrated permitted groups continue; all remaining paths end on the requesting side. The drawing does not imply universal or automatic enforcement. Conceptual marks, not measured quantities or production records.

Define access at the boundary.

Identity
Identify the user or workload requesting access to the data.
Policy
Enforce permitted tables, rows and fields on each consumption path.
Evidence
Record lineage, quality checks and access decisions for review.
05 / Practice

Reference patterns

Finance

Real-Time Fraud Detection

A stream processing pattern that scores transactions as they arrive, raises anomalies before settlement and keeps a replayable history for investigation.

Retail

Hyper-Personalization

A unified customer data pattern that feeds recommendation and marketing systems from one governed profile, refreshed in near real time.

Manufacturing

Predictive Maintenance

A telemetry pattern that lands sensor streams raw, models failure signatures on curated history and serves alerts to the operations floor.

06 / Delivery & platforms

From design to delivery.

Your team receives versioned pipelines and configuration, data contracts, validation checks and operating instructions. Platform selection follows your sources, workloads and ownership requirements.

Delivered project · Microsoft Fabric

Six sources. One foundation.

Five REST applications and one SQL Server feed a configuration-driven Fabric platform. Bronze, Silver and Gold prepare the data for Salesforce Data Cloud and Power BI.

Reference architecture · Databricks

A lakehouse, released through code.

A proposed multi-source design with source contracts, independent quality gates and reviewed deployment through Declarative Automation Bundles and GitHub Actions.

  1. 01Data Engineering
  2. 02AI Engineering
  3. 03Analytics
  4. 04Migration
  5. 05Managed Services
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