Architecture Design and Consulting

Expert architecture design and consulting for a scalable data platform.

1. Data Sources

The systems and channels that generate raw data for the platform.

Enterprise Apps (ERP · CRM · HRIS) — Core business systems of record such as SAP, Salesforce, Workday. Source of transactional and master data (finance, sales, customers, employees).
Databases (OLTP · legacy systems) — Operational relational and NoSQL databases powering day-to-day applications; often the largest source of structured data.
APIs & SaaS — Third-party and cloud application data accessed via REST/GraphQL APIs (marketing platforms, payment processors, support tools).
Streaming & IoT — Real-time event data from message queues, application logs, sensors, and connected devices.
Files & Documents — Unstructured and semi-structured content: PDFs, spreadsheets, images, emails, scanned documents.
External Data — Third-party market data, partner feeds, syndicated datasets, and public data sources used to enrich internal data.

Moves data from source systems into the platform reliably and at the required latency.

Batch Ingestion (ELT/ETL) — Scheduled bulk loads (hourly/daily) for systems where near-real-time isn't required.
CDC / Log-based Replication — Change Data Capture continuously streams inserts/updates/deletes from source databases with minimal load on production systems.
Streaming Ingestion — Continuous ingestion of event streams (e.g., Kafka/Event Hubs-style patterns) for real-time use cases.
API / File Connectors — Pre-built or custom connectors that pull data from SaaS APIs and drop files (CSV, JSON, Parquet) into the platform.

A single storage foundation that holds all data types at any scale, organized into zones by processing stage.

Raw / Landing Zone — Data lands here exactly as received, immutable and unmodified, for auditability and reprocessing.
Curated / Cleansed Zone — Data is validated, deduplicated, and conformed to standard schemas.
Curated / Gold Zone — Business-ready, aggregated, and modeled data optimized for reporting and consumption.
Object Storage (Open Table Formats) — Underlying cloud storage using open formats (e.g., Delta/Iceberg/Parquet-style) enabling both lake and warehouse-style access without duplicating data.

Where raw data is cleaned, joined, aggregated, and shaped into usable datasets.

Distributed Compute Engine — Scalable compute (Spark-style or equivalent) that processes large volumes of data in parallel.
Data Pipelines / Transform Jobs — Version-controlled, testable transformation logic (dbt-style modeling) that turns raw data into curated datasets.
Stream Processing Engine — Processes streaming data in-flight for real-time aggregation, filtering, and enrichment.
Data Quality Checks — Automated validation rules (schema checks, null/range checks, anomaly detection) applied during transformation.

Packages curated data for consistent, governed access by downstream tools and people.

Cloud Data Warehouse — High-performance, structured storage optimized for SQL analytics and BI workloads.
Semantic / Metrics Layer — Centralized business definitions (revenue, churn, active users, etc.) so metrics are calculated consistently everywhere they're used.
Data APIs & Sharing — Governed programmatic access and cross-organization data sharing without copying data.
Feature Store — Centralized repository of curated, reusable features for machine learning models, ensuring consistency between training and inference.

Where data becomes predictive and generative intelligence.

Model Training & Experimentation — Environment for building, tuning, and evaluating machine learning models.
MLOps / Model Registry & Deployment — Version control, testing, and automated deployment pipeline for models moving into production.
Generative AI / LLM Orchestration Layer — Manages prompts, model routing, and orchestration for large language model applications.
Vector Database (Embeddings / RAG) — Stores vector embeddings to support semantic search and Retrieval-Augmented Generation for grounding LLM outputs in enterprise data.
Model Monitoring — Tracks model accuracy, drift, and performance in production, triggering retraining when needed.

How business users, applications, and AI systems actually use the platform's output.

BI Dashboards & Reporting — Self-service analytics and executive reporting tools (Power BI-style, Tableau-style, etc.).
Embedded / Operational Analytics — Analytics built directly into line-of-business applications for in-context decision-making.
AI Agents & Copilots — Conversational interfaces and autonomous agents that query and act on platform data on behalf of users.
Data Science Notebooks — Interactive environments for exploratory analysis and advanced statistical/ML work.

These run continuously across every layer above — they are not a one-time step but ongoing capabilities.

Data Catalog / Metadata Management — Searchable inventory of all data assets with definitions and ownership.
Lineage & Traceability — Tracks how data moves and transforms from source to consumption, critical for debugging and audit.
Access Control (RBAC/ABAC) — Role- and attribute-based permissions enforced consistently across the platform.
Data Quality & Validation — Ongoing monitoring of accuracy, completeness, and consistency, not just at ingestion.
Privacy & Compliance (GDPR/PII) — Classification and handling of sensitive data to meet regulatory requirements.
Master Data Management — Single, trusted source of truth for core business entities (customers, products, vendors).
Encryption & Key Management — Data protection at rest and in transit.
Audit Logging — Records who accessed or changed what, and when.

Job Scheduling — Triggers pipelines on time- or event-based schedules.
Pipeline Dependency Management — Ensures downstream jobs run only after upstream dependencies succeed.
Monitoring & Alerting — Detects failures and performance issues across the pipeline estate.
CI/CD for Data & Models — Automated testing and deployment for pipeline code and ML models, applying software engineering discipline to data workflows.