Data Architecture & APIs

Build the data foundation your business will grow on.

A strong data architecture is the foundation of every analytics capability. We design and build scalable data warehouses, migration pipelines, APIs, and data collection systems that give you clean, reliable, accessible data.

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What you receive

Data engineering for cross-system reporting: connect source systems through APIs and pipelines, organize a warehouse or reporting model, and define refresh and ownership rules. We work with BigQuery, Snowflake and Redshift where they fit the project. The focus is a dependable analytics foundation, not the transactional database behind an application.

A source-system map and agreed reporting requirements
Warehouse, pipeline and API integration decisions
Reconciliation, refresh dependencies and operating ownership

We scope the systems and business questions before choosing infrastructure. Existing exports, API limits, identifiers and data quality determine whether the first step is a focused integration or a broader warehouse build.

Orlando and Central Florida clients can meet our team locally to review source systems and reporting goals. We also deliver data engineering remotely across North America. Meet our Orlando team.

For the operational database behind a web app, including schema and query tuning, see database design and optimization.

For a higher-education business school, we designed a centralized data warehouse to connect institutional systems for enrollment, program engagement, event participation and marketing reporting. The documented scope mapped five systems and defined 22 KPIs. Request case details.

What's included

Data warehouse design and implementation (BigQuery, Snowflake, Redshift)

Data migration and ETL pipeline engineering

API design, development, and integration

MCP (Model Context Protocol) implementations

Web scraping and data mining systems

Server-side tracking and data collection

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Book a free consultation to discuss your needs. No commitment required.

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How we deliver data architecture & apis

Scope, measurement, implementation and handover, agreed around your business needs.

Map sources and reporting decisions

Review source systems, available APIs, identifiers and the business questions the reporting must answer. Agree on data access and ownership.

Design the reporting architecture

Choose a warehouse or reporting model suited to the scope. Define how records join, which transformations are needed and how often data should refresh.

Build pipelines and API integrations

Connect the agreed sources and implement transformations. Account for API limits, failed loads and the source systems that remain authoritative.

Reconcile records and metrics

Compare loaded data and report calculations with source records. Investigate missing identifiers, duplicates and differences before accepting the output.

Document refresh and maintenance

Hand over refresh schedules, failure handling and maintenance responsibilities. Agree on monitoring and future integrations separately from the initial delivery.

Frequently asked questions

What is a data warehouse and do I need one?

A data warehouse combines records from multiple systems for reporting and analysis. It may help when shared metrics, recurring joins or reporting workloads outgrow existing tools. We review the reporting needs, source access and maintenance cost before recommending one; the number of systems alone does not determine the answer.

What is the difference between BigQuery and Snowflake?

Both are cloud data warehouses. BigQuery is Google's offering with serverless pricing and tight Google ecosystem integration. Snowflake offers more flexibility and works across AWS, Azure, and GCP. We help you choose the right fit.

What is web scraping and is it legal?

Web scraping is the automated collection of publicly available data from websites. When done responsibly and in compliance with terms of service and robots.txt, it is a powerful competitive intelligence tool.

What are MCPs?

Model Context Protocols (MCPs) are standardized interfaces that allow AI models to connect to and interact with external data sources and tools. We build MCP implementations that connect your AI systems to your data.