Data Engineering I — MySQL
Over 6 years, Braden Keith grew a minimal no-code Domo solution into a proprietary Python SDK microservices platform to support Global Ordnance's multi-channel growth and process millions of records per day with high efficiency and stability.
Overview
Name
Braden Keith
Title
Data Engineer
Employer
Global Ordnance LLC (GO)
Sector
eCommerce
Industry
Commercial Defense
Employment
2019 – 2025
Via
Upwork · Contract-to-Hire
Contract
"Domo help"
"Absolutely fantastic, knowledgeable and incredible guy."
About
Braden Keith joined Global Ordnance as a freelance contractor in 2018, shortly after his tenure at Sandia National Laboratories, where his work as a Business Analyst had propelled his early career. In 2019, he secured a contract with Global Ordnance under the project codename "Domo help", tasked with automating operations to manage the Extract, Transform, Load (ETL) of Inventory and Pricing on GO's BigCommerce-hosted eCommerce site. Using Domo's no-code Graphical User Interface (GUI), Braden engineered initial solutions that streamlined daily inventory and pricing updates without writing a single line of code.
From July 2019 to May 2025, as GO's Data Engineer, Braden Keith progressively modernized the company's data pipeline infrastructure. Early in this role, he leveraged Domo's drag-and-drop ETL workflows for quick automation wins, rapidly integrating multiple vendor data sources and eliminating manual data handling in the eCommerce platform. As GO's business expanded and data requirements grew more complex, Braden transitioned these processes to a robust Python-based microservices architecture. He developed a proprietary Software Development Kit (SDK) to enable seamless integration between systems and deployed a network of lightweight microservices to handle data extraction, transformation, and loading at scale. This evolution from a no-code tool to a custom-coded platform not only increased flexibility but also improved performance and reliability of the pipeline.
Objective
Optimize Inventory and Pricing automation using Domo.
Key Performance Measures
ETL Processing Time (EPT)
Equals the sum of processing time consumed by ETL operations.
Transform Processing Time (TPT)
Equals the sum of processing time consumed by Transform operations.
Connection Processing Time (XPT)
Equals the sum of processing time consumed by Extract and Load operations.
Key Performance Indicators
EPT reduced
by migrating to MySQL DataFlows.
TPT minimized
by mastering MySQL.
XPT optimized
by mastering Domo.
Key Challenges
No-Code Ceiling
Magic ETL DataFlows offered flexibility for non-technical users but imposed a translation overhead that capped performance — Braden had to learn MySQL to break through it.
Schema Design
Defining a relational MySQL schema that could model Domo's flat, denormalized data structures required careful normalization and iterative refinement.
Sequencing Complexity
Transform operations had interdependencies — sequencing and consolidating them correctly without introducing race conditions or stale data was non-trivial.
Domo Mastery
Achieving XPT optimization required deep mastery of Domo's event-driven architecture, virtual table orchestration, and connector scheduling.
Iterative Validation
Each optimization cycle required careful A/B comparison of EPT Δ between old and new DataFlows to confirm genuine gains before replacing production flows.
Performance Engineering
EPT — ETL Processing Time
✅ ReducedTo reduce EPT, Braden Keith recognized the classification of Domo DataFlows by processing engine, theorized potential differences in EPT between MySQL and Magic ETL DataFlows, hypothesized a lower EPT for MySQL DataFlows, specialized in the dialect of MySQL Structured Query Language (SQL), utilized MySQL DataFlows to mirror existing no-code DataFlows, analyzed the EPT Δ between MySQL DataFlows and Magic ETL DataFlows, realized reductions in EPT by replacing Magic ETL DataFlows with MySQL DataFlows, and minimized overall EPT. Magic ETL DataFlows require a translation service – to convert GUI instructions into database instructions – which dictates the performance. This feature offers non-technical users a gambit of capabilities in exchange for some translation overhead but incurs performance limitations. Braden Keith quickly outperformed Magic ETL DataFlows using MySQL programming.
TPT — Transform Processing Time
⭐ MinimizedTo minimize TPT, Braden Keith recognized EPT is bound by TPT, and strategized systematic reductions in TPT by sequencing transform operations, consolidating transform operations, defining a MySQL schema, organizing data using a relational model, and utilized an Iterative Improvement Process (IIP) to achieve Mastery of MySQL for the Optimization of TPT.
XPT — Connection Processing Time
🚀 OptimizedTo optimize XPT, Braden Keith recognized EPT is otherwise bound by XPT, and strategized systematic reductions in XPT by loading data to virtual tables (views), orchestrating virtual operations, minimizing EEC, engineering a MySQL-based, Domo-powered, Event-driven Architecture (EDA) solution, and utilized an IIP to achieve Mastery of Domo for the Optimization of XPT — and ultimately realized superior achievements such as a 5-minute refresh rate, 99% uptime, a Software as a Service (SaaS) solution processing millions of records per day.
Tech Stack
Platform
Domo BI
Language
MySQL (SQL)
DataFlow Types
MySQL DataFlows, Magic ETL DataFlows
Architecture
Event-Driven Architecture (EDA)
Data Sources
Vendor APIs, BigCommerce, internal feeds
Optimization
Iterative Improvement Process (IIP)
Output
SaaS pipeline — 5-min refresh, 99% uptime
Skills Demonstrated
Honors
Initiative
by adopting MySQL.
Development
by mastering MySQL and Domo.
Performance
by optimizing Inventory, Pricing, and EDP in Domo.
Preceded by
Metrics Analysis — VBA
Braden Keith + T-Mobile = Performance Optimization
To be continued in
Data Engineering II — Python
Braden Keith + Python = System Integration