We help small teams and energy-focused firms redesign their data stack using Python, SQL, DuckDB/DuckLake, Google Cloud Storage, and existing relational databases — without buying heavy platforms.
Lean data engineering · US energy market experience
A focused engagement where we review your current pipelines and databases, design a simpler architecture, and optionally implement the key pieces using a lean stack: DuckDB/DuckLake, Google Cloud Storage, and your existing relational databases.
A clear architecture, concrete changes, and an implementation plan for a cheaper, easier-to-maintain data stack.
We start with a short call, review your current stack, then deliver a written audit and a proposal to implement the most impactful improvements.
Energy firms and SMBs with growing data, rising infra costs, or slow reports who want a Python/SQL-first solution.
Python, SQL, DuckDB/DuckLake, Google Cloud Storage, and your preferred relational databases.
We inventory your pipelines, databases, and reports to understand where time and money are being wasted.
We design a Python/SQL-first architecture using DuckDB/DuckLake, Google Cloud Storage, and your existing relational databases.
We configure orchestration with Prefect for scheduling, monitoring, and recovery of your critical data flows.
We tune large tables, optimize queries, and build views/materialized views tailored for BI tools like Tableau and similar.
We prepare clean SQL surfaces for fast extracts or live reports, so your analysts don’t fight the data every day.
You receive a concise document: current issues, recommended changes, suggested stack, and estimated impact on cost and performance.
A simplified example based on real work for a boutique electricity market trading firm.
The client was ingesting and analyzing electricity market data using a mix of ad-hoc scripts and manual steps. We implemented Python-based data pipelines, deployed Prefect for orchestration, and introduced DuckLake with Google Cloud Storage to offload less-used historical data. Large tables in their relational database were partitioned, and we created views and materialized views tuned for Tableau extracts and live reports. This made daily market data updates reliable, reduced database load, and improved report responsiveness without introducing heavy, expensive platforms.
Share a few details about your current stack and main pain points. We’ll respond with whether this audit is a good fit and suggest next steps.