Performance thinking
Large datasets require better decisions around lazy execution, column selection, filtering, partitioning, and file formats. The goal is to avoid loading more data than needed.
Python · Polars · Cloud · Large datasets
I help businesses analyze large datasets faster, cleaner, and with clearer business impact.
I work with Python, Polars, Apache Arrow, Parquet and cloud-based workflows to help businesses process large datasets, clean messy exports, structure analytical data, and turn raw information into decisions.
What I help with
When spreadsheets, dashboards, or slow scripts are no longer enough.
Large dataset analysis
Analyze large CSV, Parquet, JSON, analytics, SEO, product, customer, operational, or transaction datasets without forcing everything into slow spreadsheet workflows.
Cloud-based data workflows
Work with datasets stored in AWS S3, object storage, partitioned folders, Parquet files, and analytical workflows that need faster reading and easier reuse.
Messy data cleaning
Clean inconsistent columns, duplicated records, broken types, missing values, date issues, category mismatches, and exports that are hard to trust.
Business insight and reporting
Turn technical analysis into useful findings: what is growing, what is underperforming, where data quality is weak, and what should be prioritized.
Why this matters
AI can write code, but businesses still need someone who understands the data problem.
AI can help generate scripts, but large-scale data work is not only about writing code. The real value is knowing how to structure the data, avoid memory problems, choose the right file format, validate the results, and explain what the analysis means for the business.
Cloud and storage awareness
Working with cloud storage is not the same as working with a local spreadsheet. Naming, folder structure, partitioning, file size, and read patterns affect cost and speed.
Data quality judgment
A script can run and still produce misleading results. Good analysis checks types, duplicates, missing values, outliers, and business logic before trusting the output.
Business translation
The final output should not be only a notebook. It should explain what changed, what matters, what is risky, and what the business should do next.
Technical stack
Tools I enjoy using for high-value data analysis work.
The stack depends on the problem, but these are the tools and concepts I naturally connect with this service.
01
Python
Analysis scripts, automation, data cleaning, reusable utilities, APIs, reporting workflows, and custom logic.
02
Polars
Fast DataFrame workflows, lazy queries, large file processing, transformations, aggregations, joins, and performance-focused analysis.
03
Apache Arrow
Columnar memory format, interoperability, efficient data exchange, and analytical workflows across tools.
04
Parquet
Columnar storage for analytical datasets, especially when data needs to be read, filtered, compressed, partitioned, and reused.
05
Cloud storage
Cloud object storage for data lakes, exports, partitioned datasets, and scalable analytical workflows across AWS and similar cloud environments.
06
Data visualization
Clear charts, summaries, dashboards, and stories that make the analysis easier to understand and act on.
How I work
From messy data to useful decisions.
The process is designed to avoid “analysis for the sake of analysis.” The goal is to understand the business question, inspect the data, build the right workflow, and deliver clear findings.
01
Understand the question
What decision does the business need to make? What problem are we trying to solve? What would a useful answer look like?
02
Inspect the data
Review size, schema, missing values, types, duplicates, file formats, time ranges, identifiers, and quality issues.
03
Build the analysis
Use the right workflow for the data size and storage setup: local files, Parquet, partitioned datasets, cloud storage, lazy queries, or reusable scripts.
04
Explain the result
Deliver findings, charts, tables, recommendations, limitations, and next steps in language the business can use.
Technical writing
I have written about Python, Polars, Apache Arrow and AWS before offering this as a service.
These articles show the type of technical problems I enjoy: processing larger datasets, working with cloud storage, using efficient file formats, and creating reusable Python workflows. They are useful proof that this service is connected to real hands-on interest, not just a generic consulting offer.
Over time, I may bring expanded versions of these articles into this website. For now, they are useful external proof of my technical thinking.
Polars, Apache Arrow & AWS S3
How Polars, Arrow and AWS S3 can work together for large-scale analytical workflows.
Utility Python functions for AWS projects
Practical Python utility functions for AWS-based projects and reusable workflows.
Partitioning Polars DataFrames on S3
A technical article about partitioning data on S3 using Polars and Apache Arrow.
Managing Excel Files and SharePoint Automations with Python
A technical article about connecting to SharePoint and building automated internal reporting.
Use cases
Examples of projects where this service fits.
SEO and Search Console exports
Analyze large query, page, country, device, and date exports to find patterns that are hard to see in the interface.
Product or marketplace data
Analyze product performance, categories, routes, destinations, suppliers, availability, pricing, demand, or long-tail patterns.
Campaign and lead analysis
Clean and connect campaign data, landing pages, leads, conversion events, costs, and revenue signals.
Data quality audits
Find duplicates, broken identifiers, missing data, inconsistent categories, schema drift, and reporting risks.
Large CSV to Parquet workflows
Convert slow, heavy exports into cleaner analytical datasets that are easier to read, filter, store, and reuse.
Executive reporting
Translate technical analysis into charts, summaries, decision notes, and business recommendations.
Have a large dataset, messy export, or cloud-based analysis problem?
I can help review the data, understand the business question, design the analysis workflow, and turn the findings into clear recommendations.