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Moving Beyond Pandas Memory Bottlenecks
Traditional Pandas scripts copy entire data frames in memory during transformations, requiring 5x–10x more RAM than the raw dataset size and crashing with Out-Of-Memory (OOM) errors on large CSV/Parquet files. Polars written in Rust leverages Apache Arrow’\”s columnar in-memory format for zero-copy data interchange and multi-threaded lazy execution, processing multi-gigabyte datasets 20x faster with a fraction of the RAM.
Documented by Saizul Amin for data engineers and high-performance computing specialists.
Benchmark: 50M Rows CSV Aggregation
| Engine | Execution Time | Peak RAM Consumption |
|---|---|---|
| Pandas 2.2 | 54.2 seconds | 8.4 GB RAM |
| Polars (LazyFrame) | 2.8 seconds (19x Faster) | 920 MB RAM (9x Less Memory) |
Md Saizul Amin
Digital Systems Strategist · AI Agent Developer · 9+ years in digital & ITMd Saizul Amin is a digital systems strategist and AI automation engineer from Dhaka, Bangladesh. With 9+ years across IT, web development and digital marketing, he writes practical guides on technology, business, education and online growth — and builds the same kinds of systems (AI agents, websites, SEO and ad campaigns) for clients in Bangladesh, the UK and the USA.
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Credentials & experience
- 🎓B.Sc. in Computer Science & EngineeringBangladesh University of Business & Technology (BUBT), 2017–2021
- 🤖AI Automation Engineer · Web Developer · SEO SpecialistUnique Mark Limited, Birmingham UK — 2024 to present
- 🌱IT SpecialistGreen Fund Initiative Inc., USA — 2024 to present
- 🏥Junior IT Consultant — Ministry of Health (DGHS)Built the first national Shareable Health Record website; supported 6,000+ facilities
- 🧭Head of ITWEDO Bangladesh — led IT for 50+ staff, 2021–2023