| 📅 Today’s Picks |
Stop Loading Full Datasets: Use itertools.islice() for Smart Sampling
Problem:
Data prototyping typically requires loading entire datasets into memory first before sampling.
A 1-million-row dataset consumes 7.6 MB of memory even when you only need 10 rows for initial feature exploration, creating unnecessary resource overhead.
Solution:
Use itertools.islice() to extract slices from iterators without loading full datasets into memory first.
Key benefits:
- Memory-efficient data sampling
- Faster prototyping workflows
- Less computational load on laptops
From pandas Full Reloads to Delta Lake Incremental Updates
Problem:
Processing entire datasets when you only need to add a few new records wastes time and memory.
Pandas lacks incremental append capabilities, requiring full dataset reload for data updates.
Solution:
Delta Lake’s append mode processes only new data without touching existing records.
Key advantages:
- Append new records without full dataset reload
- Memory usage scales with new data size, not total dataset size
- Automatic data protection prevents corruption during updates
- Time travel enables rollback to previous dataset versions
Perfect for production data pipelines that need reliable incremental updates.
|
| ☕️ Weekly Finds |
Semantic Kernel
AI Framework
Model-agnostic SDK that empowers developers to build, orchestrate, and deploy AI agents and multi-agent systems with enterprise-grade reliability
Ray
Distributed Computing
AI compute engine with core distributed runtime and AI Libraries for accelerating ML workloads from laptop to cluster
Apache Airflow
Workflow Orchestration
Platform for developing, scheduling, and monitoring workflows with powerful data pipeline orchestration capabilities
