Data System Design Interview Glossary
The core concepts and tradeoffs used in data-platform architecture interviews.
Systems in Practice · Library
Articles, notes, and guides on the systems behind data and AI.
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The core concepts and tradeoffs used in data-platform architecture interviews.
A study guide to ingestion, enrichment, quality, governance, and operations at scale.
A reference to requirements, architecture, and operational concepts for agentic systems.
A structured reference to the stages and tradeoffs in the Nemotron pretraining data pipeline.
Examining the data preparation behind the Nemotron 3 Super pretraining corpus.
The connected systems that acquire, validate, enrich, serve, and monitor production ML data.
Building a product-quality feature store, from raw reviews to serving and drift monitoring.
How batch and streaming paths combine into consistent views for machine learning.
Designing the ingestion, serving, and monitoring systems behind production feature stores.
Explaining a multimodal system through its constraints, design choices, and failure modes.
Turning immutable dataset manifests into WebDataset shards and efficient training loaders.
Precomputation, provenance, and observability turn a search demo into dependable data infrastructure.
Using evaluation feedback to decide whether a new dataset version deserves promotion.
The next design questions for a multimodal lakehouse: quality, deduplication, and evaluation.
Ray actors, GPU execution, and the catalog boundary in a distributed multimodal pipeline.
Moving from single-machine ETL to distributed pipelines without losing correctness or control.
Content-addressed storage, dataset versioning, and deduplication in a multimodal lakehouse.
Why CPU-bound Python threads stall, and when processes are the better tool.
A 12-stage pipeline for turning text, images, video, and audio into traceable training data.
Orchestrating a batch analytics pipeline with Airflow, AWS EMR, and Redshift.
Finding unusual electricity consumption patterns with time-series analysis and machine learning.
Building and serving a computer-vision model for recognizing amenities in property photos.
Comparing forecasting approaches for retail product demand across stores and states.
Examining seasonality and time-series patterns in retail sales before building forecasts.
Exploring the M5 retail dataset through product demand, prices, stores, and sales patterns.
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