Topic
9 posts tagged “Data Engineering”.
Chisato · · 5 min read A feature store centralizes how machine learning features are computed, stored, and served — keeping training and production predictions consistent.
The Lycoris Team · · 4 min read A data warehouse stores structured, pre-modeled data optimized for queries; a data lake stores raw data of any shape. When each one fits.
The Lycoris Team · · 4 min read Star schema denormalizes dimensions into flat tables for fast queries; snowflake schema normalizes them to save space. How to choose for your warehouse.
Chisato · · 4 min read A time-series database is optimized for timestamped data — metrics, sensor readings, prices. How it differs from general-purpose databases.
The Lycoris Team · · 5 min read A graph database stores data as nodes and relationships instead of tables, making deeply connected queries fast instead of a chain of costly joins.
Chisato · · 4 min read ETL transforms data before loading it into a warehouse; ELT loads raw data first and transforms it inside the destination. How the two approaches differ.
The Lycoris Team · · 4 min read A data lakehouse combines a data lake's cheap object storage with a data warehouse's transactional guarantees and schema. How the architecture works.
The Lycoris Team · · 4 min read Row-oriented databases store each record together on disk; columnar databases store each column together. The layout decides which workloads are fast.
The Lycoris Team · · 4 min read Change data capture streams row-level inserts, updates, and deletes out of a database in real time, powering sync pipelines, caches, and event-driven systems.
