This article discusses LinkedIn's approach to improving HDFS block placement for its massive data clusters. It explains how they adapted their block placement policy to streamline maintenance operations, reduce data replication, and maintain high data availability. The changes were necessary due to the challenges of managing over 5 exabytes of data efficiently.

Grab has evolved its machine learning feature store by transitioning from a traditional model to a more sophisticated feature table design, utilizing Amazon Aurora Postgres for efficient data management and retrieval. This new architecture addresses complexities in high-cardinality data and improves atomicity, ensuring consistency and reliability in ML model serving. The feature tables enhance user experience and streamline the model lifecycle, resulting in better performance of ML models.