Intel revealed its Aurora genAI project, a generative AI model with up to one trillion parameters that will run on the Aurora supercomputer—leveraging NVIDIA Megatron and Microsoft DeepSpeed frameworks, delivering over 2 Exaflops performance and targeting scientific as well as broader AI applications.
The article introduces AWS S3 SELECT for in‑object SQL filtering, highlights Aurora's new Multi‑Master and Serverless capabilities, describes DynamoDB's on‑demand backup and Global Tables, and provides a brief overview of the managed graph database Neptune, emphasizing performance and cost benefits.
This article provides an in‑depth technical analysis of Amazon Aurora’s cloud‑native relational database design, covering its architectural models, log‑as‑database philosophy, optimized write and read paths, fault‑tolerant replication, group commit, and fast recovery mechanisms compared with traditional DBMS.
Amazon Aurora separates compute and storage using a service‑centric design, employs a quorum‑based replication across three availability zones, slices data into 10 GB protection groups, and leverages S3‑backed redo logs to deliver MySQL‑compatible performance with rapid recovery and strong consistency.
