Data is the lifeblood of any organization. In today's world, organizations recognize the vital
role of data in modern business intelligence systems for making meaningful decisions and …

Recently, there has been a pressing need to manage high-dimensional vector data in data
science and AI applications. This trend is fueled by the proliferation of unstructured data and …

Big data management for information centralization (ie making data of interest findable) and
integration (ie making related data connectable) in health research is a defining challenge in …

Serverless computing is becoming increasingly popular, enabling users to quickly launch
thousands of shortlived tasks in the cloud with high elasticity and fine-grain billing. These …

In 2013, AmazonWeb Services revolutionized the data warehousing industry by launching
Amazon Redshift, the first fully-managed, petabyte-scale, enterprise-grade cloud data …

Many organizations are shifting to a data management paradigm called the" Lakehouse,"
which implements the functionality of structured data warehouses on top of unstructured …

Since the late 1970s, relational database technology has been adopted by most
organizations to store their essential data. However, nowadays, the needs of these …

We present operational experience running Snowflake, a cloud-based data warehousing
system with SQL support similar to state-of-the-art databases. Snowflake design is motivated …

Datacenter-scale AI training clusters consisting of thousands of domain-specific accelerators
(DSA) are used to train increasingly-complex deep learning models. These clusters rely on a …

Key-value stores (KVSs) have found wide application in modern software systems. For
persistence, their data resides in slow secondary storage, which requires KVSs to employ …