Cloud object stores such as Amazon S3 are some of the largest and most cost-effective storage systems on the planet, making them an attractive target to store large data warehouses and data lakes. Unfortunately, their implementation as key-value stores makes it difficult to achieve ACID transactions and high performance: metadata operations such as listing objects are expensive, and consistency guarantees are limited. In this paper, we present Delta Lake, an open source ACID table storage layer over cloud object stores initially developed at Databricks. Delta Lake uses a transaction log that is compacted into Apache Parquet format to provide ACID properties, time travel, and significantly faster metadata operations for large tabular datasets (e.g., the ability to quickly search billions of table partitions for those relevant to a query). It also leverages this design to provide high-level features such as automatic data layout optimization, upserts, caching, and audit logs. Delta Lake tables can be accessed from Apache Spark, Hive, Presto, Redshift and other systems. Delta Lake is deployed at thousands of Databricks customers that process exabytes of data per day, with the largest instances managing exabyte-scale datasets and billions of objects.

We introduce "temporal lensing": a technique that concentrates a relatively low-bandwidth flood into a short, high-bandwidth pulse. By leveraging existing DNS infrastructure, we experimentally explore lensing and the properties of the pulses it creates. We also empirically show how attackers can use lensing alone to achieve peak bandwidths more than an order of magnitude greater than their upload bandwidth. While formidable by itself in a pulsing DoS attack, attackers can also combine lensing with amplification to potentially produce pulses with peak bandwidths orders of magnitude larger than their own.