AWS Glue is Amazon's serverless data integration cloud service that makes it simple and cost effective to extract, clean, enrich, load, and organize data. Originally launched in August 2017, AWS Glue began as an extract-transform-load (ETL) service designed to relieve developers and data engineers of the undifferentiated heavy lifting needed to load databases, data warehouses, and build data lakes on Amazon S3. Since then, it has evolved to serve a larger audience including ETL specialists and data scientists, and includes a broader suite of data integration capabilities. Today, hundreds of thousands of customers use AWS Glue every month. In this paper, we describe the use cases and challenges cloud customers face in preparing data for analytics and the tenets we chose to drive Glue's design. We chose early on to focus on ease-of-use, scale, and extensibility. At its core, Glue offers serverless Apache Spark and Python engines backed by a purpose-built resource manager for fast startup and auto-scaling. In Spark, it offers a new data structure --- DynamicFrames --- for manipulating messy schema-free semi-structured data such as event logs, a variety of transformations and tooling to simplify data preparation, and a new shuffle plugin to offload to cloud storage. It also includes a Hivemetastore compatible Data Catalog with Glue crawlers to build and manage metadata, e.g. for data lakes on Amazon S3. Finally, Glue Studio is its visual interface for authoring Spark and Python-based ETL jobs. We describe the innovations that differentiate AWS Glue and drive its popularity and how it has evolved over the years.

Oracle has developed new implementations for integer division and floating-point division and square root. Our task was to verify the correctness of the new designs by formally proving equivalence between the RTL for these designs and their specifications in the SPARC ISA and in the IEEE 754 Standard on floating-point arithmetic. Performing such verifications involved many steps, which we describe in this paper. The contributions of this paper are two-fold. First, this paper describes Oracle's methodology for abstracting from low-level Verilog to a high-level algorithm using the latest open-source tools. Second, this paper describes the use of interval arithmetic in the error analysis of each algorithm. Our verification efforts proved that the designs had no errors, resulted in various improvements, and reduced the lookup tables by approximately 50% (division) and 75% (square root).