Glossary

LeSS/ LeSS Huge | Glossary

Definition:

LeSS is a lightweight, Agile framework for scaling Scrum to more than one team. It was born from the experiences of Agile leaders Bas Vodde and Craig Larman, who saw an opportunity to shorten time to market and improve product quality by scaling Scrum beyond the individual team level. LeSS Huge builds on the LeSS framework by optimizing for eight or more teams. As a result, LeSS Huge introduces several new concepts and challenges for managing large-scale backlogs. These are requirement areas, area product backlogs, and area product owners.

LeSS Huge applies to products with “8+” teams. Avoid applying LeSS Huge for smaller product groups as it will result in more overhead and local optimizations. All LeSS rules apply to LeSS Huge, unless otherwise stated. Each Requirement Area acts like the basic LeSS framework.

LeSS HUge framework is ideal for:

Hierarchy view — Ideal for a product owner to see high-level business goals.

Priority view — Ideal for team members to manage their individual backlog items.

Further Reading:

Large Scale Scrum by Bas Vdde and Craig Larman

Agile Data Science 2.0: Building Full-Stack Data Analytics Applications with Spark | Book Series

Overview:

This book attempts to introduce a new methodology for analytics product development the book accomplishes it’s stated goal. Although somewhat lengthy, the flow of information within this book stays focused on the critical path to the end product while covering documentation, facilitation, exploration, and discovery. A reappearing theme of aligning data science with the rest of the organization is present throughout.

With the revised second edition of this hands-on guide, up-and-coming data scientists will learn how to use the Agile Data Science development methodology to build data applications with Python, Apache Spark, Kafka and other tools.

Author Russell Jurney demonstrates how to compose a data platform for building, deploying and refining analytics applications with Apache Kafka, MongoDB, ElasticSearch, d3.js, scikit-learn and Apache Airflow. Youíll learn an iterative approach that lets you quickly change the kind of analysis youíre doing, depending on what the data is telling you. Publish data science work as a web application and affect meaningful change in your organization.

Authors:

Russell Jurney

Published In:

2017