Bøker av Kevin Schmidt

Worker safety should always be the number one priority of every company. Toolbox talks should be conducted on a regular basis to educate workers on safe work practices and stay compliant with regulations regarding safety and training.Safety toolbox talks are important to building a strong safety culture and reinforcing your company's commitment to protecting your workers. Holding toolbox talks can prevent workers from getting complacent and avoid taking safety for granted.Conduct toolbox talks on a monthly basis to reinforce your company's focus on safety. Toolbox talks, sometimes referred to as tailgate meetings or safety briefings, are short, informal safety meetings held at the start of a day or shift.Toolbox talks are a great way to reinforce safety basics, focus on high-risk scenarios and to inform workers about changes to the job and/or working conditions that may have occurred. Be sure to discuss any accidents or injuries that have occurred and how they could have been prevented. This series is aimed at lawncare employees, and allows for a tailgate safety chat every week.Take the time today to talk to your employees about safety!

Although you dont need a large computing infrastructure to process massive amounts of data with Apache Hadoop, it can still be difficult to get started. This practical guide shows you how to quickly launch data analysis projects in the cloud by using Amazon Elastic MapReduce (EMR), the hosted Hadoop framework in Amazon Web Services (AWS).Authors Kevin Schmidt and Christopher Phillips demonstrate best practices for using EMR and various AWS and Apache technologies by walking you through the construction of a sample MapReduce log analysis application. Using code samples and example configurations, youll learn how to assemble the building blocks necessary to solve your biggest data analysis problems.Get an overview of the AWS and Apache software tools used in large-scale data analysisGo through the process of executing a Job Flow with a simple log analyzerDiscover useful MapReduce patterns for filtering and analyzing data setsUse Apache Hive and Pig instead of Java to build a MapReduce Job FlowLearn the basics for using Amazon EMR to run machine learning algorithmsDevelop a project cost model for using Amazon EMR and other AWS tools