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ServiceNow: Now on Now: Enabling Intelligent Operations Using ITOM, AIOps, and the Now Platform

Most enterprises know that their industry is being disrupted by digital trends, but few are prepared for it. IT Operations - which is at the heart of the transformation – is under the most pressure to modernize. With the drive to cloud-first initiatives, IT Ops often tries to build flexible digital experiences on top of outdated technology stacks, breaking traditional processes and increasing the likelihood of service degradation with complex business services.

Stackery: AWS Developer Happiness - Cloudside Consistency AND Local development speed: Can you have both?

Building serverless applications results in the fastest "time to thank you" and iteration path for teams today. However, getting that velocity requires a few changes in the process of building, deploying, managing, and iterating applications from what development teams have done in the past.

5 Critical Shortcomings of Traditional BI Tools

Business Intelligence (BI) tools have taken the business world by storm. According to new research, over 80% of executives believe that tools such as advanced visualization, dashboards, and reporting are critical tools when it comes to parsing data. However, many end users aren’t bringing in those dashboards because they really use them, rather they are hoping to get a sense of security (incorrectly) that they will know everything about their business.

Rails Migration A Complete Guide

A Rails migration is a tool for changing an application’s database schema. Instead of managing SQL scripts, you define database changes in a domain-specific language (DSL). The code is database-independent, so you can easily move your app to a new platform. You can roll migrations back, and manage them alongside your application source code.

Distributed Machine Learning With PySpark

Spark is known as a fast general-purpose cluster-computing framework for processing big data. In this post, we’re going to cover how Spark works under the hood and the things you need to know to be able to effectively perform distributing machine learning using PySpark. The post assumes basic familiarity with Python and the concepts of machine learning like regression, gradient descent, etc.