Compromising a Microsoft SQL Server

From The Art of Network Penetration Testing by Royce Davis

Implementing a MapReduce Framework Using Python Threads

From High-Performance Python for Data Analytics by Tiago Rodrigues Antao

In this article we will start to explore Python’s framework for concurrency – the first step in developing parallel applications.

Chaos Engineering (for) People

From Chaos Engineering by Mikolaj Pawlikowski

This article explores how you can apply Chaos engineering principles to make your team better.

Fear of Deployments

From Operations Anti-Patterns, DevOps Solutions by Jeffery Smith

This article covers

Longer release cycles and their impact to the team’s deployment confidence
Automation techniques for deployments
The value of code deployment artifacts
Feature flags for releasing incomplete code

Deployment of Fluentd

From Unified Logging with Fluentd by Phil Wilkins

This article describes how and when to deploy Fluentd.

Running Containers in Kubernetes with Pods and Deployments

From Learn Kubernetes in a Month of Lunches by Elton Stoneman

This article delves into getting started running pods with controllers in Kubernetes.

Achieving Loose Coupling

From Practices of the Python Pro by Dane Hillard

This article covers

•  Recognizing the signs of tightly coupled code

•  Strategies for reducing coupling

Preparing Yourself for a Job in Data Science, Part 1: bootcamp

From Build a Career in Data Science by Emily Robinson and Jacqueline Nolis

Want a job in Data Science?

This article discusses one popular way to get the skills that you’re going to need: attending a bootcamp.

The Layers of a Cloud Data Platform

From Designing Cloud Data Platforms by Danil Zburivsky and Lynda Partner

In this article, we’ll layer some of the critical and more advanced functionality needed for most data platforms today. Without this added layer of sophistication your data platform would work but it wouldn’t scale easily, nor would it meet the growing data velocity challenges. It would also be limited in terms of the types of data consumers (people and systems who consume the data from the platform) it supports, as they’re also growing in both numbers and variety.

Case Study: Breast Cancer Diagnosis

From Ensemble Methods for Machine Learning by Gautam Kunapuli

Our first case study explores a medical decision-making task: breast cancer diagnosis. We will see how to use scikit-learn’s homogeneous parallel ensemble modules in practice. Specifically, we will train and evaluate the performance of three homogeneous parallel algorithms, each characterized by increasing randomness: bagging with decision trees, random forests and ExtraTrees.

© 2021 Manning — Design Credits