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Sometimes when you’re working with several different dictionaries, you need to group and manage them as a single one. In other situations, you can have multiple dictionaries representing different scopes or contexts and need to handle them as a single dictionary that allows you to access the underlying data following a given order or priority. In those cases, you can take advantage of Python’s ChainMap from the collections module. By Leodanis Pozo Ramos.

In this tutorial, you’ll learn how to:

• Create ChainMap instances in your Python programs
• Explore the differences between ChainMap and dict
• Use ChainMap to work with several dictionaries as one
• Manage key lookup priorities with ChainMap

Python’s ChainMap from the collections module provides an efficient tool for managing several dictionaries as a single one. This class is handy when you have multiple dictionaries representing different scopes or contexts and need to set access priorities to the underlying data. Good read!

Akka Streams is a library to process and transfer a sequence of elements. It is built on top of Akka Actors to make the ingestion and processing of streams easy. As it is build on top of Akka Actors, it provide a higher-level abstraction over Akka’s existing actor model. By Asbin Bhadra.

Akka streams consist of 3 major components in it – Source, Flow, Sink – and any non-cyclical stream consist of at least 2 components Source, Sink and any number of Flow element.

The article deals with:

• Features of Akka Streams
• Terminology in Akka-Streams (Source, Sink, Flow, RunnableGraph)
• Akka Streams in action

You should now better understand the basics of Akka Streams and there is also example code to help you. Informative read!

Akka Serverless exposes part of the open-source battle-tested Akka framework as an as-a-service offering. If you ever wondered what a fully managed version of Akka Cluster+Sharding+Persistence would look like, here’s the answer! If you don’t know what Akka is at all, don’t worry — we’ll cover all the necessary details. By Adam Warski.

Source: https://softwaremill.com/first-look-at-akka-serverless/#case-study-user-registration

The main points in this article:

• Programming model
• Technicalities ()
• State models
• Actions
• Effects
• Views
• Inter-service communication
• Case study: user registration

… and more. The basic unit of deployment in Akka Serverless is a service written in any of the supported languages (there are official SDKs for Java and JavaScript, with more community SDKs available and more official ones coming). Very interesting read!

Refactoring is like the NeverEnding Story: you might think you’re done, but as long as the plot (the project) is ongoing, there is always room for more changes. By Fernando Doglio.

The problem though, comes when we focus on the wrong aspect of our code looking for things to refactor. After all, a good refactor normally means running the risk of breaking a working feature, so you better make damn sure that whatever you’re trying to change, is worth it.

The article covers:

• Magic values
• Abusing primitive values
• Duplicated code
• Callback hell or unending promise chains
• Spread responsibility

Keeping your code clean through refactor is a never-ending task, and that’s why author covers it as a completely separate topic in his latest book, Code Well With Others. Good read!

A rejection from the App Store review team is a frustrating setback for any developer, and definitely something you want to avoid. Before you send your precious app off to Apple, you’ll need to make sure you’ve taken care of all of the crucial things the software giant expects. There are a lot of mistakes developers make when submitting an app to the App Store, and many can be easily avoided or rectified. By Grace Njoroge.

Before you begin you’ll need to create an iOS App Store Package (or .ipa file) for your app. This package contains everything needed for Apple to install the app on a device: not just the app file itself, but various other information and assets like the app name, developer name and ID, bundle identifier, copyright information, certain app icons, and so on.

In this article, we’ll give you a refresher on the entire process of submitting your app, focusing on the various app info, metadata, and assets required along the way:

• App information: name, description, and more
• App icons
• Screenshots and previews
• Make use of keywords and categories
• Update your app regularly
• Common App Store rejection reasons
• Consider automation: App Store Connect API

Hopefully you now have a newfound or refreshed understanding of how the App Store submission process works, and of the common pitfalls that could lead to App Store review rejections! Good read.

It wasn’t the first personal computer. Nor was it the most advanced. But shortly after the IBM ® Personal Computer arrived in 1981, it became the leading platform in the revolution that brought computing out of the glass house and into daily life. By IBM.

But it almost didn’t happen. When the concept first came up at IBM corporate headquarters, a senior executive asked the simple question: “Why would anyone want to take a computer home with them?”

In the late 1970s, when the office closed, you turned off your terminal—if you had one—and went home. If you had work to finish up in the evening, you carried a briefcase filled with papers. A handful of aggressive young companies set out to take computing out of back offices and give it to the people. Commodore, Apple, Tandy, Atari and Digital Research had been putting together the pieces that make up a personal computer: a microprocessor (a central processing unit on a single chip), a BIOS (the system boot code), read-only memory (usually a solid-state ROM for controlling the PC), a floppy disk drive, a motherboard and an operating system.

In those days, an entry-level computer at IBM meant a US$90,000 IBM System/38 minicomputer (forefather of today’s IBM Power Systems™ servers) or the barely luggable 50-pound IBM Portable Computer, selling at US$9000. Typical margins were 20 percent to 60 percent on these machines plus the software and services that went with them. IBM at the time was a US$23 billion enterprise with 337,000 employees.

For the details and to learn moore about history of personal computing please follow the link to the full article. Excellent read!

Securing and protecting enterprise data is at the center of the modern security plan. There are many considerations for organizations that aim to move critical workloads and data stores to the cloud, and understanding how business-critical data will be accessed, stored, and secured is a paramount concern. Written by Chris Steffen.

In an exclusive research study conducted for IBM, Enterprise Management Associates recently polled over 200 technical leaders and practitioners from organizations of 500 employees or more. They represented industries from 10 different verticals, providing an excellent cross-section of organizations, all impacted by data privacy and security in different ways.

Some key takeaways from the study include:

• Data Security: Nearly all of those surveyed (95.1%) indicated that data security is a critical or very important factor in their organizations
• Zero Trust: In this survey, 73% of organizations were investigating or planning to launch a zero trust project
• Security Tools: Of organizations surveyed, 64% report that two or more of their tools are at least somewhat redundant, and 75% currently use three or more tools to secure their hybrid multi-cloud environments
• Security Silos: While 56% of respondents said that they are working to break down barriers

For more follow the link to the full article. Good read!

How do I start using WebAssembly That’s exactly what we’re going to address this time around. The author is going to demonstrate how to get started with this technology. By Jack Wallen.

Let’s get on with the steps toward your first WebAssembly app. The article covers:

• Install the necessary dependencies
• Install Emscripten
• Create your Hello, World! (with source)
• Compile the source code

Emscripten is open source software that compiles projects written in C or C++ – or any language that uses LLVM – to browsers, Node.js, or wasm runtimes. The Emscripten SDK profiles all of the necessary tools (such as Clang, Python, and Node.js), as well as an updated mechanism to enable migrating to newer versions of the toolchain as they are released.

You will also get commands and step by step process how to use the emcc compiler to take your C code and turn it into a WebAssembly (WASM) HTML file. Short but sweet!

The Kubeflow project was announced back in December 2017 and has since become a very popular machine learning platform with both data scientists and MLOps engineers. If you are new to the Kubeflow ecosystem and community, here’s a quick rundown. By Jimmy Guerrero.

In the article you will get the link to video in which author will demonstrate the following:

• Launch a MiniKF AMI from the AWS Marketplace
• Configure the instance for MiniKF
• Deploy all the necessary components including Kale and Rok
• Bring up the Kubeflow UI to start your first project

Kubeflow is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. In a nutshell, Kubeflow is the machine learning toolkit for Kubernetes. As such, anywhere you are running Kubernetes, you should also be able to run Kubeflow. You will also get links to further resources and reading related to Kubeflow. Good read!

Machine learning is still harder than it needs to be. The open-source tool ModelDB and the ML model management platform Verta can help. By Matt Asay.

The artificial intelligence/machine learning software development and deployment lifecycle is still very nascent. The challenge of moving models into production is exacerbated by a demand for speed and a shortage of qualified ML engineers. But there’s hope that things may soon get better.

There’s a need for MLOps: We’re still early enough in ML that it lacks the mature tooling and workflow processes of traditional software development. There, concepts like agile development and continuous integration and continuous deployment let entrenched companies and scrappy startups push new features to market quickly.

Many machine learning solutions are actually assemblies of models. They run several models to get one prediction. Then, after all that, data scientists need to monitor model performance, retrain when needed and redeploy. Interesting read!