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Written by James Long this blog post focuses on namecheap.com top five tips to help you maximize your productivity when mapping your day working from home. Ask anyone in a 9-5 office job, and the chances are they quietly yearn for that trademark flexibility of the home worker, self-employed, or business owner.

But one of the toughest challenges, particularly when working at home, often alone, is keeping yourself motivated throughout the whole day. Suddenly the 9-5 you had seems comparatively productive — the whole day was carefully mapped out, and you got stuff done. Whether that was true productivity is another thing; when the lunches, coffee breaks and commute (oh yeah — remember the commute) are considered, and those hours where you were just filling time, you realize it was perhaps the illusion of productivity, rather than productivity itself.

Top 5 tips for maximizing productivity:

• Choosing location
• Setting goals
• Creating variation
• Distractions & breaks
• Exercise

Without your commute, it’s easy to become stuck inside. Aside from obvious health implications, you might find that you find it more difficult to sleep. So, it’s important to use up that energy. Great read!

How to learn to unify Google Sheets, Excel, and CSV files – a code-along guide. By Fabian Bosler.

Python is often called a glue language. This is due to the fact that a plethora of interface libraries and features have been developed over time — driven by its widespread usage and an amazing, extensive open-source community. Those libraries and features give straightforward access to different file formats, but also data sources (databases, webpages, and APIs).

This story focuses on the extraction part of the data. Next week’s story will then dive a little deeper into analyzing the combined data to derive meaningful and exciting insights.

What you will learn:

• Extracting data from Google Sheets
• Extracting data from CSV files
• Extracting data from Excel files

As this article is intended as a code-along article, you should have your development environment (recommended: Jupyter Notebook/Lab) set up and start a new Notebook. You can find the source code and files in provided GitHub repository. Great resource!

Haridas N is author of this article about data pipelines and how machine learning (ML) helps businesses manage, analyze, and use data more effectively than ever before.

ML identifies patterns in data through supervised and unsupervised learning, using algorithms to get actionable insights. Recommendations in travel, shopping, and entertainment websites are typical examples, which use consumer data to make personalized offerings.

The article main parts are:

• Data pipeline – an overview
• Data collection and cleansing
• Storage Layer
• Feature extraction for different models
• Model design and Training
• Serve trained Model

Data Pipelines can be broadly classified into two classes:

Batch processing processes scheduled jobs periodically to generate dashboard or other specific insights

Stream processing processes / handles events in real-time as they arrive and immediately detect conditions within a short time, like tracking anomaly or fraud.

A solid data pipeline holds the promise of transforming the dark-data hidden in silos. Having a flexible, efficient and economical pipeline with minimal maintenance and cost footprint allows you to build innovative solutions.

In this tutorial, you will learn how to implement and train autoencoders using Keras, TensorFlow, and Deep Learning. Author discusses what autoencoders are, including how convolutional autoencoders can be applied to image data. Also more about the difference between autoencoders and other generative models, such as Generative Adversarial Networks (GANs). By Adrian Rosebrock.

Typically, we think of an autoencoder having two components/subnetworks:

• Encoder: Accepts the input data and compresses it into the latent-space. If we denote our input data as x and the encoder as E, then the output latent-space representation, s, would be s = E(x).
• Decoder: The decoder is responsible for accepting the latent-space representation s and then reconstructing the original input. If we denote the decoder function as D and the output of the detector as o, then we can represent the decoder as o = D(s).

You can thus think of an autoencoder as a network that reconstructs its input! To train an autoencoder, we input our data, attempt to reconstruct it, and then minimize the mean squared error (or similar loss function). Ideally, the output of the autoencoder will be near identical to the input.

To learn more follow the link to the article. Nice one!

Ovie Okeh is the author of this article on logrocket.com about a journey from monolithic apps running on a server to flexible units running on ephemeral instances. So why is the serverless popular? What does the term serverless mean?

All serverless means is that you can ship code to your users without having to worry about the underlying architecture required to run it. There is still a server running your code somewhere, but you don’t have to worry about setting it up or managing it. Serverless in a nutshell: “Let someone else be your DevOps team.

The article then make discovery on:

• Servers and why you need one
• The gross inefficiency of a traditional server
• Benefits of a serverless architecture
• Trakkr on serverless (case study)
• Trade-offs of going serverless
• Should you switch to a Serverless architecture?

The decision to adopt an architecture should not be based on a single article. However, the aim of this section is to provide you some of the questions you should be asking if you’re considering adopting a serverless architecture. Excellent!

Visual Studio is an Integrated Development Environment (IDE) developed by Microsoft which is used to code,debug and run the respective applications. Visual Studio supports around 36 programming languages (at time of writing) and the advantage of using Visual Studio is that the code editor and debugger will support all these programming languages and also it supports cross-platform. By Suhas Parameshwara.

The article provides some interesting tips and tricks that can be used in Visual Studio:

• Project templates
• Code snippets
• Document health indicator
• Code cleanup
• IntelliCode
• Vertical selection
• Solution filter

… and more. For full list click to the original article. Nicely done!

Aggregator Leaf Tailer (ALT) is the data architecture favored by web-scale companies, like Facebook, LinkedIn, and Google, for its efficiency and scalability. In this blog post, I will describe the Aggregator Leaf Tailer architecture and its advantages for low-latency data processing and analytics. By Dhruba Borthakur.

The biggest advantage of the Lambda architecture is that data processing occurs when new data arrives in the system, but ironically this is its biggest weakness as well. Most processing in the Lambda architecture happens in the pipeline and not at query time. As most of the complex business logic is tied to the pipeline software, the application developer is unable to make quick changes to the application and has limited flexibility in the ways he or she can use the data. Having to maintain a pipeline just slows you down.

The article then goes about:

• Traditional Data Processing: Batch and Streaming
• Common Lambda Architectures: Kafka, Spark, and MongoDB/Elasticsearch
• Shortcomings of Lambda Architectures
• ALT: Real-Time Analytics Without Pipelines
• Advantages of ALT
• Why ALT Makes Sense Today

The ALT architecture enables the app developer or data scientist to run low-latency queries on raw data sets without any prior transformation. A large portion of the data transformation process can occur as part of the query itself. The ALT architecture clearly has the performance, scale, and efficiency to handle real-time use cases at some of the largest online companies. Excellent read!

The Repository pattern is used to create a middle layer between a data layer (which can be a database, a file, a CSV, etc) and your controllers. It is mainly used on large scale application, where there will be dozens of reference to a single data layer. It gives the ability to change the data layer without affecting any code in your controller. By Anastasio Nico.

The repository pattern probably is best known for its widespread use within MVC application. In a normal application the flow works like this: The users reach a view or an endpoint requesting some data, this request need to be processed by a controller that in the majority of the cases will communicate with a source of data. Usually, that would be a database.

Repository pattern may be considered structural because of its similarity with the Facade pattern but at the same time, it is kinda creational because with some easy addition can look like a Factory.

For the example of the code in PHP and further explanation how it can be implemented follow the link to the original article. Nice one!

Fernando Amezcua put together this tutorial how to implement React Hooks creating the example project. React Hooks, TypeScript and Redux for React Native, and author is going to add testing for React Native.

You are going to build a counter app. This exercise consists of two buttons that increment or decrement the state of our app. You are going to implement Hooks to see the benefits of using them, and how to integrate TypeScript and Redux. Then, you are going to test with Jest.

Redux is a package that helps us create only one state into our app. The only way to change the state is through dispatch actions.

The tutorial describes:

• Getting started with TypeScript
• Install environment
• Redux
• How does it work?
• Actions
• Type
• Reducers

… and more. Reducers help us specify how the state will change. They are a response to the actions sent to the store. The actions describe what happened, they do not describe how the state will change. Easy read with plenty of code examples!

There are several best practices for building microservices architecture properly. In this article author mentions the best practices with some new rules dedicated especially for microservices deployed on Kubernetes platform. By Piotr Minkowski.

The first question is if it makes any difference when you deploy your microservices on Kubernetes instead of running them independently without any platform? Well, actually yes and no… Yes, because now you have a platform which is responsible for running and monitoring your applications, and it launches some its own rules. No, because you still have microservices architecture, a group of loosely coupled, independent applications, and you should not forget about it!

What you will find in the article:

• Allow platform to collect metrics
• Prepare logs in right format
• Implement both liveness and readiness health check
• Consider your integrations
• Use service mesh
• Be open for framework specific solutions
• Be prepared for a rollback

… and more. You will also get sample Spring Boot application that implements currently described best practices and is written in Kotlin. It is available on GitHub in repository sample-spring-kotlin-microservice under branch kubernetes.