Tips, Expertise, Articles and Advice from the Pro's for Your Website or Blog to Succeed
Are you planning on getting the vaccine as soon as it becomes available to you, after its been a bit more thoroughly vetted, or not at all?
Personally, I think it was pushed through just a bit too fast for my liking. Im holding off until they see what long term side effects and efficacy are.
First, we need a Kubernetes cluster. You may use an existing one or set up a new one. For this tutorial, we chose to use GKE (Google Kubernetes Engine).
Just follow the quick start to create a cluster. To save your money, the default pool with only one node is sufficient for our testing. For node image type, use the default Container-Optimized OS; for machine type, please select one with at least 8G mem. After creating the cluster, go ahead to configure kubectl to connect to the cluster following this guide.
When you read about software architecture structures and views, you will find different opinions from different authors. And what is worse, they use the same terms for explaining different concepts, which just contributes to the general confusion.
This article just reviews these concepts and tries to clarify them proposing a simplification.
PalFish is a fast-growing online education platform that focuses on English learning. It offers tailored English speaking experience to English as a Second Language (ESL) students. As of October 2020, PalFish has over 40 million users, of which more than 2 million are paid users.
As our business rapidly grew, the surge of data posed a severe challenge to our MongoDB database. MongoDB (2.x and 3.x) does not support transactions and has no predefined schema to directly regulate data. This blocked our business growth. To solve these problems, we migrated from MongoDB to TiDB, an open-source, MySQL-compatible, distributed SQL database that supports Hybrid Transactional/Analytical Processing (HTAP) workloads. This turned out to be the right move.
As companies intensify their push toward adopting DevOps practices and cultural values, there are several practical methodologies cropping up. One such concept is GitOps, which stems from the DevOps need to automate everything and the philosophy of you build it you run it.
The fear of losing out to the competition warrants the need to be agile which then leads to a retrospective of how teams and organizations are getting code from ideation to production. DevOps! The motivation is simple: The faster the release, the more chances of securing the survival of your product in the fast-paced world of technology.
Python and PHP are two extremely popular and high-ranked programming languages. If you may be wondering which language has a greater demand for web development, Python or PHP, Python is the winner. Statistics show that PHP comprises 26.2% of user-share and Python 44.1%. In our article, we will conduct a detailed comparison of these two languages and determine in which cases it is better to use Python or PHP for web development and why Python is more suitable.
Selecting a coding language for any project, be it a website or application development, is a challenging and crucial step. There has always been intense competition between Python and PHP. We will provide a comparison of these two languages for you to make the best choice for your software development. We developed criteria that can be applied to almost any project. Let’s get started!
In previous tutorials, we implemented PrimeNG datatable inplace cell editing and PrimeNG datatable row editing. In this tutorial, we will implement a PrimeNG datatable CRUD example. We will use Spring Boot for the backend to expose REST APIs.
We will be making use of:
Webhook backend is a popular use case for FaaS (Functions-as-a-service) platforms. They could be used for many use cases such as sending customer notifications to responding with funny GIFs! Using a Serverless function, it's quite convenient to encapsulate the webhook functionality and expose it in the form of an HTTP endpoint. In this tutorial, you will learn how to implement a Slack app as a Serverless backend using Azure Functions and Go. You can extend the Slack platform and integrate services by implementing custom apps or workflows that have access to the full scope of the platform allowing you to build powerful experiences in Slack.
This is a simpler version of the Giphy for Slack. The original Giphy Slack app works by responding with multiple GIFs in response to a search request. For the sake of simplicity, the function app demonstrated in this post just returns a single (random) image corresponding to a search keyword using the Giphy Random API. This post provides a step-by-step guide to getting the application deployed to Azure Functions and integrating it with your Slack workspace.
Since its release in 2015 followed by multiple commits on GitHub, Kubernetes (or K8s) has been scoring points across industries and professional communities. But while the reasons behind the success of Kubernetes are crystal clear for software developers, DevOps, and datascience engineers, it is not that obvious for business owners.
Why has transferring a company’s application-related processes to Kubernetes has become a mainstay? To find this out, let’s go over and brush up on some key concepts driving the benefits K8s can bring about.
At its core, the branching model offered by Git is intended to help you avoid merging unstable code into the main codebase. Working with branches in Git is a breeze, especially if you’re working with the GitKraken Git client to visualize your repository.
To delete a local branch in Git using the terminal, you’re going to run the git branch command and pass in the -d flag. Next, you will pass in the name of the branch you wish to delete.
The rapid growth of the Internet of Things over recent years has brought countless examples of smart devices, cloud services, and applications to life. As their complexity increases, the need for IoT platforms ̶ solution frameworks that streamline IoT application development ̶ also grows. In their recent report, MarketsandMarkets estimated that the global IoT cloud platform market will grow from USD 6.4 billion in 2020 to USD 11.5 billion by 2025, at a Compound Annual Growth Rate (CAGR) of 12.6% during the forecast period.
The number of IoT platform vendors shows similar dynamics; as estimated by Statista, there were about 620 publicly known IoT platforms in 2019, twice as many as in 2015. Big companies from different industries, such as Amazon, Bosch, and Deutsche Telekom, have joined the fray, all trying to increase their footprint in technology markets. However, a bigger vendor doesn’t always mean a better solution. When choosing the best IoT platform for your business, it is important to consider the total cost of ownership, usability, speed of implementation, etc. as much as strong feature sets. What is considered to be a great IoT platform, in general, may not be the best option for your case.
The COVID-19 virus has disrupted peoples’ lifestyles and the working patterns of many sectors and industries. The IT industry has seen a thorough transformation within the premises, yet no significant changes in the outputs. Here in this blog, let me consider the discrepancies that working remotely has caused the IT companies, especially the Agile software development teams.
The prime role of Agile is to aid the co-located team function effectively. However, the pandemic has obliged everyone to work from home, i.e., working remotely, and the on-floor teams got converted into distributed teams.
Deep Reinforcement Learning is a somewhat new field within Machine Learning or Artificial Intelligence (you may pick your favorite term between these two, even if they’re not strictly the same), which combines Deep Learning and Reinforcement Learning and is based on the general idea that an agent can learn by observing its actions and their consequences. No, it is not a return to John B. Watson and B. F. Skinner’s behavioral psychology. We are talking, instead, about a set of pretty advanced machine learning algorithms that, when properly mastered, allow computers to achieve remarkable results in some complex tasks. That’s what this book is about, so let’s dive in…
Foundations of Deep Reinforcement Learning - Theory and Practice in Python begins with a brief preliminary chapter, which serves to introduce a few concepts and terms that will be used throughout all the other chapters: agent, state, action, objective, reward, reinforcement, policy, value function, model, trajectory, transition.
Google’s Rich Results Test came out of beta in July with support for all Google Search rich result features like carousels, images, or other non-textual elements. At that time, Google announced it would be deprecating the Structured Data Testing Tool, which SEOs, developers, and site owners have relied on for testing structured markup and troubleshooting schema issues.
The SEO community reacted strongly to this change, because the Rich Results Test only validates structured data that is supported by Google. It also does not provide much useful feedback on errors.
“This is awful,” SEO consultant Barry Adams said in response to the change. “The SDTT is a tool that validates ALL schemas and helps make the web a semantically richer place. The RR test only supports a tiny narrow subset of Google-approved schemas. You’re downgrading the web with this move.”
When Google announced the Structured Data Testing Tool’s deprecation on Twitter, comments from various web professions indicated widespread disappointment in the decision.
“The new tool is painfully slow,” digital marketing consultant Ian Lurie said. “The old tool showed a structured data result for the URL tested above. It provided useful feedback and supported industry-wide standardization. The truth is, you’re replacing a great structured data tool for an inferior Google-specific one.”
After taking note of all the negative feedback, Google announced last week that it will no longer be deprecating the tool. Instead, the company plans to migrate it to a new domain:
To better support open standards and development experience, we’re refocusing the Structured Data Testing Tool and migrating it to a new domain serving the schema.org community by April 2021. The main purpose of the tool will be to check syntax and compliance of markup with schema.org standards.
Google is making some changes to the tool to differentiate it from the Rich Results Test. The Structured Data Testing tool will not be able to check for Google Search rich result types. It will be refocused to validate only schema.org properties, and is recommended for validating types that Google Search doesn’t yet consume.
Serverless architecture is becoming a compelling choice for developers and companies to host their applications. It is easy to see why with its ability to dynamically scale to meet load requirements as well as removing a lot of the complexity with deploying and maintaining applications, sometimes even removing the need for an Ops team. But what are the security considerations we should consider before choosing to go serverless?
Serverless architecture (also known as serverless computing or function as a service, FaaS) is a software architecture where applications are hosted by a third-party service. This essentially means that your application is broken into individual services, which negates the need for server software and hardware management by the developers.
The ways and levels of perception have always differed from person to person. Although, in our time, most of the information is absorbed visually. This is the main reason why, for a better understanding of the user's content, you need to choose the right type of data visualization. Here, I want to talk about one of the ways of displaying information in tables; heatmaps.
A heatmap is a graphical representation of data where values of quantitative variables are expressed as colors, mainly by hue or intensity, giving obvious visual cues about how the phenomenon is clustered or varies over space.
Azure Cosmos DB Cassandra API is a fully managed cloud service that is compatible with Cassandra Query Language (CQL) v3.11 API. It has no operational overhead, and you can benefit from all the underlying Azure Cosmos DB capabilities such as global distribution, automatic scale-out partitioning, availability and latency guarantees, encryption at rest, backups, etc.
At the time of writing this blog, the Azure Cosmos DB Cassandra API serverless is available in preview mode!
Finding (micro)service boundaries is a challenge and become relatively easy as we learn more about the business domain. So, we wanted to try a modular monolith (modules within the microservice) approach.
As the module matures and we see it becoming the first-class microservice, we can take it out of the modular monolith very easily and with fewer efforts as the module is developed as a first-class concept with loose coupling with other modules.