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Having the ability to identify clear root cause for incidents in production workloads is valuable, the best way to prevent future incidents is to understand what caused past ones. Of course you can have a good set of load tests but still, incidents in production environments can happen and it’s crucial to have available data that can help to understand what happened so application can be improved and prevent replications. By LuisLopes.

In this article we will learn how to use Application Insights and automatic profiler to find root cause for past incidents and prepare logging platform to ensure meaningful data for analysis. The guide goes over:

• Check configuration
• Isolate relevant data in a concrete scenario
• Finding the latency source
• Other tools
• Checking profiler trace using Windows Performance Analyzer

Having the profiler option enabled in Application Insights is a great way to root cause past events without having to wait for problem to happen again to collect fresh data. Traces can be isolated, verified and changes applied avoiding future incidents. In this article is shown how to isolate relevant data in timeline, locate traces and use them to root cause analysis. Good read!

Learn how to build a secure infrastructure as code workflow with Terraform Cloud dynamic provider credentials, Microsoft Defender for Cloud, and HCP Vault. By David Wright, Arnaud Lheureux.

In this part, we will set up more security utilities to provide a defense-in-depth approach to cloud vulnerabilities. These utilities include Microsoft Defender for Cloud, Dynamic provider credentials in Terraform Cloud, HashiCorp Cloud Platform (HCP) Vault.

Further in the article you will learn about:

• Reference architecture
• Dynamic provider credentials with Terraform Cloud and HCP Vault
• Adding HCP Vault on Azure

Incorporating security tools along with best practices into your infrastructure as code is essential for maintaining secure and reliable infrastructure. By doing so, organizations help ensure that their cloud deployments are secure, compliant, and easily maintainable. Code examples and video tutorials are also linked in the article. Good read!

Encapsulation is a fundamental concept in object-oriented programming (OOP) that allows developers to achieve data abstraction, improve code maintainability, and create more robust software. In this article, we will dive deep into the principles of encapsulation in Java, understand its benefits, and explore the best practices to implement it effectively. By Rahul.

In this article you will learn:

• Encapsulation overview
• Benefits of encapsulation
• Encapsulation in Java
• Example of encapsulation in Java
• Best practices

Mastering encapsulation in Java is crucial for writing clean, maintainable, and robust code. By adhering to the principles of encapsulation and following the best practices outlined in this article, you can create well-structured, modular, and reusable software that is easier to develop, debug, and maintain. Nice one!

Autonomous Database (ADB) Observability blog series where we’ll cover the basics of database observability and then introduce the most relevant Oracle tools and services that you can leverage to observe your Autonomous Databases. By German Viscuso.

What’s needed is more abundant, higher-quality telemetry that can be used to create high-fidelity, context-rich, fully correlated multi-level metrics for every application event, transaction, infrastructure change, etc. Enter Observability.

The article pays attention to:

• Observing the autonomous database
• Observability tools and services for autonomous database within the OCI console
  • Direct access to ADB metrics
  • Events and notifications
  • Performance hub
  • Database actions monitoring pages
• Oracle Cloud observability and management platform

Autonomous Database on Dedicated Exadata Infrastructure and Cloud@Customer gives you the best of both worlds today: the ability to run natively in the cloud (OCI) or on-premises (Cloud@Customer) while providing deep levels of observability (at the database instance, fleet and data plane levels) via a rich set of Oracle tools and services that cover the whole database observability cycle. Good read!

Learn how to build and train your first Image Classification model with Keras and TensorFlow using Convolutional Neural Network. By Aryan Garg.

AI has gone so far now, and various state-of-the AI models are evolving that are used in Chatbots, Humanoid Robots, Self-driving cars, etc. It has become the fastest-growing technology, and Object Detection and Object Classification are trendy these days.

Here is the complete workflow of this tutorial:

• Importing necessary libraries
• Loading of the data
• Preprocessing of the data
• Building the model
• Evaluating the model performance

This article shows the entire process of building and training a Convolutional Neural Network from scratch. We got around 75% accuracy. You can play with the hyperparameters and use different sets of convolutional and pooling layers to improve the accuracy. Nice one!

At Meta, we run one of the largest deployments of MySQL in the world. The deployment powers the social graph along with many other services, like Messaging, Ads, and Feed. Over the last few years, we have implemented MySQL Raft, a Raft consensus engine that was integrated with MySQL to build a replicated state machine. By Anirban Rahut, Abhinav Sharma, Yichen Shen, Ahsanul Haque.

To allow for high availability, fault tolerance, and scaling reads, Meta’s MySQL datastore is a massively sharded, geo-replicated deployment with millions of shards, holding petabytes of data. The deployment includes thousands of machines running over several regions and data centers across multiple continents.

Tah article then explains:

• Why was MySQL Raft necessary?
• The Raft library and the MySQL Raft plugin
• MySQL Raft replication topologies
• Replicated log
• Write transaction on MySQL primary using Raft
• Crash recovery
• Raft-initiated state machine transitions
• Monitoring the MySQL Raft rollout
• Performance

… and more. The biggest win of MySQL Raft was simplification of the operation and making MySQL servers take care of promotions and membership. This gave the provable safety of Raft and reduced significant operational pain. Our goals of having a hands off-management of MySQL consistency, and having tools for the rare cases of availability loss, are mostly met. Good read!

A step-by-step tutorial on building an interactive NFT Gallery using QuickNode’s GraphQL NFT API, icy.tools, JavaScript, Next.js, and Tailwind CSS. By Aayush Gupta.

In this tutorial, you will build an NFT gallery using QuickNode’s GraphQL NFT API, Icy.tools, React, JavaScript, Next.js, Vercel, and TailwindCSS. The React web app will provide a user interface displaying a grid view of all the NFTs held by any Ethereum Name Service (ENS). You will fetch NFT data with icy-nft-tools.

The content of the article:

• What is QuickNode?
  • What is QuickNode’s GraphQL NFT API?
  • Explore QuickNode Marketplace!
• The Functionalities
• The Tech Stack
• The Prerequisites
• Step 1: Creating Next.js project
• Step 2: Installing the icy-nft-hooks library
• Step 3: Getting an NFT API key
• Step 4: Installing the dotenv package
• Step 5: Fetching the NFT
• Step 6: Display the NFT gallery
• Step 7: Deploy NFT gallery
• Full code base

QuickNode is a blockchain platform that provides infrastructure for developers to build and scale decentralized applications (DApps). It offers a suite of tools and services, including a developer API, to make it easier for developers to build, test, and deploy DApps on multiple blockchain networks. Good read!

This post covers step-by-step instructions on how to use MockServer to mock an HTTP API, as well as an overview of limitations one may encounter. As the name suggests, MockServer is a powerful tool that allows developers to mock HTTP APIs, making testing much easier and more efficient. It aims to solve the problem of testing APIs that are still in development or not yet available, allowing developers to continue working without being held up by dependencies. By Kasper Siig. Kasper Siig.

This post will cover step-by-step instructions on how to use MockServer to mock an HTTP API, as well as an overview of any limitations you might encounter. It’s important to note that while MockServer is a great tool, it may not be the best fit for every use case. Further in teh article:

• What is MockServer?
• How to Mock HTTP APIs
• The limitations of MockServer
• Align the Tool with your use case

In conclusion, this post has provided a comprehensive guide on how to use MockServer to mock HTTP APIs, with a focus on an intriguing example of an online game platform’s RESTful API. MockServer can be used to effectively isolate client-side interactions for testing, with minimal dependency on actual server availability. However, while MockServer is a powerful tool, it’s important to consider its limitations in simulating complex scenarios and real-world network conditions. Good read!

For many years, microcontrollers have been a staple in various products, continuously revolutionizing their feature sets, reliability, and performance. Moore’s Law has brought 16- and 32-bit processing to even the smallest and most affordable consumer products. The presence of larger memory and CPU power has allowed the use of real-time operating systems (RTOS) where previously developers had to rely on “bare metal” coding. However, as products have evolved to become connected devices in the context of IoT, it has revealed fundamental shortcomings in the traditional methods of software development for microcontrollers. By Jonathan Williams.

The article then pays attention to:

• Consistent Device Reachability is Key in IoT Projects
• What is a Microvisor?
• Guaranteeing Device Availability for Firmware Updates

A microvisor is an IoT approach using hypervisors for microcontrollers, which enables reliable and secure remote operations, such as failsafe over-the-air firmware updates, on Internet-connected devices. Architecturally, a microvisor makes use of hardware separation within a microcontroller – such as the STM32U585 from ST – to divide it into two sections at boot time, e.g. by leveraging Arm® Trustzone®. Good read!

In this article, we are going write the function as a service (FaaS) — i.e., a serverless function that will interact with data via a database, to produce meaningful results. FaaS can also be very useful in A/B testing when you want to quickly release an independent function without going into actual implementation or release. By Mohit Sharma.

The article then explains following steps:

• Serverless function: Hello World!
• Installing the Azure plugin
• First Azure function
• Deploying and running
• Connecting the serverless function with MongoDB Atlas
• Adding the Azure function IP address in Atlas Network Config

With MongoDB Atlas on Microsoft Azure, developers receive access to the most comprehensive, secure, scalable, and cloud–based developer data platform on the market. Now, with the availability of Atlas on the Azure Marketplace, it’s never been easier for users to start building with Atlas while streamlining procurement and billing processes. Code examples and screenshots are also included. Nice one!