According to Precedence Research, the global digital health market will reach $1,011.07 billion in 2028. Experts anticipate IoT medical devices market will hit a $203.13 billion value in the same year. Almost exponential growth is mainly due to medical innovation and improving data processing in the healthcare sector.  
The healthcare industry is humongous and complex, divided into many sectors, dealing with massive amounts of data that require strict organization and thorough analysis. With as much as human lives at stake, providers struggle to solve data-related issues and open new healthcare provision capabilities. 

The widely adopted SCM tools we use today, GitHub and Gitlab, are built on the dated architecture and design of git. I recently happened upon this thread on HackerNews that talked about a lot of the pains we’ve spoken about in our article On Git and Cognitive Load. These aren’t the only issues with Git; many of them are just really simple missing features that are no longer aligned to our current engineering practices, take this post on Folder (Non)Tracking, and today I want to talk about common workarounds (AKA hacks) for secure storage with today’s SCMs.
When we build applications, from mobile to desktop, across industries, we often have many configurations in order to connect our applications to external services. Some of these services are required at runtime (e.g., ad networks, configuration servers, analytics), and others at build time (e.g., code signing). We’ll dive into some of the challenges third-party services and imports introduce into our development processes.

This article describes steps to automate AWS Aurora Global Database services using Cloud Formation, Lambda, and State Function. It also provides detailed steps to create a Global Database with sample code snippets.  Some of the features detailed in the article are: 

Overview of Aurora Global Database
Creating an RDS Global Database 

Amazon Aurora Global Database is designed for globally distributed cloud applications in AWS. It provides high availability and database resiliency by way of its ability to fail over to another AWS region. It allows a database to span multiple regions (AWS limits regions to a maximum of six), and it consists of one primary and up to five secondary regions in a global database cluster. Primary region can perform read and write operations, whereas the secondary region can perform read operations only. The way AWS facilitates this feature is by activating writer endpoints in the primary region and deactivating writer endpoints in secondary regions. Furthermore, Aurora replicates data from primary region to secondary regions, usually under a second.

This tutorial walks through the process of configuring your server or HTTP client to enable hot reloading of the SSL configuration at runtime. This will result in no longer restarting your server when the certificates need to be updated, and you won’t need to recreate your HTTP client when you want to use your new certificates. In this tutorial, we will cover only a Spring Boot application with Jetty as an embedded server to demonstrate the basic configuration and the different ways to trigger an update. However, every server or HTTP client which uses a SSLContext, SSLServerSocketFactory/SSLSocketFactory, TrustManager or KeyManager to configure SSL can also enable hot reloading, including Scala and Kotlin-based servers and HTTP clients. 
The hot reloading mechanism is provided by the SSLContext Kickstart library and all of the code examples shown in this tutorial can also be found on GitHub: Java Tutorials.

I’m guilty of applying the word “debugging” to practically anything. My kids’ Legos won’t fit, let’s debug that. Observability is one of the few disciplines that actually warrant that moniker: it is debugging. But traditional debugging doesn’t really fit with observability practices. I usually call it “precognitive debugging.” We need to have a rough idea in advance of what our debugging process will look like for effective observability troubleshooting.
Note that this doesn’t apply to developer observability, which is a special case. That’s a more dynamic process that more closely resembles a typical debugging session. This is about more traditional monitoring and observability: where we need to first instrument the system and add logs, metrics, etc. to cover the information we would need as we will later investigate the issue.

With modern GPS-aware devices, location data is widely collected and used for applications like traffic monitoring, route planning, friend-finding, advertisement deals, emergency services, etc. Location Based Service (LBS) is a service that is offered to users based on their location information. It is a convergence of technologies such as smartphones, the internet, and GIS that makes LBS possible. It is accessed by users from their mobile device, through the mobile internet available on the device while sharing their geographical location (and other required information) with the service provider. 
The quality of service provided by the LBS depends on the type of service, underlying architecture, and the accuracy of the data collected. Highly precise data is collected for location-based services, which is either a single location update or continuous location updates depending on the kind of service. Analysis of such highly precise spatiotemporal data, when collected over an interval of time, can lead to the identification of individuals, estimation of trajectories (identification of popular source/destination points), and eventually their behavior. A lot of information about a person’s beliefs, preferences, religion, and health can be inferred by analyzing the location data.

People’s experiences with the www (World Wide Web) aren’t equal. Web accessibility has truly become highly relevant than before. For specially-abled or those with physical disabilities, accessing sites isn’t always trouble-free. As per the current report to World Bank, 15% of the worldwide population lives with a disability, of which two to four percent face problems in functioning. In fact, World Health Organization’s current studies also mentioned that 1 billion folks live with a few forms of disability. In simple words, web accessibility is not given the major concern for everybody that it should be.
Hence, business or technology enablers (e.g., service providers, app developers, product enterprises, etc.) need to concentrate on accessibility tests more than ever before. That is where web accessibility comes in. Web accessibility means making sure that folks of all capacity levels and several techniques of access can interact, comprehend, and enjoy using a site. With an accessible site, no one feels left out. Of course, there are several other reasons why organizations need to concentrate on accessibility while designing their site. 

When an application needs server-side implementation, clients (such as mobile devices or browsers) must authenticate to the server. For instance, when someone uses Gmail to log into Facebook after having logged in before, they transmit certain identifying information to the Facebook server.
User authentication for your application might be established in several different ways. However, the most suitable option, in our opinion, is to use AWS Cognito.

Kubernetes requires extensive configuration, and keeping container security at the right level is always challenging. One of the best ways to tighten your clusters’ security is by implementing tactics that have become industry standard. Here are the 10 most important ones.

10 Best Practices for Kubernetes Security
1. Don’t Keep Secrets in an Environment Variable
It’s a good practice to have your objects use a secret in an environment variable since other parts of your system can access environment variables. That’s why it’s best to use secrets as files or take advantage of a secretKeyRef to minimize potential attacks.

Service mesh is the next logical step to overcoming security and networking challenges obstructing Kubernetes deployment and container adoption. Check out the benefits of deploying a service mesh here.
With the increased adoption of Microservices, new complexities have emerged for enterprises due to a sheer rise in the number of services. Problems that had to be solved only once for a monolith, such as resiliency, security, compliance, load balancing, monitoring, and observability, now need to be handled for each service in a Microservices architecture.

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