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This article explains the Architecture of Prometheus. Prometheus is an open source monitoring and alerting toolkit for services and applications that run in containers. Developed first at SoundCloud, the project became part of the Cloud Native Computing Foundation (CNCF). Prometheus is now the industry standard for both containerized infrastructure and classic implementation scenarios, especially within Kubernetes clusters. By Ju.

You will further learn in this blog post:

• Key architectural elements
• Prometheus server
• Applications
• Pushgateway
• Exporter
• Service discovery
• Alertmanager
• Grafana

Prometheus server is the central architectural element. It is well written in Golang and is battle tested. Interesting read!

For healthcare providers and payers, improving the experience of members and patients requires replacing disconnected legacy systems with agile infrastructure and applications. By Dan Schneider.

Digital transformation of systems and platforms is changing the way hospitals and clinics are serving patients and the way insurers are acquiring and servicing members. Cloud and 5G is the powerful business platform for digital transformation and critical to adopting new technologies from cybersecurity to AI/ML to edge computing and IoT.

The article also mentions:

• Healthcare transformations
• 5G: Critical to digital transformation projects
• AIOps and 5G

Automation is critical to managing 5G at speed and scale. Automation has failed in some operations environments for one reason – lack of trust. Operations staff are often unwilling to trust automation for root cause and remediation. Interesting read!

In my recent interview with Software Daily, I discussed that there are many companies looking for better customer experience, faster execution, and lower infrastructure costs… and that they have discovered a better use of Java to help them with just that. By John Ceccarelli.

The interview describes that when you use hyper-optimized Java runtimes instead of vanilla OpenJDK – you do less tuning and debugging. This means you can build applications, data architectures or anything that needs better performance using an optimized JVM – with little or no code changes.

…there are other JVMs, like Azul Platform Prime and GraalVM, that actually do run your code differently and get better performance, carrying capacity, etc. Azul Platform Prime achieves this by taking OpenJDK, replacing key components like the JIT Compiler and the Garbage Collector, and replacing them with more optimized versions.

Many companies, when they want to look at how fast their app runs, will just do a pedal-to-the-metal throughput test that totally saturates their environment, post the top number achieved, and call it a day. But nobody runs like that in production. Instead, people say “I have an app that has a 100ms response SLA. I need to know how many instances of that app I need to provision to serve up my peak traffic of 2M requests per hour.”

So, when you’re benchmarking, you want to not look at max throughput, but max throughput you can achieve while still keeping your SLA. And you’ll find that yes, Prime can deliver higher throughput at that SLA. Follow the link to full interview to learn more!

We’ve all heard about Haskell success stories from famous companies like Meta and Tesla. But did you know that Haskell is successfully used in plenty of enterprises, many of which you wouldn’t think of as being at the forefront of technology? By Gints Dreimanis.

We are living through a slow period of change towards functional declarative programming and away from procedural imperative approaches. This led me to research where some of these ideas came from originally, and subsequently to discovering category theory, which I see as a beautiful and highly useful framework for thinking about software and logic and relating this to the physical world around us

Our guest is Rob Harrison, a Lead Architect at Flowmo.co. He has worked as a technical lead on projects for clients like Vodafone and Tesco. . In the interview, we’ll be talking about his experience and techniques that he uses to bring the power of functional programming to consulting projects.

What do you think is the single biggest problem in the software development industry that functional programming solves?

If I were to pick one, I’d have to say parallelisation/concurrency. It’s a recent problem really, because for much of the history of software, there was only one CPU or core to execute anything on. This fact, along with the increase in available RAM seems to be the driver towards the functional declarative style in the industry as a whole. Mental models of computing where the programmer thinks their computer is still doing one instruction at a time are really no longer valid.

Follow the link to this very interesting interview to learn more!

Putting machine learning (ML) models in production is considered an operational challenge that is performed after all the hard work on training and optimizing the model is completed. In contrast, serverless ML starts with a minimal model, including the operational feature pipeline(s) and inference pipeline. By Jim Dowling.

In this article, we show you that writing feature pipelines and inference pipelines should not be hard and if you don’t have to configure/build the MLOps infrastructure yourself, getting to a minimal viable production model within a couple of weeks should be feasible for most models. You will learn:

• The MVP for Machine Learning
• 3 Programs: Feature, Training, and Inference Pipelines
• When >90% build complete ML Systems

There is no such thing as a single machine learning pipeline - there are feature pipelines, training pipelines, and inference pipelines. And if you structure your ML systems as such, you too will be able to quickly build an end-to-end working ML system that can be iteratively improved. Good read!

As cyber threats continue to evolve, it’s essential for businesses to have systems in place to protect against attacks. One important tool in the cybersecurity toolkit is an intrusion detection network (IDN). By Priyanshu Sahay.

An IDN is a system that monitors a network for signs of unauthorized access or malicious activity. It does this by analyzing network traffic and looking for patterns or anomalies that could indicate an intrusion. If an IDN detects a potential threat, it can alert the system administrator or take automated action to prevent or mitigate the attack.

In this blog you will learn:

• So, what is an IDN, and how does it work?
• Types of Intrusion Detection Network (IDN)
• How does a network intrusion detection system work?
• How Intrusion Detection Network (IDN) helps from Cyber Threats?

It’s important to note that IDNs are just one part of a comprehensive cybersecurity strategy. Businesses should also consider implementing other measures, such as antivirus software, firewalls, and employee training, to help protect against cyber threats. Nice one!

Robotics and AI are two fields that have created buzz worldwide. Artificial Intelligence (AI) refers to developing computer programs or machines that can perform tasks that normally require human intelligence, such as learning, problem-solving, and decision-making. By moonpreneur.com.

Robotics‌ refers to the design, construction, and operation of robots. A robot is a machine that can be programmed to carry out a series of actions automatically without human intervention. Robots can be used in various settings, from manufacturing and assembly lines to space exploration and search and rescue missions.

Source: https://moonpreneur.com/blog/ai-and-robotics/

How are Robotics and AI connected:

• Robotics generate data for AI
• AI improves robotics capabilities
• AI and robotics create new opportunities
• AI is used for controlling robots
• Robotics facilitates AI research
• The combination of robotics and AI is transforming various industries

Finally, the convergence of robotics and artificial intelligence is reshaping many sectors of the economy and the entire nature of technological progress. Robots and other forms of artificial intelligence are becoming smarter and more common in industries ranging from manufacturing to healthcare to agriculture and transportation. Good read!

Many of your microservices need secrets to operate securely. Examples of secrets include the private key for an SSL/TLS certificate, an API key to authenticate to another service, or an SSH key for remote login. Proper secrets management requires strictly limiting the contexts where secrets are used to only the places they need to be and preventing secrets from being accessed except when needed. By Robert Haynes of F5.

In this tutorial, we show how to safely distribute and use a JSON Web Token (JWT) which a client container uses to access a service. In the four challenges in this tutorial, you experiment with four different methods for managing secrets, to learn not only how to manage secrets correctly in your containers but also about methods that are inadequate:

• Hardcode secrets in your app
• Pass secrets as environment variables
• Use local secrets
• Use a secrets manager

Although this tutorial uses a JWT as a sample secret, the techniques apply to anything for containers that you need to keep secret, such as database credentials, SSL private keys, and other API keys. There is also configuration and code included together with youtube tutorial. Nice one!

Functional programming has steadily gained traction in the software development world, thanks to its ability to create more maintainable, efficient, and robust code. By Arthur Frank.

In this blog post, author will delve into the core concepts of functional programming, discuss its advantages, and explore popular functional programming languages:

• What is functional programming?
• Key concepts of functional programming
  • Immutability
  • Pure functions
  • First-class functions
  • Higher-order functions
  • Recursion
• Advantages of functional programming
• Popular functional programming languages

Functional programming offers numerous benefits in terms of maintainability, testability, and modularity. This is nice and comprehensive introduction to the topic with pseudo code examples included. Nice one!

Data manipulation and analysis can be challenging and involve working with large datasets. Thankfully, a widely used Python library known as Pandas has become the go-to tool for processing and manipulating data. Pandas recently got an update, which is version 2.0. This article takes a closer look at what Pandas is, its success, and what the new version brings, including its ecosystem around Arrow, Polars, and DuckDB. By Simon Späti.

That makes it an excellent time to reflect on what Pandas is and why it’s successful. Further in the article:

• What is Pandas
• How does Pandas work?
• What are the highlights of version 2.0
• What changes code-wise?
• What is Apache Arrow?
• Why Apache Arrow?
• Interoperability
• When not to use Pandas
• The alternatives
• Polars: Riding the fast train of rust
• DuckDB: The SQL version
• What about Dask?
• Others: Koalas, Vaex, VertiPaq

Apache Arrow sets the open standard to exchange in a heterogeneous data pipeline, which needs to read and share data among different steps. Overall, this article provides insights into the benefits of using Pandas, particularly with its 2.0 version, and the exciting changes in its ecosystem around Arrow, Polars, and DuckDB. Excellent read!