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  • Let's make Cloud #33: Microfrontends at IKEA, Analyzing Volatile Memory on a GKE Node, Real-time databases

Let's make Cloud #33: Microfrontends at IKEA, Analyzing Volatile Memory on a GKE Node, Real-time databases

Microfrontends at IKEA, Analyzing Volatile Memory on a GKE Node, Real-time databases

Hello CloudMakers!

Warm greetings! There's much to look forward to in this upcoming issue. So, fasten your seatbelts! Today we shall see:

  • Microfrontends at IKEA

  • Analyzing Volatile Memory on a GKE Node

  • Real-time databases


History of IKEA.com: Static files and Microfrontends

There's something about transformation stories and case studies that I find utterly captivating. Today, we're digging into the transformation journey of Ingka Digital, the team that powers IKEA's e-commerce operations.

In a significant move, they bid adieu to their 20-year-old monolithic system, the IKEA Retail Web (IRW), replacing it with a forward-thinking Microfrontends architecture. This shift, highlighting both their adaptability and a commitment to innovation, is what makes this story truly intriguing.

Analyzing Volatile Memory on a Google Kubernetes Engine Node

Spotify has pioneered a new method for memory analysis on the Google Kubernetes Engine (GKE). This approach, developed to swiftly analyze and address suspicious behavior in their containerized workloads, marries the power of three open-source tools: AVML, dwarf2json, and Volatility 3.

The result? A method that provides a detailed snapshot of all processes and memory activities on a GKE node, offering a viable alternative or complement to commercial monitoring solutions. In the upcoming article, they delve into this novel technique, its practical applications, and its potential to shape future practices in workload analysis.

Real-Time Databases: Who Is Using Them, and Why?

In the fast-paced, data-driven world of today, traditional databases like Postgres, MySQL, and MongoDB sometimes fall short in handling the needs of real-time applications. As the field of data science advances, the demand for databases capable of supporting user-facing features such as in-product analytics, anomaly detection, inventory management, and more, has grown.

This shift has given rise to a new class of databases - real-time databases. These databases are tailored to support event-driven architectures, handle high concurrency and low latency connections, and provide the necessary speed and scale to manage complex queries over large volumes of data. This article delves deeper into the world of real-time databases, their unique capabilities, and their increasing importance in the modern tech landscape.

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