Architecture Weekly Issue #43. Articles, books, and playlists on architecture and related topics. Split by sections, highlighted with complexity: 🤟 means hardcore, 👷‍♂️ is technically applicable right away,  🍼 - is an introduction to the topic or an overview. Now in telegram as well.

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Video

Highlights of the Week

The industry comes through a tough period: Big Tech is layoff people; those who don't - freeze the hire. According to layoffs.fyi,  the Bay Area got ~70,000 engineers freed of their jobs. However, Adyen keeps hiring to make a day positive.

This week I would like to bring your attention to several articles about data management and the future of Frontend in the highlight section. Another seven awaiting you in the follow-up section.

Making Schema Changes Safe with Raft 🤟

Schema change is adding or deleting a column, changing column types etc. ScyllaDB is a distributed database, and it should apply the changes across the cluster. But this is a hard task and bugs are being reported over and over again. So they applied Raft to solve those problems. From the video, I discovered the prevoting procedure; pretty sure you will find a lot of interesting for you as well!

Making Schema Changes Safe with Raft
ScyllaDB adopted Raft as a consensus protocol in order to dramatically improve our operational aspects as well as provide strong consistency to the end user. This talk will explain how Raft behaves in ScyllaDB Open Source 5.0, and introduces the first end-user visible major improvement: schema chang…

#databse #raft #distributedconsensus

The future history of Data Engineering 🍼

Matt Aderne makes an exercise of predicting the future of Data Engineering. He extrapolates that with the adoption of cloud services and the rise of Data Processing as a Service(Tech abstraction aaS)  the demand for data engineering will drop focusing on tooling and orchestration instead of ETLs. Debatable? Indeed. Interesting nonetheless.

The future history of Data Engineering
On Data Engineers and their place in a Data SaaS world

#dataengineering #forecast

The state of Frontend in 2022 👷‍♂️

Gergely Orosz published the state of Frontend report. Technology-wise: TypeScript is at 80% of adoption. Surprisingly, 20% of developers are not using CI/CD despite the impression that this is a standard nowadays. More insights inside!

The State of Frontend in 2022
Analysis of The State of Frontend survey with 3,700 respondents. What engineering practices and technologies are gaining momentum in the field?

#frontend #statereport

Follow Up

Aligning organization and architecture with strategic DDD 🍼

Find a deck by Michael Plöd on the application of DDD to organizing teams. As to Convey's law, your architecture will resemble the organization, picking the proper team helps a lot. Pick up the DDD approach, identify contexts, make good boundaries and remember, nothing is perfect. The slide deck below.

#ddd #teamtopologies #accelerate

Accelerating the LinkedIn experience with Azure Front Door  👷‍♂️

Another excellent article from the LinkedIn engineering blog. Serving data to far locations affects latency significantly and worsens the user experience. Points of Presence help with it by heavy lifting the HTTPS and TCP/IP connections between the end user and itself, while maintaining the connection to LI servers. Brilliant illustrations and lessons learned inside.  

Accelerating the LinkedIn Experience with Azure Front Door
Co-authors: Samir Jafferali, Viranch Mehta, and Thanglalson Gangte

#azure #edge #cloud

Machine Learning for Fraud Detection in Streaming Services 👷‍♂️

Streaming platforms use limitations for the content from the perspective of the number of devices, accessing particular content etc. The implementation of those limitations can be tricky. Netflix heavily uses machine learning based on anomaly detection to detect fraudsters. More details in their tech blog.

Machine Learning for Fraud Detection in Streaming Services
By Soheil Esmaeilzadeh, Negin Salajegheh, Amir Ziai, Jeff Boote

#ml #fraud

Scaling customer review system for peak traffic 👷‍♂️

Fresh post from the Booking.com tech blog on the system scaling for customer reviews. To prepare for picks in traffic they have the load testing with predicted data, have observability in place and employ a consistent hashing algorithm to distribute the load. Details inside!

Scaling our customer review system for peak traffic
Abstract: Customer reviews is a high-traffic system, which requires scaling to meet the peak traffic. Read on to learn how we scaled it !

#performance #scalability #consistenthashing

Lyfts' SafeMode 🍼

I remember when you roll out a new feature in a mobile application, and it starts crashing right from the first screen. It is unacceptable with any scale of a mobile app, but the complexity of applications is extremely high. In order to safeguard Lyft introduced SafeMode: an approach to detect a crash, disable a feature flag and report the issue. Read how it helped Lyft!

Recovering from Crashes with Safe Mode
Feature flags are everywhere in modern software development: They’re a great tool for running A/B experiments, slowly rolling out changes…

#scalability #resiliency

Is Enterprise Architecture Dead? 🍼

Short note on Enterprise Architecture continuing the topic of Road-way architecture. As we remember the global rearchitecting never completes; instead you need to be flexible and extendable. Navdeep Singh shares the lesson learned.

Is Enterprise Architecture (EA) Dead in Digital?
Any EA group in an organization starting or going through digital transformation will face this question. I’ve been there, have been challenged directly, and have asked myself this question when leading EA through digital.

#enterprisearchitecture #ea

Why B-trees are efficient? 🤟

I am continuing reading the "Database Internals" book. This time it's chapter number 2 where I cover the binary search trees and B-trees. Find out why the latter are used in databases!

#video #database #btree

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