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Dan Fabulich, Principal Engineer at Redfin, shares his thoughts on paying technical debt faster. Paradoxically, employees who want to take vacation and/or clean up their code do less of it when they have “unlimited” time.

Shipping first time code is like going into debt. A little debt speeds development so long as it is paid back promptly with a rewrite… Author explains:

Technical debt can’t be measured in mythical man-months. It can only be measured in tears, as engineers weep in frustration over code that they desperately want to refactor.

His advice is to establish a technical debt budget for each engineer, to make sure they actually spend it. Read now to discover more practical advice!

Tips for launching a side project from KJ Jones. As many of us take more lead roles at their workplace, our time writing code will decrease. The main goal was to go through the entire development and deployment process.

Author shares some of the things he’s learned during the process. Some main points mentioned in the article:

• Create a Minimum Viable Product
• Free is a key - do not spend much time thinking about price strategy
• Launching a product takes time

Creators should never see any completed project as a failure. Side projects are one of the best ways to improve your value. The goal is go through the entire development and deployment process. Good read.

Blog post from Sam solutions on topic of migration to cloud. Companies that employ more than 1,000 people assume that their spending on migration to the cloud will go up in 2017.

Author lists useful guide how to prepare for migration. The article is a list of few checklists actually and includes:

• The Basics of the migration to cloud
• What to look in a service provider and how to choose one
• The migration strategy
• Proper preparation and planning for migration
• Migration aspects
• Verification process

Each sections contains detailed tabular information and checklists. To avoid any pitfalls and errors one should not disregard migration security and pay close attention to developing a safe and thorough plan, and following it closely.

Robert Chang piece on how data products have always been an instrumental part of Airbnb’s service. However, engineers have long recognized that it’s costly to make data products.

Robert describes how auto machine learning tools worked together to expedite the modeling process and hence lower the overall development costs for a specific use case of life time value modeling — predicting the value of homes on Airbnb.

He describes:

• What customer life time value is (LTV)
• Machine learning workflow or LTV modeling
• Feature engineering they use (Zipline)
• Prototyping and training in Python
• How they perform model selection

and more. Example of code are included. Great article!

Robert C. Martin (Uncle Bob) post about how move to functional programming began and happened.

He starts with Moore’s law and processor development and how multi-core processors changed the way we design software.

One of software designers responses was to learn Functional Programming (FP). One of our responses was to learn Functional Programming (FP). FP strongly discourages changing the state of a variable once initialized. This has a profound effect upon concurrency.

Authors argues that FP is easier to write, easier to read, easier to test, and easier to understand. Why? Because you don’t have to keep track of the state of the system. The state of variables can’t change; so the state of the system remains unaltered.

Interesting article with bit of code in Clojure.

George Crump wrote introductionary blog post about scale-out storage - which provides data centers the ability to create infrastructures that can scale to meet the capacity demands of hyper-scale environments. He focuses on the challenge involving storage scale in docker environments.

The problem is that most scale-out architectures, whether they are dedicated to storage or share CPU with application and services, are hard set to the amount of CPU power they will use per node.

Project Longhorn

Storage compute challenge in hyperconverged architectures is more obvious in container environment with potentially thousands of new instances. Project Longhorn is a block storage system. It essentially containerizes each block storage volume into a dedicated controller, and runs each controller on the same host the storage is consumed.

Project Longhorn is 100% open source software. It takes data protection very seriously - and it is not just for docker. How? Read more …

Kenneth White excellent blog post exploring his experience with using Chromebook as his main development station after 2 weeks of usage.

Author fully appreciates Google’s Chrome OS design. The architecture benefited from a modern view of threat modeling and real-world attacks. Chromebook is also the daily driver for many of Google’s own senior developers and security engineers.

The point of this exercise was to retain the hardened posture of the platform and have a flexible, safe development environment without depending on the crutch of privileged access.

Author describes in depth:

• What his motivation and goals were
• Chromebook supported gear
• Lists hardware
• Authentication and security
• Android apps used for daily development work

… and much much more. Great!

Harrison Davis has written article in which he collected 9 tips how to become better front end developer.

Whether you are a newbie or professional, chances are that some of these points will help you to bring your front-end development career to the next level.

Some tips he mentions:

• Take automation to the next level
• PLan your project
• Be proactive

… and more. Each tip contains explanation why it is useful, the tooling you may use and links to other resources related to the tip. If you are not going forward, you are going backwards, especially in web development ;)

Recommended reading!

@AshNuq from Lightbend posted about usage of akka streams in mission critical systems by large online payment industry leaders including PayPal, Credit Karma and Flipkart. These are collectively worth well over $50 billion in market capitalization.

Reactive Streams - asynchronous stream processing with non-blocking back pressure - which ensures that fast data sources don’t overwhelm the stream destination.

For enterprises dealing with streaming workloads that may never end, Akka Streams has proven to provide the availability, scalability, and resilience needed for production systems.

Author then looks into how each of above mentioned companies use akka to process terabytes of data transferred from browsers and mobile devices to their and ensures the data flow reliably, without data volumes overwhelming target data stores.

Charts included. Interesting reading!

Nick Chase from Mirantis quick intro into what we can expect from Kubernets 1.7. This release focusing on production features such as security, extensibility, and stateful applications.

Security - the most notable feature is that data that remains “at rest” such as Secrets and other resources stored in etcd, you now have the ability to protect that information from prying eyes (and programs) with encryption. The Network Policy API enables users to create rules that determine which pods can communicate with each other.

There are other interesting features related to:

• Application development
• Stateful workloads (databases, etcd, Zookeeper)
• Extensibility

and the most intresting Api aggregation which enables new functionality to be added by user that can be accessed in a method similar to the core Kubernetes features.