Community members are invited to honor the life of Knoxville Police Department recruit Wisbens Antoine, who collapsed during officer training session.

Knoxville Police Department recruit Wisbens Antoine funeral details and procession route

Sign up for Newsletters and Alerts · to submit an obituary

Live updates: Speakers eulogize Burnsville’s three fallen first responders, ‘the nicest people’

Thousands across Minnesota on Wednesday will say goodbye to the three Burnsville first responders who made the ultimate sacrifice. Here are the details you need to know.

Public memorial for Burnsville first responders: Details on shuttle service, road closures and procession route - CBS Minnesota

A public procession and funeral will be held for Police Officers Paul Elmstrand and Matthew Ruge and Firefighter/Paramedic Adam Finseth.

'Thousands' Expected At Procession, Funeral For Burnsville Responders | Burnsville, MN Patch

Here is where Wednesday&#x27;s procession for first responders killed in Burnsville will go

Here is where Wednesday's procession for first responders killed in Burnsville will go

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Working With Wav2vec2 Part 1: Finetuning XLS-R for Automatic Speech Recognition

Wav2vec2 এর সাথে কাজ করা পার্ট 1: স্বয়ংক্রিয় বক্তৃতা স্বীকৃতির জন্য XLS-R ফাইনটিউনিং

Arbeiten mit Wav2vec2 Teil 1: Feinabstimmung von XLS-R für die automatische Spracherkennung

Trabajar con Wav2vec2 Parte 1: Ajuste de XLS-R para el reconocimiento automático de voz

Travailler avec Wav2vec2 Partie 1 : Affiner XLS-R pour la reconnaissance vocale automatique

Wav2vec2 के साथ कार्य करना भाग 1: स्वचालित वाक् पहचान के लिए XLS-R को परिष्कृत करना

Wav2vec2 の操作パート 1: 自動音声認識のための XLS-R の微調整

Wav2vec2 작업 1부: 자동 음성 인식을 위한 XLS-R 미세 조정

Trabalhando com Wav2vec2 Parte 1: Ajuste fino do XLS-R para reconhecimento automático de fala

Работа с Wav2vec2. Часть 1. Точная настройка XLS-R для автоматического распознавания речи

Wav2vec2 ile Çalışmak Bölüm 1: Otomatik Konuşma Tanıma için XLS-R'ye İnce Ayar Yapmak

Làm việc với Wav2vec2 Phần 1: Tinh chỉnh XLS-R để nhận dạng giọng nói tự động

使用 Wav2vec2 第 1 部分：微调 XLS-R 以实现自动语音识别

Stack: Older developers and engineers might remember the LAMP stack💡  and although it’s reengineered, if we strip down everything to the basics what we find and need is: an operating system


a web server


a database


a programming language What I’m going to talk to you today is how I reimagined the LAMP stack in an alternative format: Linux
Apache
MySql
Php
to:
Ubuntu
Apache
Sqlite3
Python What Does It Take? Nowadays, all that I hear all too often is: “I want to become a developer how do I get started?”, “I want to become a developer what laptop do I need?” or “Do I need to buy the latest MacBook to become a developer?” and to be honest, it’s starting to get to me. No kids, you don’t need the latest fancy tech or a rocket🚀 to become a developer, what you need is an operating system that is capable of installing programming packages and can compile them; you can even use your grandma’s old laptop. And now you ask: “How can this be?!” Let’s take a short trip down memory lane; almost 3 and a half years ago, I started tinkering with an SBC - single board computer that starts at under 50E to buy, and on it right now, I have a web server, a database, am performing hundreds of web-scrapes per hour, and on top of it, I’m working on developing an API endpoint. This SBC is not even the latest in its range and has: 8GB of LPDDR4  internal memory


1.5GHz quad-core ARM Cortex-A72 CPU (launched in 2015) - that you can easily and safely overclock at 2GHz (+fan extension & heatsink) Here’s what GPT has to say about these specs:
The specifications you provided are decent for certain types of computing tasks, but they may not be sufficient for others. Here's an evaluation: 8GB LPDDR4 internal memory:
LPDDR4 is a relatively fast and power-efficient type of RAM commonly used in mobile devices and some laptops.
8GB is a moderate amount of memory that should be sufficient for everyday tasks like web browsing, office work, and light multitasking. It can also handle moderate gaming and content creation applications.
However, for more demanding tasks like video editing, graphic design, or running virtual machines, 8GB may be limiting, and you might experience performance bottlenecks.
1.5GHz Quad-Core ARM Cortex-A72 CPU:


The ARM Cortex-A72 is a capable processor design commonly found in smartphones, tablets, and other mobile devices.


A quad-core configuration means it has four cores, allowing it to handle multiple tasks simultaneously more efficiently.


The 1.5GHz clock speed is decent for general-purpose computing tasks, including web browsing, media consumption, and productivity applications.


However, for more demanding tasks such as gaming, video editing, or running complex software, a higher clock speed and possibly more cores would provide better performance.
In summary, these specifications are suitable for light to moderate computing tasks, such as everyday productivity, web browsing, and multimedia consumption. However, for more demanding tasks or future-proofing, you may want to consider upgrading to a system with more RAM and a faster CPU. Trust me, in the beginning, light to moderate is more than sufficient, so with a Pi board, you can easily have a working desktop environment in which you can learn how to code, and not only frontend but even backend programming languages such as Python, c++ (which is even more energy efficient), or javascript. What do I use? On my Raspberry Pi, I am developing code with: Ubuntu Desktop - courtesy of desktopify:

Which I had to secure and in doing so I learned about:


web server vulnerabilities


firewalls


antivirus


intrusion prevention software IPS


networking


DNS


strong password management


ssh keys


SSL certificates






VSCode remotely connecting to the server


Python3 scripts which scrape the web and build HTML webpages embodied by CSS


requests


selenium


pandas


parallel processing - concurrent.futures




Javascript coupled with PHP here and there.


Python3 scripts that automatically write in my Sqlite3 database in JSON format.


Flask app, a small and lightweight Python web framework that provides useful tools and features that make creating web applications in Python.


And lastly, a Javascript piece of code that calls the Flask endpoint - a RESTful API endpoint
For close to 4 years at a definitely amortized cost of 120E and < 4E per month in electricity🔌. To summarize everything, here’s the BerryNews system architecture: The key factors here are Efficient Resource Utilization + Resource Utilization Constraints. Why splurge when you can write efficient code? Conclusion: If we were to sum up everything, we’d end up with a table such as this on a 100-dollar computer: Category

Keywords



Programming languages

Python3, Javascript, PHP, CSS, HTML, SQL



Data formats

JSON



Frameworks

flask, selenium, concurrent.futures



Databases

sqlite3



Networking

router config, DNS



Cybersecurity

UFW (firewall), ClamAV (antivirus), Fail2ban (IPS), password management, SSH Keys, Let’s Encrypt (SSL certificate)



Linux

Raspberry Pi, Ubuntu Desktop, VSCode So, tell me, do you still think you need the latest laptop specs to become a developer? Stack: Stack: Older developers and engineers might remember the LAMP stack💡  and although it’s reengineered, if we strip down everything to the basics what we find and need is: strip down everything to basics an operating system a web server a database a programming language an operating system an operating system a web server a web server a database a database a programming language a programming language What I’m going to talk to you today is how I reimagined the LAMP stack in an alternative format : the LAMP stack in an alternative format Linux Apache MySql Php
to: Ubuntu Apache Sqlite3 Python Linux Apache MySql Php to: to: Ubuntu Apache Sqlite3 Python What Does It Take? What Does It Take? Nowadays, all that I hear all too often is: “I want to become a developer how do I get started?”, “I want to become a developer what laptop do I need?” or “Do I need to buy the latest MacBook to become a developer?” and to be honest, it’s starting to get to me. No kids , you don’t need the latest fancy tech or a rocket🚀 to become a developer, what you need is an operating system that is capable of installing programming packages and can compile them; you can even use your grandma’s old laptop. And now you ask: “How can this be?!” No kids Let’s take a short trip down memory lane; almost 3 and a half years ago, I started tinkering with an SBC - single board compute r that starts at under 50E to buy, and on it right now, I have a web server, a database, am performing hundreds of web-scrapes per hour, and on top of it, I’m working on developing an API endpoint. SBC single board compute This SBC is not even the latest in its range and has: SBC 8GB of LPDDR4  internal memory 1.5GHz quad-core ARM Cortex-A72 CPU (launched in 2015) - that you can easily and safely overclock at 2GHz (+fan extension & heatsink) 8GB of LPDDR4  internal memory 8GB of LPDDR4  internal memory 1.5GHz quad-core ARM Cortex-A72 CPU (launched in 2015) - that you can easily and safely overclock at 2GHz (+fan extension & heatsink) 1.5GHz quad-core ARM Cortex-A72 CPU (launched in 2015) - that you can easily and safely overclock at 2GHz (+fan extension & heatsink) Here’s what GPT has to say about these specs:
The specifications you provided are decent for certain types of computing tasks, but they may not be sufficient for others. Here's an evaluation: 8GB LPDDR4 internal memory: LPDDR4 is a relatively fast and power-efficient type of RAM commonly used in mobile devices and some laptops. 8GB is a moderate amount of memory that should be sufficient for everyday tasks like web browsing, office work, and light multitasking. It can also handle moderate gaming and content creation applications. However, for more demanding tasks like video editing, graphic design, or running virtual machines, 8GB may be limiting, and you might experience performance bottlenecks. 1.5GHz Quad-Core ARM Cortex-A72 CPU:


The ARM Cortex-A72 is a capable processor design commonly found in smartphones, tablets, and other mobile devices.


A quad-core configuration means it has four cores, allowing it to handle multiple tasks simultaneously more efficiently.


The 1.5GHz clock speed is decent for general-purpose computing tasks, including web browsing, media consumption, and productivity applications.


However, for more demanding tasks such as gaming, video editing, or running complex software, a higher clock speed and possibly more cores would provide better performance.
In summary, these specifications are suitable for light to moderate computing tasks, such as everyday productivity, web browsing, and multimedia consumption. However, for more demanding tasks or future-proofing, you may want to consider upgrading to a system with more RAM and a faster CPU. 8GB LPDDR4 internal memory: LPDDR4 is a relatively fast and power-efficient type of RAM commonly used in mobile devices and some laptops. 8GB is a moderate amount of memory that should be sufficient for everyday tasks like web browsing, office work, and light multitasking. It can also handle moderate gaming and content creation applications. However, for more demanding tasks like video editing, graphic design, or running virtual machines, 8GB may be limiting, and you might experience performance bottlenecks. 1.5GHz Quad-Core ARM Cortex-A72 CPU: The ARM Cortex-A72 is a capable processor design commonly found in smartphones, tablets, and other mobile devices.


A quad-core configuration means it has four cores, allowing it to handle multiple tasks simultaneously more efficiently.


The 1.5GHz clock speed is decent for general-purpose computing tasks, including web browsing, media consumption, and productivity applications.


However, for more demanding tasks such as gaming, video editing, or running complex software, a higher clock speed and possibly more cores would provide better performance.
In summary, these specifications are suitable for light to moderate computing tasks, such as everyday productivity, web browsing, and multimedia consumption. However, for more demanding tasks or future-proofing, you may want to consider upgrading to a system with more RAM and a faster CPU. The ARM Cortex-A72 is a capable processor design commonly found in smartphones, tablets, and other mobile devices. A quad-core configuration means it has four cores, allowing it to handle multiple tasks simultaneously more efficiently. The 1.5GHz clock speed is decent for general-purpose computing tasks, including web browsing, media consumption, and productivity applications. However, for more demanding tasks such as gaming, video editing, or running complex software, a higher clock speed and possibly more cores would provide better performance.
In summary, these specifications are suitable for light to moderate computing tasks, such as everyday productivity, web browsing, and multimedia consumption. However, for more demanding tasks or future-proofing, you may want to consider upgrading to a system with more RAM and a faster CPU. The ARM Cortex-A72 is a capable processor design commonly found in smartphones, tablets, and other mobile devices. The ARM Cortex-A72 is a capable processor design commonly found in smartphones, tablets, and other mobile devices. A quad-core configuration means it has four cores, allowing it to handle multiple tasks simultaneously more efficiently. A quad-core configuration means it has four cores, allowing it to handle multiple tasks simultaneously more efficiently. The 1.5GHz clock speed is decent for general-purpose computing tasks, including web browsing, media consumption, and productivity applications. The 1.5GHz clock speed is decent for general-purpose computing tasks, including web browsing, media consumption, and productivity applications. However, for more demanding tasks such as gaming, video editing, or running complex software, a higher clock speed and possibly more cores would provide better performance.
In summary, these specifications are suitable for light to moderate computing tasks, such as everyday productivity, web browsing, and multimedia consumption. However, for more demanding tasks or future-proofing, you may want to consider upgrading to a system with more RAM and a faster CPU. However, for more demanding tasks such as gaming, video editing, or running complex software, a higher clock speed and possibly more cores would provide better performance.
In summary, these specifications are suitable for light to moderate computing tasks, such as everyday productivity, web browsing, and multimedia consumption. However, for more demanding tasks or future-proofing, you may want to consider upgrading to a system with more RAM and a faster CPU. Trust me, in the beginning, light to moderate is more than sufficient, so with a Pi board, you can easily have a working desktop environment in which you can learn how to code, and not only frontend but even backend programming languages such as Python, c++ (which is even more energy efficient), or javascript. desktop What do I use? What do I use? On my Raspberry Pi, I am developing code with: Ubuntu Desktop - courtesy of desktopify:

Which I had to secure and in doing so I learned about:


web server vulnerabilities


firewalls


antivirus


intrusion prevention software IPS


networking


DNS


strong password management


ssh keys


SSL certificates VSCode remotely connecting to the server Python3 scripts which scrape the web and build HTML webpages embodied by CSS


requests


selenium


pandas


parallel processing - concurrent.futures Javascript coupled with PHP here and there. Python3 scripts that automatically write in my Sqlite3 database in JSON format. Flask app, a small and lightweight Python web framework that provides useful tools and features that make creating web applications in Python. And lastly, a Javascript piece of code that calls the Flask endpoint - a RESTful API endpoint
For close to 4 years at a definitely amortized cost of 120E and < 4E per month in electricity🔌. Ubuntu Desktop - courtesy of desktopify: Which I had to secure and in doing so I learned about:


web server vulnerabilities


firewalls


antivirus


intrusion prevention software IPS


networking


DNS


strong password management


ssh keys


SSL certificates Ubuntu Desktop - courtesy of desktopify : Ubuntu desktopify Which I had to secure and in doing so I learned about:


web server vulnerabilities


firewalls


antivirus


intrusion prevention software IPS


networking


DNS


strong password management


ssh keys


SSL certificates Which I had to secure and in doing so I learned about: web server vulnerabilities


firewalls


antivirus


intrusion prevention software IPS


networking


DNS


strong password management


ssh keys


SSL certificates secure web server vulnerabilities firewalls antivirus intrusion prevention software IPS networking DNS strong password management ssh keys SSL certificates web server vulnerabilities web server vulnerabilities web server vulnerabilities firewalls firewalls antivirus antivirus intrusion prevention software IPS intrusion prevention software IPS networking networking DNS DNS DNS strong password management strong password management ssh keys ssh keys ssh keys SSL certificates SSL certificates SSL certificates VSCode remotely connecting to the server VSCode remotely connecting to the server Python3 scripts which scrape the web and build HTML webpages embodied by CSS requests


selenium


pandas


parallel processing - concurrent.futures Python3 scripts which scrape the web and build HTML webpages embodied by CSS Python3 HTML CSS requests selenium pandas parallel processing - concurrent.futures requests requests selenium selenium pandas pandas parallel processing - concurrent.futures parallel processing - concurrent.futures Javascript coupled with PHP here and there. Javascript coupled with PHP here and there. Javascript PHP Python3 scripts that automatically write in my Sqlite3 database in JSON format. Python3 scripts that automatically write in my Sqlite3 database in JSON format. Sqlite3 JSON Flask app, a small and lightweight Python web framework that provides useful tools and features that make creating web applications in Python. Flask app, a small and lightweight Python web framework that provides useful tools and features that make creating web applications in Python. Flask web framework And lastly, a Javascript piece of code that calls the Flask endpoint - a RESTful API endpoint For close to 4 years at a definitely amortized cost of 120E and < 4E per month in electricity🔌. And lastly, a Javascript piece of code that calls the Flask endpoint - a RESTful API endpoint endpoint RESTful For close to 4 years at a definitely amortized cost of 120E and < 4E per month in electricity🔌. To summarize everything, here’s the BerryNews system architecture: The key factors here are Efficient Resource Utilization + Resource Utilization Constraints . Efficient Resource Utilization Resource Utilization Constraints Why splurge when you can write efficient code? Conclusion: If we were to sum up everything, we’d end up with a table such as this on a 100-dollar computer: Category

Keywords



Programming languages

Python3, Javascript, PHP, CSS, HTML, SQL



Data formats

JSON



Frameworks

flask, selenium, concurrent.futures



Databases

sqlite3



Networking

router config, DNS



Cybersecurity

UFW (firewall), ClamAV (antivirus), Fail2ban (IPS), password management, SSH Keys, Let’s Encrypt (SSL certificate)



Linux

Raspberry Pi, Ubuntu Desktop, VSCode Category

Keywords Programming languages

Python3, Javascript, PHP, CSS, HTML, SQL Data formats

JSON Frameworks

flask, selenium, concurrent.futures Databases

sqlite3 Networking

router config, DNS Cybersecurity

UFW (firewall), ClamAV (antivirus), Fail2ban (IPS), password management, SSH Keys, Let’s Encrypt (SSL certificate) Linux

Raspberry Pi, Ubuntu Desktop, VSCode Category Keywords Category Category Category Keywords Keywords Keywords Programming languages Python3, Javascript, PHP, CSS, HTML, SQL Programming languages Programming languages Python3, Javascript, PHP, CSS, HTML, SQL Python3, Javascript, PHP, CSS, HTML, SQL Data formats JSON Data formats Data formats JSON JSON Frameworks flask, selenium, concurrent.futures Frameworks Frameworks flask, selenium, concurrent.futures flask, selenium, concurrent.futures Databases sqlite3 Databases Databases sqlite3 sqlite3 Networking router config, DNS Networking Networking router config, DNS router config, DNS Cybersecurity UFW (firewall), ClamAV (antivirus), Fail2ban (IPS), password management, SSH Keys, Let’s Encrypt (SSL certificate) Cybersecurity Cybersecurity UFW (firewall), ClamAV (antivirus), Fail2ban (IPS), password management, SSH Keys, Let’s Encrypt (SSL certificate) UFW ( firewall ), ClamAV ( antivirus ), Fail2ban ( IPS ), password management, SSH Keys, Let’s Encrypt ( SSL certificate ) firewall antivirus IPS SSL certificate Linux Raspberry Pi, Ubuntu Desktop, VSCode Linux Linux Raspberry Pi, Ubuntu Desktop, VSCode Raspberry Pi, Ubuntu Desktop, VSCode So, tell me, do you still think you need the latest laptop specs to become a developer? So, tell me, do you still think you need the latest laptop specs to become a developer?

RaspberryPi: Technology Overload - LAMP🕯 to UASP🐝

রাস্পবেরিপি: প্রযুক্তি ওভারলোড - LAMP🕯 থেকে UASP🐝

RaspberryPi: Technologie-Überlastung – LAMP🕯 zu UASP🐝

RaspberryPi: Sobrecarga tecnológica: LAMP🕯 a UASP🐝

RaspberryPi : surcharge technologique - LAMP🕯 vers UASP🐝

रास्पबेरीपाई: प्रौद्योगिकी अधिभार - LAMP🕯 से UASP🐝 तक

RaspberryPi: テクノロジーの過剰 - LAMP🕯からUASP🐝まで

RaspberryPi: 기술 과부하 - LAMP🕯에서 UASP🐝까지

RaspberryPi: sobrecarga de tecnologia - LAMP🕯 para UASP🐝

RaspberryPi: технологическая перегрузка — от LAMP🕯 до UASP🐝

RaspberryPi: Aşırı Teknoloji Yükü - LAMP🕯'den UASP🐝'ye

RaspberryPi: Quá tải công nghệ - LAMP🕯 đến UASP🐝

RaspberryPi：技术超载 - 从 LAMP🕯 到 UASP🐝

If you bumped into this page thinking you’re gonna get rich with some get-rich-quick scheme, I’m sorry to disappoint you. This article will rather talk about how to decrease your cloud cost bills by $1 million. By doing that, you will have essentially generated an extra million dollars in revenue — which you can spend buying my online course on how to get rich with AWS (link to course here). Cloud cost is often overlooked and unaccounted for at the beginning of Companies’ projects. The 2021 HashiCorp survey found that almost 40% of companies overspent on cloud costs in 2021 [1]. In 2023, almost all companies (94%) admitted that they were wasting money on the cloud [1] and at least 30% of cloud cost was wasted [2]. Cloud spending was almost $500 billion in 2022 — therefore we’re talking about $150 billion wasted a year!! Not only this is a concern of missed revenues but also poor sustainability practices. $150 billion of wasted energy! These findings involve large enterprises as well as smaller ones, from high-cloud maturity to low-cloud maturity. It refers to AWS, but the same principles can be applied to any other cloud provider. So, if any part of your job is in the cloud, then this article is for you. I’m speaking from a data engineer perspective, but the same learnings can be applied to other software engineering practices. Let’s dive in. What does it take to spend $1 million in cloud costs in a year? This kind of cloud bill is usually restricted to very large enterprises that operate globally with millions of customers. To give you an idea, a $1 million cloud bill can result from a Spark ETL job processing ~1.5Tb per hour 24x7 for 365 days a year. Another example might be an application that receives billions of requests a day from multiple locations in the world. In a large enterprise, there are hundreds of applications at this size — resulting in billion-dollar contracts with cloud-providers. For example, Airbnb had a commitment to spend $1.2 billion on cloud resources over five years at the end of 2019 [3]. At Expedia we slashed costs for a data processing ETL costing $1.1 million dollar a year to a mere $100,000 a year by implementing optimisation practices. That’s a 91% cost reduction!! Not all companies have applications of such a huge size but imagine cutting your cloud cost by 90% just for a single application or for your entire company. How do we start saving? STEP 1: Challenge your design assumptions Go and get a list of your most expensive applications and challenge your design assumptions. Are you building an application that has a 99.999% availability and sub-millisecond latency but realistically users would be good enough with a 99% availability and hundreds of millisecond latency?
Are you creating datasets with billions of rows but users would only be using aggregations of some of the measures?
Are you landing data real-time but data is only analysed once a day?
Are you refreshing the cache every 10 seconds but it’s only really changing across days? All these questions go back to the most important question: how is the application going to be used? What’s the business value for it to exist? How is the application helping us to achieve a given goal? Of course, all these answers are very often unclear at the beginning of a project; but that’s why design should always be an iterative process — allowing changes to happen as seamlessly as possible. Engineers should embrace evolution and change, aligning application development with impact. STEP 2: Fine-tune your infrastructure resources to your needs The second step consists of providing the application with the right resources and tuning it to the right infrastructure. As an engineer, be aware of how cloud costs are calculated. For example, AWS provides spot instances, where you can bid for the cluster price — this is particularly useful if you have fault-tolerant and flexible applications. Use them if you can — AWS claims up to 90% reduction in costs [4]. Some other considerations you might want to address are: Are you serving customers globally or only in one geographical area? Do you really need your infrastructure to live across the globe or can you set it up closer to your customer base?
Are you over-provisioning your cluster instances? Try to ensure there is enough capacity to handle peak loads without unnecessary costs. Utilise auto-scaling to dynamically adjust resources based on actual demand, preventing overpayment for idle resources.
If you’re working with data and Spark, make sure you understand Spark concepts and tuning! If you don’t, take a look at the following resources [5] [6] [7] [8] [9]. STEP 3: Use AWS Graviton instances There are little to no drawbacks in utilising AWS Graviton instances. AWS has invested heavily in creating the most cost-effective processors. You can get up to 40% reduction in cloud spending just by switching from an intel-based processor to an ARM-based processor [10]. The only caveat to this is that your application needs to be compatible with the ARM-based processors that Graviton runs on. If you’re dealing with a managed service such as RDS or OpenSearch then there’s no complication at all in switching — AWS deals with the underlying OS and application compatibility. If you’re building your own application, then you might need to recompile the package depending on which language you’re using — Java and other languages require no change whereas Python requires some attention. STEP 4: Monitor your cost spending and educate on cost awareness Lastly, don’t forget to keep monitoring your costs for unexpected peaks and surprises. The cost on day 0 of your application will be different from the cost on day 170. Make sure you keep track of the changes, and you understand why the change is happening: is it stacking s3 storage costs or is it just a one-off spike? Set up the necessary alerts and operational guidebooks! Importantly, implement cost allocation tags to track spending by department, project, or environment. Avoid the risk of creating a data swamp where cost is untraceable or requires a long journey across different log systems. It should be quick and simple to go back to any given application cost. Final thoughts Wherever you are working, balancing the delivery of new features with the optimisation of current ones is hard. Who hasn’t been pressured to deliver new quirky features at the speed of light. However, it is essential for both engineers and managers to make deliberate and proactive decisions about their current projects, managing risks and opportunities effectively. If you bumped into this page thinking you’re gonna get rich with some get-rich-quick scheme, I’m sorry to disappoint you. This article will rather talk about how to decrease your cloud cost bills by $1 million. By doing that, you will have essentially generated an extra million dollars in revenue — which you can spend buying my online course on how to get rich with AWS (link to course here). If you bumped into this page thinking you’re gonna get rich with some get-rich-quick scheme, I’m sorry to disappoint you. This article will rather talk about how to decrease your cloud cost bills by $1 million. By doing that, you will have essentially generated an extra million dollars in revenue — which you can spend buying my online course on how to get rich with AWS ( link to course here ). link to course here Cloud cost is often overlooked and unaccounted for at the beginning of Companies’ projects. The 2021 HashiCorp survey found that almost 40% of companies overspent on cloud costs in 2021 [ 1 ]. In 2023, almost all companies (94%) admitted that they were wasting money on the cloud [ 1 ] and at least 30% of cloud cost was wasted [ 2 ]. Cloud spending was almost $500 billion in 2022 — therefore we’re talking about $150 billion wasted a year!! 1 1 2 Not only this is a concern of missed revenues but also poor sustainability practices. $150 billion of wasted energy! These findings involve large enterprises as well as smaller ones, from high-cloud maturity to low-cloud maturity. It refers to AWS, but the same principles can be applied to any other cloud provider. So, if any part of your job is in the cloud, then this article is for you. I’m speaking from a data engineer perspective, but the same learnings can be applied to other software engineering practices. Let’s dive in. What does it take to spend $1 million in cloud costs in a year? What does it take to spend $1 million in cloud costs in a year? This kind of cloud bill is usually restricted to very large enterprises that operate globally with millions of customers. To give you an idea, a $1 million cloud bill can result from a Spark ETL job processing ~1.5Tb per hour 24x7 for 365 days a year. Another example might be an application that receives billions of requests a day from multiple locations in the world. In a large enterprise, there are hundreds of applications at this size — resulting in billion-dollar contracts with cloud-providers. For example, Airbnb had a commitment to spend $1.2 billion on cloud resources over five years at the end of 2019 [ 3 ]. 3 At Expedia we slashed costs for a data processing ETL costing $1.1 million dollar a year to a mere $100,000 a year by implementing optimisation practices. That’s a 91% cost reduction!! Not all companies have applications of such a huge size but imagine cutting your cloud cost by 90% just for a single application or for your entire company. How do we start saving? STEP 1: Challenge your design assumptions Go and get a list of your most expensive applications and challenge your design assumptions . challenge your design assumptions Are you building an application that has a 99.999% availability and sub-millisecond latency but realistically users would be good enough with a 99% availability and hundreds of millisecond latency? Are you creating datasets with billions of rows but users would only be using aggregations of some of the measures? Are you landing data real-time but data is only analysed once a day? Are you refreshing the cache every 10 seconds but it’s only really changing across days? Are you building an application that has a 99.999% availability and sub-millisecond latency but realistically users would be good enough with a 99% availability and hundreds of millisecond latency? Are you creating datasets with billions of rows but users would only be using aggregations of some of the measures? Are you landing data real-time but data is only analysed once a day? Are you refreshing the cache every 10 seconds but it’s only really changing across days? All these questions go back to the most important question: how is the application going to be used? What’s the business value for it to exist? How is the application helping us to achieve a given goal? how is the application going to be used? What’s the business value for it to exist? How is the application helping us to achieve a given goal? Of course, all these answers are very often unclear at the beginning of a project; but that’s why design should always be an iterative process — allowing changes to happen as seamlessly as possible. Engineers should embrace evolution and change, aligning application development with impact. very often unclear at the beginning of a project; STEP 2: Fine-tune your infrastructure resources to your needs STEP 2: Fine-tune your infrastructure resources to your needs The second step consists of providing the application with the right resources and tuning it to the right infrastructure. As an engineer, be aware of how cloud costs are calculated. For example, AWS provides spot instances, where you can bid for the cluster price — this is particularly useful if you have fault-tolerant and flexible applications. Use them if you can — AWS claims up to 90% reduction in costs [ 4 ]. 4 Some other considerations you might want to address are: Are you serving customers globally or only in one geographical area? Do you really need your infrastructure to live across the globe or can you set it up closer to your customer base? Are you over-provisioning your cluster instances? Try to ensure there is enough capacity to handle peak loads without unnecessary costs. Utilise auto-scaling to dynamically adjust resources based on actual demand, preventing overpayment for idle resources. If you’re working with data and Spark, make sure you understand Spark concepts and tuning! If you don’t, take a look at the following resources [5] [6] [7] [8] [9]. Are you serving customers globally or only in one geographical area? Do you really need your infrastructure to live across the globe or can you set it up closer to your customer base? Are you over-provisioning your cluster instances? Try to ensure there is enough capacity to handle peak loads without unnecessary costs. Utilise auto-scaling to dynamically adjust resources based on actual demand, preventing overpayment for idle resources. If you’re working with data and Spark, make sure you understand Spark concepts and tuning! If you don’t, take a look at the following resources [ 5 ] [ 6 ] [ 7 ] [ 8 ] [ 9 ]. make sure you understand Spark concepts and tuning! 5 6 7 8 9 STEP 3: Use AWS Graviton instances STEP 3: Use AWS Graviton instances There are little to no drawbacks in utilising AWS Graviton instances. AWS has invested heavily in creating the most cost-effective processors. You can get up to 40% reduction in cloud spending just by switching from an intel-based processor to an ARM-based processor [ 10 ]. 10 The only caveat to this is that your application needs to be compatible with the ARM-based processors that Graviton runs on. If you’re dealing with a managed service such as RDS or OpenSearch then there’s no complication at all in switching — AWS deals with the underlying OS and application compatibility. If you’re building your own application, then you might need to recompile the package depending on which language you’re using — Java and other languages require no change whereas Python requires some attention. STEP 4: Monitor your cost spending and educate on cost awareness STEP 4: Monitor your cost spending and educate on cost awareness Lastly, don’t forget to keep monitoring your costs for unexpected peaks and surprises. The cost on day 0 of your application will be different from the cost on day 170. Make sure you keep track of the changes, and you understand why the change is happening: is it stacking s3 storage costs or is it just a one-off spike? Set up the necessary alerts and operational guidebooks ! Set up the necessary alerts and operational guidebooks Importantly, implement cost allocation tags to track spending by department, project, or environment. Avoid the risk of creating a data swamp where cost is untraceable or requires a long journey across different log systems. It should be quick and simple to go back to any given application cost. Final thoughts Wherever you are working, balancing the delivery of new features with the optimisation of current ones is hard. Who hasn’t been pressured to deliver new quirky features at the speed of light. However, it is essential for both engineers and managers to make deliberate and proactive decisions about their current projects, managing risks and opportunities effectively.

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PROCESIO

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