Incoming Trump administration advisers Elon Musk and Vivek Ramaswamy floated ending remote work for federal workers, calling it a “privilege” left over from the pandemic.

Musk and Ramaswamy float ending remote work for federal employees and ‘large-scale firings’

Space Banana SpaceX's latest test flight of its Starship spacecraft carried a highly unusual payload into orbit: a single banana. Footage showed the interior of the behemoth rocket being adorned with one, lonely piece of fruit. It also conveniently puts the size of the spacecraft to scale.

Starship Shoots Into Orbit With Single Banana in Cargo Hold

Energy Secretary Ed Miliband is expected to scale back plans for 'hidden tax' on new boilers amid the backlash to Chancellor Rachel Reeves' debut Budget

Boiler tax slashed by Miliband in nightmare Budget panic | Personal Finance | Finance | Express.co.uk

COMPANY NEWS: Pure Storage®, the IT pioneer delivering the world’s most advanced data storage technologies and services, and CoreWeave, the AI HyperscalerTM, today announced Pure Storage’s strategic investment in CoreWeave to accelerate AI cloud services innovation.

iTWire - Pure Storage announces partnership with CoreWeave to accelerate large-scale AI Cloud Services Innovation

Securing climate finance on the necessary scale relies on unlocking private investment in climate solutions and change in the real economy, write WWF's Manuel Pulgar Vidal and Aaron Vermeulen

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The large-scale operation took advantage of open repositories, hardcoded credentials in source code, and other cloud oversights.

EmeraldWhale's Massive Git Breach Highlights Config Gaps

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Hackers steal 15,000 cloud credentials from exposed Git config files

Specifically, SuiHub Dubai will provide Web3 startup founders access to workshops, technical support, and community-driven events, as well as resources, funding, and technical expertise, ensuring they have the support needed to launch and scale their projects using Sui’s blockchain infrastructure.

Sui Launches ‘SuiHub Dubai’, Its First Global Hub to Drive Web3 Innovation | Coinspeaker

Unfortunately, the only thing truly surprising about this high-profile breach is the sheer scale of the information leaked, and the number of individuals affected. That said, this attack is far from anomalous in the world of enterprise cybersecurity.

Despite the Devastation, the National Public Data Breach Is Anything But Irregular | Manufacturing.net

scale

The Hidden Cost of Hypergrowth: Scale Without Sacrificing Stability

If you’re a marketing department and you want to reach the widest audience possible, text messaging is the best channel to do that with. Most people check their texts more often than email, and text messages have a 90%+ open rate within three minutes of reception. To send SMS and MMS messages at scale you need a cloud communications platform — a.k.a. a communications platform as a service (CPaaS). Peer-to-peer review site G2 lists more than 100 of them, with Plivo ranked number one for customer satisfaction. Throughout this article we’ll use Plivo and Twilio, the CPaaS market share leader, as examples. What’s Your Number? Once you’ve settled on a CPaaS, you have another decision to make: What number type should you use to send your messages? g follows different rules from person-to-person (P2P) texting. Businesses have three options: Commercial application-to-person (A2P) messagin 10-digit long codes (10DLC), toll-free numbers, and short codes. 10DLC Numbers Long codes are normal 10-digit phone numbers — the kind that individuals use every day to engage in P2P conversational messaging. is a service offered by major US carriers to explicitly allow commercial A2P SMS traffic over long code phone numbers. 10DLC is available only in the US. 10DLC You can turn long codes into 10DLC numbers by registering them with a carrier, which you can do through your CPaaS provider. Provisioning a 10DLC number takes less than a week. A vetting process determines the maximum throughput a carrier will allow for a 10DLC number. Throughput is the number of messages they can send in a given time period — messages per second, for example. Toll-Free are used by businesses that want their customers to reach them at no cost. In North America, 800, 888, 877, 866, 855, 844, and 833 denote toll-free numbers. Toll-free numbers Businesses provide toll-free numbers so that incoming calls are free of charge for the calling party, but they can also be enabled for SMS messaging through your carrier or CPaaS, generally in about one business day. Short Codes are five- or six-digit numbers used to send and receive SMS messages. Short codes Unlike long code numbers, shortcodes are not tied to an area code, so they can be used across a country to provide a nationwide brand identity under one easy-to-remember number. Short codes take the longest to acquire — up to 16 weeks. Throughput and Cost Two major differences between number types are their maximum throughput and cost. Throughput Limits Carriers impose varying maximum throughput on different number types. Generally speaking, short codes support the highest throughput and toll-free numbers the lowest. 10DLC numbers sit in the middle. Carrier-imposed throughput for 10DLC numbers depends on vetting scores assigned by an independent organization called The Carrier Registry. Cost Considerations There are three kinds of costs involved in messaging: one-time setup costs, number rental costs, and cost per message. All pricing numbers that we quote below were taken from the two representative vendors’ pricing pages. Short codes incur the highest setup costs: $1,500 for Plivo and $500 for Twilio. 10DLC numbers incur $44 charges for brand and campaign registration and vetting. There are no one-time charges for toll-free numbers. Number rental costs for long codes and toll-free numbers are negligible compared to usage costs for organizations that send a lot of messages. Short code rental costs, however, are significant. Cost per message depends on several factors: whether you’re sending or receiving messages, whether you’re using SMS or MMS, and which carrier you use. Your CPaaS vendor’s pricing page can break it down for you. For comparison purposes, we put together a table showing pricing for sending SMS messages, which for many businesses is the most common use case, and arbitrarily chose T-Mobile as the carrier for which to show surcharges in this table; the “plus” numbers are the T-Mobile carrier surcharges. Run the Numbers All of the different options make it difficult to figure out which number type makes the most economic sense. The best way to decide is to create a spreadsheet that includes the number of SMS and MMS messages you plan to send and receive per month, and factor in your carrier’s surcharges. Regardless of which number type you use, message senders must obtain opt-in consent before texting consumers — and be aware of other considerations. Plivo’s SMS best practices guide lays out those considerations for businesses that want to take advantage of the best channel for business communications — text messaging.

How to Send SMS and MMS Messages at Scale: What is Your Number?

Cómo enviar mensajes SMS y MMS a escala: ¿Cuál es su número?

Comment envoyer des messages SMS et MMS à grande échelle : quel est votre numéro ?

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SMS および MMS メッセージを大規模に送信する方法: あなたの番号は何ですか?

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Building a food delivery fleet of hundreds of robots is like teaching math to third graders. Their ability to learn and interact with their surroundings emotionally is a work in progress, but the sky is the limit. If carried out well, growth and success are very likely. Since starting in 2017, we’ve been scaling robotic technology to create smart cities while demonstrating to the general public and investors the extraordinary potential of the latest technological advances. Not only was and automation important, but setting boundaries and acknowledging the difficulties of the competitive tech space are key to success. company culture Most importantly, some of the most progressive companies deliberately take on employees with track records reflecting both as failing is an essential part of growth. failure and success So if your fintech launch doesn’t go to plan or SaaS doesn’t gain traction, however painful it may be to let go and move on, you’re not alone. Almost of startups pivot by changing their business plan. 60% With plans to grow tenfold next year and expand our team, this is what I’ve learned about scaling operations. Assess Your Company’s Capability to Take on Large Projects There are key performance indicators (KPIs) and quantitative metrics to measure a company´s success and help calculate the mid to long-term predictions to achieve profitability. I call them complex or compounded metrics – they are formed by input variables and governed by cost and time. For example, at Kiwibot, we look at how much it costs to operate our robots per kilometer. Any journey in a pilot program that costs over $2 per km is not considered a progression from regular delivery fees. The project would need far more testing to be able to scale up. What robotic delivery fleets are doing is similar to what Amazon did when eCommerce was less prevalent a few years ago: Exploring uncharted territory and working their way into the spotlight. There were plenty of attempts to set up eCommerce sites and sidewalk robotic delivery services but only those that leverage existing technological capabilities, and apply them in new ways, can succeed. Amazon didn’t invent anything new, they just leveraged the infrastructure and technology that already existed to take shipping speed to the next level. is a vital aspect of your company’s capability to scale too – and is an indicator of this. Employees must show flexibility, persistence, resilience, and be prepared for challenges. Human capital culture Startups are the kings of ambiguity, and the people you want by your side when times get tough are those that have equal levels of passion and persistence, otherwise known as . grit Search for individuals who have and have irreplaceable knowledge from overcoming hardships. Most importantly, fail as fast as possible. survived failure According to Jeff Bezos, the defining attribute of Amazon is its : “Most large organizations embrace the idea of invention, but are not willing to suffer the string of failed experiments necessary to get there.” appreciation of failure And as soon as you see something that doesn't work, say goodbye. It doesn’t matter to an idea or innovation, there’s just no point going over old territory. how attached you felt Start Small to Control the Risk of Growing Too Fast Startups must be optimistic, but they often tend to overshoot with overly hopeful objections. Always make sure to underpromise and overdeliver. Take calculated risks as you assess opportunities, start with small operations, and set up shop in one major city, allowing you to sprawl. This is what would be referred to as a “cluster” – where concentrations of interconnected companies and institutions from a specific field are situated – a generic concept used by many years ago that still serves today. Michael Porter As a company scales, it must cluster around points of interest and use cases that deliver traction. It’s vital to partner with organizations in a chosen region and improve relationships with city authorities. Once you have one client signed or a city in the bag, there will be proof of your product gaining customers, and the company will grow swiftly. For example, university campuses or cities like Los Angeles and Miami are significant markets for food delivery, so that’s where Kiwibot has active operations. We have the capacity as a company to expand to Scotland and Wales, for example, but would that be good for business? Probably not. My advice would be: Always choose those “second cities,” know your limits, and set boundaries. Pushbacks: Why It’s Harder to Scale Tech Companies Scaling a tech company is notoriously difficult because of ongoing public antitrust sentiment and the sheer cost. Andy Grove, one of Intel’s founders, once warned that it can be difficult for smaller US tech companies to expand while , and this can mean missed opportunities. competing with giant rivals Delivery robots are part of the green revolution and have a set speed governed by laws in cities across the US. Indeed, technology needs regulation, but with that comes added taxes or licenses which can impact tech companies’ budgets. However, these taxes could help generate revenue for city authorities and cover the costs associated with robotic delivery service operations. Tax implications are what transport technology companies should watch out for – it’s a repetition of the Uber and Lyft stories all over again. Now, consider this example. Buildings have not been designed for robots, but there is a high demand for delivery robots to have self-autonomy indoors. Imagine getting your food order delivered to your desk or a dress from Bloomingdales brought to your apartment before a date. In theory, it’s an incredible invention. Still, it involves APIs to facilitate communication between robots and elevators, the merging of technologies, and a lift company to actually have the appropriate software. It poses a costly venture which can be a blocker to innovation. Malls have the potential to drastically innovate with robots – like Westfield or Halfords – as they search for a leading edge. But will they take that leap? This challenge is similar to what Google struggled with twenty years ago – the development of the cloud, servers, mapping data, as well as physical infrastructure. All the foundations had to be laid before the infrastructure could be built. Over time, robotic technology will become cheaper as demand skyrockets. Think about Yelp’s algorithm, for example, which allows you to find a restaurant that has a view or allows pets – something that is now viewed as indispensable. That opportunity came from mapping technology, and billions were spent on that by Google who now own about 80 percent of the market. Learning to Manage a Lack of Resources US Cities are now busy planning to revive the economy and help local businesses grow post-pandemic. One initiative that was introduced was food ordering apps in restaurants. But when 80% of deliveries are made by cars powered by gas, cities are searching for green opportunities for local businesses. Diverging demand to delivery robots could be the answer, but there aren’t sufficient resources. Covid-19 exposed the lack of tech infrastructure at a local level. From a technology standpoint, there are several major obstacles for robotic fleets to work with local businesses: The cost, legal issues, logistical aspects, and the fact that each has a different app. The solution is to integrate with their apps and offer an alternative tool for delivery. It is vital that technology providers work together to offer free IT support and advice to those local businesses who need it, in an effort to protect jobs and the wider economy. When more and more tasks are becoming digitized or automated, but workers do not possess the skills that the technology requires, the outcome will be greater inequality. Overall, it’s vital for tech companies wanting to scale to do market research, spend time creating demand for their product, assess their qualitative and quantitative metrics, and strive for continuous growth.  The best way to move forward is by searching for innovative solutions to stop a lack of resources or high costs from being a barrier to innovation.

What Tech Companies Should Watch Out for When Scaling Operations 

This will be the first article on a series dedicated to the micro-frontends topic. This one will focus on and an suitable for an enterprise application at scale while keeping the advantages that a Single Page Application ( provides to our end users. analysing designing architecture SPA) By the end, you’ll have a working platform with a scalable micro-frontends approach. Why Micro-Frontends? First of all, you might be wondering why do you even need this, right? Well, there are several answers to this question, and I’m definitely not an advocate of using this pattern/architecture “just because”. I’ll provide a concrete example where this makes sense: You’re in a big enterprise project, it’s a big monolithic application encompassing several different “products” inside one single project/product. The company most likely chose this initial model to start fast and get to market soon. Now, they are established and new engineers come to the team. The team grows a lot, and dedicated teams for each sub-product starts to feel like a good and realistic possibility. Maybe each sub-product can have its own product manager, to extract the most out of each product and to enable the teams to focus on smaller parts of the platform. Up until now, the teams have been working horizontally, meaning that they are working focused on the technology itself (front-end, back-end), instead of focused on the Product. The company decides that vertical teams will be more productive on the long term and also more scalable as new engineers get into the company. These vertical teams will hold all the knowledge inside a specific product and will be composed by the 3 related horizontal layers (front-end, back-end, infrastructure), which will also allow growing each product individually. With this approach, the company can also get more independent teams and deployments, by not being dependent on a single gigantic release to get a new version out to the clients. Given these changes, how will the front-end reflect them? Solutions After a lot of investigation, I concluded that micro-frontends would be the solution to the scenario presented above. But micro-frontends has many faces, many technologies possible, many opinion articles (just like this one) and it’s not easy to know where and what to bet on. I’ll be presenting an that is but that still leverages the and performance that we love to rely on to deliver a great experience to our customers. architecture suitable for scale SPA feel The imaginary business that we are going to use for these articles is a store of clothing items (dresses, shirts, pants, etc). This business is a case of the scenario presented above, working in a horizontal structure on a monolithic application. Now, to increase team independence, they want to create 3 vertical teams for the different sub-products of their platform: , and the . Items Checkout, Blog Architecture To achieve the objective of the clothing company presented in the previous section, we need to design an architecture that enables all the teams to be independent while connecting all these individual products into a coherent platform. The following technologies will allow us to achieve what we need: ReactWebpack 5 with Module Federation Yarn Workspaces with lerna The following architectural diagram represents an overview of the final solution. This is what we’ll achieve at the end of this series of articles on ( ). micro-frontends MFEs All of the components presented in the architecture are , and as such, each is , and but all of them tie together to deliver a consistent experience to the customer. MFEs independently developed tested deployed, App Shell This MFE will be the into the platform. single entry point It will be responsible for rendering the first page to our user and also for invoking the respective micro-frontends as the user navigates through our platform. Using , we will be able to asynchronously load the necessary micro-frontends , keeping the solution memory and performance efficient. client-side navigation at run-time It’s pretty common to find this App Shell approach in micro-frontend projects, but some of the approaches are not exactly like this. They follow more of a where each MFE can be the entry point for the user, and then that MFE will import the “setup” code from the App Shell (e.g. Base Layout with the Header and Footer). Although this approach is perfectly valid, it will most definitely not feel like an SPA to the end user while switching between different entry points for the platform, since it will force a refresh each time the user navigates to a domain from a different MFE. micro-services approach Shared Inevitably, there are components that will be shared between our different MFEs, and for that reason the Shared MFE will be their home. Regarding this MFE, in my opinion, there is not a team specifically responsible for this, instead, there should be a “caretaker” of the MFE that would review any pull request created regarding shared components. This person would be responsible for keeping order in Shared and also for keeping a critical thinking on the following question: “Will this change affect other MFEs?”. Items / Checkout / Blog These MFEs are the actual products with dedicated teams working on them. They’re all in development and deployment. None of them is directly dependent on the other. Naturally, since the platform works as a whole, we need to cover the possibility of some MFE being broken and handle that gracefully. However, this approach will not let any MFE break the whole platform, as long as we handle the remote imports properly. independent Repository Structure We’re going to use a for this solution. monorepo The option to use a is arguable, and for all the cons it may have, the I’m after by using it are: monorepo pros Easily shareable configurations (linters, tasks, setup jobs, code styles)Better / easier overview of the whole systemEasier to make a gradual migration from an existing monolith Architecture (in detail) Let’s see in more detail how these components interact with each other and how exactly we enable the features mentioned in the previous section. The key to making this whole architecture work is . Module Federation Webpack 5 Module Federation aims to solve the sharing of modules in a distributed system, by shipping those critical shared pieces as macro or as micro as you would like. It does this by pulling them out of the build pipeline and out of your apps. The excerpt is from the official a new feature shipped officially on . If you want to learn more you can read the information on the website and also check out their section. There is also a whole with examples using this new technology. Module Federation page , Webpack 5 videos GitHub project Very briefly, Module Federation provides us the chance to code (components, functions, variables) at , while efficiently managing dependencies between different remotes. remotely import javascript run-time Basically, any application can be classified as a (in case it’s the entry point) or a (in case it’s imported by a host). An application can be both a host and a remote at the same time, in which case it’s called . host remote bi-directional Next, let’s analyse some common micro-frontends pitfalls and see how we can get around them using the presented architecture. Common pitfalls Dependency Duplication Using a micro-frontend approach may lead to importing dependencies multiple times. When the components are remotely imported from another MFE, they need certain dependencies to work. Chances are that the MFE importing that piece of code already has some (or all) of those dependencies. Without highly customised caching for dependencies, the imported components will most likely bring along their own dependencies, causing the host to have multiple instances of the same libraries (e.g. having different versions of React in scope may lead to unexpected problems). With the presented architecture we’ll take advantage of to manage these dependencies. When a remote module is imported at run-time, Webpack will actually check if it already has the dependencies required in the . If they are there, then the only thing imported from the remote is the actual code. Module Federation host Dependency Mismatches In micro-frontends, sometimes different versions of the same library are loaded in the same scope. To avoid this, we’ll be using a combination of to manage our monorepo and to manage the dependencies of our different MFEs. lerna yarn workspaces By using yarn workspaces we’ll make sure that the versions of critical libraries are locked for all the micro-frontends, preventing this whole class of problems. This approach reduces independence, but it allows a more robust overall solution. UI / UX In a micro-frontends solution, it often happens that the and diverge between different MFEs. It’s a natural process when different independent teams are each developing their products. At the same time, we need to keep in mind that the different products form the platform as a whole and that platform should provide a consistent experience to the user. UI UX This should happen both in the UI components and in the flows / user experience provided. In the solution presented, by using a Shared MFE, we are keeping these same visual components and reusable behaviours in the same package. This will guarantee that the UI experience is unique and consistent. At the same time, we’re giving this Shared MFE the independence it needs to release frequently. This will help fix small problems in the common components without needing to redeploy any other product MFE (like Items and Blog). Local Development Local development can be taxing in a micro-frontends solution, sometimes needing to run the whole solution in order to test what you are developing in a small part of that platform. The way we’re going to solve this is by providing the chance for all the different MFEs to be run in mode. This will be done by providing 2 separate entry points to our MFE, one for the integration with the whole MFE solution and another for the stand-alone entry point. stand-alone Conclusion The architecture presented in this article was achieved after a lot of investigation on the topic and you can check a lot of material in the section below. References In the next articles, we’ll be deep-diving on how to implement this architecture by following along a github repository with the example project for our imaginary business use case. We’ll also talk about common challenges that occur when using and what can we do to solve them. Module Federation Stay tuned 😉 If you find this article interesting, please share it, because you know — Sharing is caring! If you enjoy working at a large scale in projects with global impact and if you like a real challenge, feel free to reach out to us at xgeeks ! We are growing our team and you might be the next one to join this group of talented people 😉 Check out our social media channels if you want to get a sneak peek of life at xgeeks! See you soon! References https://martinfowler.com/articles/micro-frontends.html https://dev.to/marais/webpack-5-and-module-federation-4j1i https://blog.nrwl.io/monorepos-and-react-microfrontends-a-perfect-match-d49dca64489a https://dev.to/brandonvilla21/micro-frontends-module-federation-with-webpack-5-426 https://www.nicolasdelfino.com/blog/micro-frontends-module-federation-webpack https://indepth.dev/webpack-5-module-federation-a-game-changer-in-javascript-architecture https://blog.bitsrc.io/revolutionizing-micro-frontends-with-webpack-5-module-federation-and-bit-99ff81ceb0 https://medium.com/dazn-tech/orchestrating-micro-frontends-a5d2674cbf33 https://medium.com/hacking-talent/two-years-of-micro-frontends-a-retrospective-522526f76df4 https://single-spa.js.org/docs/recommended-setup/ https://medium.com/swlh/webpack-5-module-federation-a-game-changer-to-javascript-architecture-bcdd30e02669 https://module-federation.github.io/videos Also read behind a paywall at https://medium.com/xgeeks/micro-frontends-at-scale-part-1-a8ab67bfb773

An Introduction to Micro-Frontends in Enterprise Applications (part 1)

I’ve come to talk with you again Photo by on Shubham Dhage Unsplash I’ve written a whole bunch of blogs about running Cloud Foundry on Kubernetes. They all work, to varying degrees of success and have the support of a smattering of communities (read: companies with large budgets + engineers + open source ideals). Sometimes you just want BOSH. That sweet, sweet, familiar BOSH. Enter . bosh-lite What is ? bosh-lite works by spinning up a VM and placing BOSH on it. From there, you can use it to spin , , or anything else that has a BOSH manifest. bosh-lite cf-deployment zookeeper The BOSH director is configured with runtime configs, cloud configs, stemcells, releases, and all the other BOSH-like functionality you’ve come to expect. Installation The tool has been around forever, with a very good tutorial on installing and configuring located at . The rest of this blog assumes that you have followed these instructions up to step #7. bosh-lite https://bosh.io/docs/bosh-lite/ I had an issue with a newer version of VirtualBox (6.1.28), what worked for me is version 6.1.26 which is available . here It ends with an example deployment of which I guess is cool, but I’m guessing most BOSH directors are associated with a Cloud Foundry deployment. Read on for tips that maybe aren’t obvious from the documentation! zookeeper Deploying Cloud Foundry In -land there is documentation for using BBL to deploy and CF to . I point these instructions out in case you have an unlimited IAAS budget, however, I’ll show you how to deploy this to your Mac. cf-deployment bosh-lite GCP or AWS There is also a Readme within . I’ll be using this as the basis for the scripting below. cf-deployment For Operators Deploying CF to local bosh-lite Clone cf-deployment For these examples, I’ve assumed you will clone the CF repo to your home folder: cd ~

git clone https://github.com/cloudfoundry/cf-deployment.git Setup BOSH Environment variables This is spread across a few places in the documentation, I’ve gathered them all together: export CREDHUB_SERVER=https://192.168.56.6:8844
export CREDHUB_CLIENT=credhub-admin
export CREDHUB_SECRET=$(bosh interpolate ~/deployments/vbox/creds.yml --path=/credhub_admin_client_secret)
export CREDHUB_CA_CERT="$(bosh interpolate ~/deployments/vbox/creds.yml --path=/credhub_tls/ca )"$'\n'"$( bosh interpolate ~/deployments/vbox/creds.yml --path=/uaa_ssl/ca)"

export BOSH_CLIENT=admin
export BOSH_CLIENT_SECRET="$(bosh int ~/deployments/vbox/creds.yml --path /admin_password)"
export BOSH_CA_CERT="$(bosh interpolate ~/deployments/vbox/creds.yml --path /director_ssl/ca)"
export BOSH_ENVIRONMENT=vbox

bosh alias-env vbox -e 192.168.56.6 --ca-cert <(bosh int ~/deployments/vbox/creds.yml --path /director_ssl/ca) You might consider adding this to your bash/zsh profile. Upload Stemcell, Cloud Config and Runtime Config bosh upload-stemcell --sha1 f399044d2ebe3351f0f1b0b3f97ef11464d283b4 "https://bosh.io/d/stemcells/bosh-warden-boshlite-ubuntu-xenial-go_agent?v=621.125"
bosh update-runtime-config ~/workspace/bosh-deployment/runtime-configs/dns.yml --name dns
bosh update-cloud-config ~/cf-deployment/iaas-support/bosh-lite/cloud-config.yml As time goes by, CF may complain of wanting a newer stemcell version, update the command with the requested version. The version and are listed , note that uses stemcells. bosh upload-stemcell sha1 here bosh-lite warden Go Get a Drink This part will take around 45 minutes to complete, unless you’ve changed the IP address of the BOSH director, the following command will deploy CF. cd ~/cf-deployment
bosh -e 192.168.56.6 -d cf deploy \
  cf-deployment.yml \
  -o operations/bosh-lite.yml \
  -v system_domain=bosh-lite.com Log Into CF This is done in two steps, targeting the API and then logging in, pulling the admin password out of credhub without printing it to the screen: cf api https://api.bosh-lite.com --skip-ssl-validation
cf login -u admin -p $(credhub get -n $(credhub find -n admin | grep cf_admin | cut -d: -f2) | grep value | cut -d: -f2) -o system -s test Tada! Create a space and start pushing apps. (Optional) Setup SSH to BOSH Director Why? I like to be able to SSH to my BOSH Director to poke around. I’m weird. This is not required for the CF deployment, but nice to have: bosh int ~/deployments/vbox/creds.yml --path /jumpbox_ssh/private_key > ~/deployments/vbox/jumpbox.key
chmod 600 ~/deployments/vbox/jumpbox.key
ssh jumpbox@192.168.56.6 -i ~/deployments/vbox/jumpbox.key Beware of Dragons Issue #1 – Reboot Rebooting your Mac causes to misbehave, badly, unless you follow a couple of easy steps. bosh-lite Before shutdown: In Oracle VM VirtualBox Manager select the VM, right click and navigate to > Close Save State After reboot: In Oracle VM VirtualBox Manager select the VM, right click and navigate to > Start Headless Start Even performing these steps I can’t always bring the BOSH Director back online. If you have accidentally rebooted the host you can recreate the BOSH director using the state file and use to recover the broken CF deployment: bosh cck cd ~/deployments/vbox

bosh create-env ~/workspace/bosh-deployment/bosh.yml \
  --state ./state.json \
  -o ~/workspace/bosh-deployment/virtualbox/cpi.yml \
  -o ~/workspace/bosh-deployment/virtualbox/outbound-network.yml \
  -o ~/workspace/bosh-deployment/bosh-lite.yml \
  -o ~/workspace/bosh-deployment/bosh-lite-runc.yml \
  -o ~/workspace/bosh-deployment/uaa.yml \
  -o ~/workspace/bosh-deployment/credhub.yml \
  -o ~/workspace/bosh-deployment/jumpbox-user.yml \
  --vars-store ./creds.yml \
  -v director_name=bosh-lite \
  -v internal_ip=192.168.56.6 \
  -v internal_gw=192.168.56.1 \
  -v internal_cidr=192.168.50.0/24 \
  -v outbound_network_name=NatNetwork --recreate

bosh cck If that doesn’t work, you can completely wipe and recreate the BOSH and CF deployment by: Logging into Oracle VM VirtualBox Manager, select the VM, right click and select > Remove Delete all files Delete the state file at ~/deployments/vbox/state.json Rerun the command then redeploy CF. bosh create-env Issue 2 – Networking issues If you get: Deploying:
  Creating instance 'bosh/0':
    Waiting until instance is ready:
      Post "https://mbus:<redacted>@192.168.56.6:6868/agent": dial tcp 192.168.56.6:6868: connect: connection refused

Exit code 1 You probably skipped the step to add the host routing: sudo route add -net 10.244.0.0/16     192.168.56.6 Final Thought We all deserve nice things. is one of those nice things for folks who enjoy BOSH and want to use it on their own computer. bosh-lite Enjoy! Also published . here

Spin Up a BOSH VM Faster Using BoshLite

When I started working on a start-up nine months ago, this statement didn’t satisfy me or my co-founders: “There is no way to build a Slack bot more reliable than a Slack itself.” — common sense. Our product is an incident management tool called . It’s a Slack app and yes, it should be super reliable. We didn’t leave our SE jobs in Silicon Valley and London to give up when faced with our first technical challenge. Amixr.IO What’s a reliable web service? And what’s not? In terms of reliability, Slack is pretty good. They do a lot of good things. For example they keep users posted about problems through and . status page twitter Unfortunately, Slack has also experienced serious periods of downtime, and users remember that. That’s why users don’t expect an uptime-critical service, such as the our tool, to rely on it. We’ve found a way to achieve that goal. Over the last few months, our service has proved much more reliable than Slack, and we’ve even unveiled an SLA because we’ve built something that customer can rely on. Six simple principles described below helped us here. 1. Draw a red line Any Slack app, as well as any other bot for Facebook or Telegram, uses a web server. Slack is just an interface that allows users to interact with a system. That means we can draw a red line across all of our applications. Parts that are connected to Slack are in danger; other parts depend only on us. Imagine you’re creating a small Slack app. Its purpose is to post a message about new cards on your Trello board. The bot could be a simple web server that will wait for a from Trello in order to post a new message to Slack. It could be as simple as: webhook slackclient SlackClient rest_framework.views APIViewё sc = SlackClient(<bot_access_token>)
        sc.api_call( ,
           channel=<channel_id>,
           text= ,
        ) from import from import : class TrelloForwarderAPIView (APIView) : def get (self, request) "chat.postMessage" "New Cards in Trello detected" Let’s imagine that sc.api_call raises an Exception because the Slack API doesn’t work. I’m sure it won’t last long, so there’s no need to panic. However, you won’t be able to publish a message about this particular Trello card. It will be missed, forever, and your business could be damaged. Now, let’s draw a line between the Trello part and the Slack part and sandbox everything where Slack could cause data loss. To do that, we’ll receive a message from Trello, write it to a queue, and then only remove it from the queue when the message has been successfully posted. celery shared_task slackclient SlackClient rest_framework.views APIView sc = SlackClient(<bot_access_token>)
    sc.api_call( ,
        channel=<channel_id>,
        text= ,
    ) notify_slack.apply_async() from import from import from import @shared_task(autoretry_for=(Exception,), retry_backoff=True, max_retries=None) : def notify_slack () "chat.postMessage" "New Cards in Trello detected" : class TrelloForwarderAPIView (APIView) : def get (self, request) We use Celery, RabitMQ, and Django. Celery’s apply_async method publishes a task to RabbitMQ. In our case, it’s a highly available cluster. Celery workers look out for new tasks and try to execute them until they’re executed, without exception. Now, we can be sure that we won’t lose data if the Slack API causes exceptions on our side. 2. “Broken” doesn’t always mean that “everything is broken” Slack is huge. It has millions of users online and uses multiple servers in regions across the world. If something goes wrong in one place, it doesn’t mean that everything is broken at another location. Even if 99% of servers aren’t able to proceed with a request, there’s another 1% that can. That’s exactly what to Slack in June when its servers weren’t able to process a small fraction of our requests. However, our server didn’t give up, and sent everything successfully. Just nine messages got stuck at one moment, but we’ve sent them successfully since then. happened 3. Be notified early We love it when Slack works, but we need to know when it doesn’t. That’s why we monitor Slack ourselves. We check message delivery, verify API responses, and so forth. We proactively monitor Slack and sometimes detect issues, such as the one detailed in this message regarding the , before anyone else. blocks API If you’re building your service on top of another service, then write scripts and automated tests that proactively monitor the third party. That’s the only way to be the first to learn about a problem. 4. Prepare a Plan B OK, so you’ve built an awesome Slack app that works even when Slack doesn’t. How can you be sure that everything is ready? . It’s just a small example of a fascinating subject called chaos engineering ( ). Simulate a problem, even in production, then check your monitoring and your backup systems. Try simulate Slack downtime yourself https://principlesofchaos.org/ A huge part of this trick is known as “graceful degradation.” It’s the ability to deactivate non-critical parts if they don’t work as expected. In the case of total Slack downtime, our app will switch to a back-up delivery channels, such as e-mail, phone, or another messaging service. It reduces functionality and delivers business value anyway. 5. People should be close We don’t let a critical situation evolve without qualified people watching the process carefully. Even if a case-critical situation is 100% what we expected and recently tested for, there’s still a huge chance of the unexpected occurring. Sometimes, engineers consider how to fix problems temporarily while causing as little damage as possible. For instance, during the issue with the blocks API, we were able to find a workaround pretty quickly. We dealt with the issue before Slack fixed the problem on its side. 6. Talk with clients When discussing outages, we’ve chosen a proactive strategy. We actively inform our users about outages before they notice anything suspicious. If that happens, they won’t consider our service unreliable — they’ll be confident that we know about the problem and are doing our best to fix it. Fun fact: At the very beginning ,we did not have reserve notification channels about a downtime. For the first time, I had to look for users on social networks, write on Facebook, introduce ourselves, and make sure that we didn’t let anyone down. It was really creepy, but, in the end, people reacted positively and became regular users. The six principles described above are universal. They can help to build a reliable service on top of any other third party, and they’ve already helped us with Slack. Each product has different requirements so it's up to you which practices to implement.

How to build a Slack App more reliable then Slack

Authors:
(1) Zhaoqing Wang, The University of Sydney and AI2Robotics;
(2) Xiaobo Xia, The University of Sydney;
(3) Ziye Chen, The University of Melbourne;
(4) Xiao He, AI2Robotics;
(5) Yandong Guo, AI2Robotics;
(6) Mingming Gong, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence;
(7) Tongliang Liu, The University of Sydney. Table of Links Abstract and 1. Introduction 2. Related works 3. Method and 3.1. Problem definition 3.2. Baseline and 3.3. Uni-OVSeg framework 4. Experiments 4.1. Implementation details 4.2. Main results 4.3. Ablation study 5. Conclusion 6. Broader impacts and References A. Framework details B. Promptable segmentation C. Visualisation 4.3. Ablation study We conduct an extensive ablation study to demonstrate the contribution of each component of our framework. Mask-text alignment. Compared to the straightforward baseline, as shown in Tab. 3, our proposed Uni-OVSeg achieves significant gains of 4.8% PQ and 9.5% mIoU on the COCO dataset, and 11.2% mIoU on the PASCAL Context-59 dataset. This demonstrates our method effectively align objects in images and entities in text descriptions, generalising the CLIP embedding space from the image level to pixel level. By resorting to the refinement of text descriptions, new texts are more correlated with the corresponding images, improving the mIoU from 34.5% to 37.3% on the COCO dataset. Compared to the traditional NLP toolkit (NLTK) [3], ChatGPT-based parser extracts more reliable entities from text descriptions, which achieves obvious improvements of 3.1% and 3.7% mIoU on the COCO and PASCAL Context-59 datasets, respectively. Finally, the proposed multi-scale ensemble strategy that leverages the multi-scale information of objects within the images, stabilise the mask-text matching, which achieves a performance gain of 1.8% PQ on the COCO datasets. Multi-scale ensemble in mask-text matching. The quality of correspondence between masks and entities is an essential part of mask-text matching. To investigate the impact of multi-scale information on this correspondence, as illustrated in Tab. 4, we use masks and semantic classes from the ADE20K and COCO datasets, reporting the Top1 accuracy and forward time per sample. We first resize input images to multiple resolutions and extract visual features via the clip visual encoder. Given ground-truth masks, regional features are pooled from CLIP visual features and projected into the clip embedding space. Each regional embedding is classified by text embeddings. Taking into account the trade-off between performance and latency, we adopt the sizes of 869 × 896 and 1024 × 1024 as default. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Zhaoqing Wang, The University of Sydney and AI2Robotics; (2) Xiaobo Xia, The University of Sydney; (3) Ziye Chen, The University of Melbourne; (4) Xiao He, AI2Robotics; (5) Yandong Guo, AI2Robotics; (6) Mingming Gong, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence; (7) Tongliang Liu, The University of Sydney. Authors: Authors: (1) Zhaoqing Wang, The University of Sydney and AI2Robotics; (2) Xiaobo Xia, The University of Sydney; (3) Ziye Chen, The University of Melbourne; (4) Xiao He, AI2Robotics; (5) Yandong Guo, AI2Robotics; (6) Mingming Gong, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence; (7) Tongliang Liu, The University of Sydney. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Related works 2. Related works 3. Method and 3.1. Problem definition 3. Method and 3.1. Problem definition 3.2. Baseline and 3.3. Uni-OVSeg framework 3.2. Baseline and 3.3. Uni-OVSeg framework 4. Experiments 4.1. Implementation details 4.1. Implementation details 4.2. Main results 4.2. Main results 4.3. Ablation study 4.3. Ablation study 5. Conclusion 5. Conclusion 6. Broader impacts and References 6. Broader impacts and References A. Framework details A. Framework details B. Promptable segmentation B. Promptable segmentation C. Visualisation C. Visualisation 4.3. Ablation study We conduct an extensive ablation study to demonstrate the contribution of each component of our framework. Mask-text alignment. Compared to the straightforward baseline, as shown in Tab. 3, our proposed Uni-OVSeg achieves significant gains of 4.8% PQ and 9.5% mIoU on the COCO dataset, and 11.2% mIoU on the PASCAL Context-59 dataset. This demonstrates our method effectively align objects in images and entities in text descriptions, generalising the CLIP embedding space from the image level to pixel level. By resorting to the refinement of text descriptions, new texts are more correlated with the corresponding images, improving the mIoU from 34.5% to 37.3% on the COCO dataset. Compared to the traditional NLP toolkit (NLTK) [3], ChatGPT-based parser extracts more reliable entities from text descriptions, which achieves obvious improvements of 3.1% and 3.7% mIoU on the COCO and PASCAL Context-59 datasets, respectively. Finally, the proposed multi-scale ensemble strategy that leverages the multi-scale information of objects within the images, stabilise the mask-text matching, which achieves a performance gain of 1.8% PQ on the COCO datasets. Mask-text alignment. Multi-scale ensemble in mask-text matching. The quality of correspondence between masks and entities is an essential part of mask-text matching. To investigate the impact of multi-scale information on this correspondence, as illustrated in Tab. 4, we use masks and semantic classes from Multi-scale ensemble in mask-text matching. the ADE20K and COCO datasets, reporting the Top1 accuracy and forward time per sample. We first resize input images to multiple resolutions and extract visual features via the clip visual encoder. Given ground-truth masks, regional features are pooled from CLIP visual features and projected into the clip embedding space. Each regional embedding is classified by text embeddings. Taking into account the trade-off between performance and latency, we adopt the sizes of 869 × 896 and 1024 × 1024 as default. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

The Impact of Mask-Text Alignment and Multi-Scale Ensemble on Uni-OVSeg’s Segmentation Accuracy

To tackle the booming counterfeit market, Ennoventure Inc., an AI-driven brand protection leader, has raised $8.9 million in its Series A funding round, led by Tanglin Venture Partners. The funding, which also includes investment from Fenice Investment Group, will fuel Ennoventure’s ambitious expansion plans in the U.S., UAE, and India. This development comes at a critical time when industries across the globe are facing an unprecedented surge in counterfeit products and intellectual property theft. Ennoventure’s platform is built on a combination of AI and cryptography, allowing brands to embed an invisible digital signature within product packaging. This invisible signature is a game-changer for brands because it enables instant authentication without disrupting the manufacturing process or requiring additional capital investments. The company’s patented technology has already gained traction across sectors such as FMCG, automotive, and pharmaceuticals, where counterfeiting poses severe financial and reputational risks. By harnessing AI for real-time product authentication, Ennoventure’s solution is designed to protect both brands and consumers from fraudulent goods. “Ennoventure is addressing a critical need for effective, scalable brand protection with its innovative, AI-based approach,” said Ahmad Chatila, Chairman of Ennoventure. “This funding round empowers us to expand our footprint and enhance our product offerings to safeguard more brands globally.” The Rising Demand for Anti-Counterfeit Solutions The size of the global trade in counterfeit goods could reach $1.79 trillion by 2030. Industries worldwide are urgently seeking effective anti-counterfeit solutions, with Asia-Pacific emerging as a significant market for product authentication technologies. Ennoventure’s technology is poised to meet this demand with a platform that is both efficient and scalable for large enterprises. Sankalp Gupta, Partner at Tanglin Venture Partners, emphasized Ennoventure’s potential in addressing this market need. “Ennoventure’s AI-driven technology stands out for its seamless integration and scalability. We believe they’re well-positioned to redefine brand protection across multiple sectors.” How Ennoventure’s AI-Based Technology Works Ennoventure’s solution leverages AI and cryptography to create a digital “fingerprint” on product packaging. This invisible marker enables brands to authenticate products in real-time, protecting against counterfeit goods that threaten both revenue and customer trust. The AI system is process-agnostic, meaning it can be applied without changes to existing packaging lines –a critical feature for brands aiming to streamline operations without compromising security. The company’s technology has already proven effective in securing billions of product units, and its platform continues to evolve with advanced AI capabilities. The New Beginning With the Series A investment, Ennoventure plans to enhance its R&D capabilities and drive adoption across high-risk markets. The company’s expansion in the U.S., UAE, and India marks a strategic step forward as it positions itself as a leader in the anti-counterfeit industry. “We’re committed to building innovative solutions that empower brands to tackle counterfeiting,” said Padmakumar Nair, CEO and co-founder of Ennoventure. “With the support of our investors, we’re positioned to drive meaningful change in brand protection.” Key Takeaways Series A Funding Round: Ennoventure raises $8.9 million led by Tanglin Venture Partners, with contributions from Fenice Investment Group.
Innovative Technology: Uses AI and cryptography to create invisible digital markers for product authentication.
Global Expansion: Funding will support growth in key markets, including the U.S., UAE, and India, in industries where counterfeiting is a significant risk.
Industry Impact: Ennoventure’s solution provides real-time authentication, making it a sought-after technology for brands facing counterfeiting threats. As the global fight against counterfeiting intensifies, Ennoventure’s AI-powered platform is poised to play a pivotal role in safeguarding brand integrity and consumer safety. This latest funding round not only accelerates Ennoventure’s mission but also reinforces the company’s standing as a leader in AI-based anti-counterfeit solutions. To tackle the booming counterfeit market, Ennoventure Inc. , an AI-driven brand protection leader, has raised $8.9 million in its Series A funding round, led by Tanglin Venture Partners. The funding, which also includes investment from Fenice Investment Group, will fuel Ennoventure’s ambitious expansion plans in the U.S., UAE, and India. This development comes at a critical time when industries across the globe are facing an unprecedented surge in counterfeit products and intellectual property theft. Ennoventure Inc. Ennoventure’s platform is built on a combination of AI and cryptography, allowing brands to embed an invisible digital signature within product packaging. This invisible signature is a game-changer for brands because it enables instant authentication without disrupting the manufacturing process or requiring additional capital investments. The company’s patented technology has already gained traction across sectors such as FMCG, automotive, and pharmaceuticals, where counterfeiting poses severe financial and reputational risks. By harnessing AI for real-time product authentication, Ennoventure’s solution is designed to protect both brands and consumers from fraudulent goods. “Ennoventure is addressing a critical need for effective, scalable brand protection with its innovative, AI-based approach,” said Ahmad Chatila, Chairman of Ennoventure. “This funding round empowers us to expand our footprint and enhance our product offerings to safeguard more brands globally.” “Ennoventure is addressing a critical need for effective, scalable brand protection with its innovative, AI-based approach,” said Ahmad Chatila, Chairman of Ennoventure. “This funding round empowers us to expand our footprint and enhance our product offerings to safeguard more brands globally.” The Rising Demand for Anti-Counterfeit Solutions The size of the global trade in counterfeit goods could reach $1.79 trillion by 2030. Industries worldwide are urgently seeking effective anti-counterfeit solutions, with Asia-Pacific emerging as a significant market for product authentication technologies. Ennoventure’s technology is poised to meet this demand with a platform that is both efficient and scalable for large enterprises. Sankalp Gupta, Partner at Tanglin Venture Partners, emphasized Ennoventure’s potential in addressing this market need. “Ennoventure’s AI-driven technology stands out for its seamless integration and scalability. We believe they’re well-positioned to redefine brand protection across multiple sectors.” How Ennoventure’s AI-Based Technology Works Ennoventure’s solution leverages AI and cryptography to create a digital “fingerprint” on product packaging. This invisible marker enables brands to authenticate products in real-time, protecting against counterfeit goods that threaten both revenue and customer trust. The AI system is process-agnostic, meaning it can be applied without changes to existing packaging lines –a critical feature for brands aiming to streamline operations without compromising security. The company’s technology has already proven effective in securing billions of product units, and its platform continues to evolve with advanced AI capabilities. The New Beginning With the Series A investment, Ennoventure plans to enhance its R&D capabilities and drive adoption across high-risk markets. The company’s expansion in the U.S., UAE, and India marks a strategic step forward as it positions itself as a leader in the anti-counterfeit industry. “We’re committed to building innovative solutions that empower brands to tackle counterfeiting,” said Padmakumar Nair, CEO and co-founder of Ennoventure. “With the support of our investors, we’re positioned to drive meaningful change in brand protection.” “We’re committed to building innovative solutions that empower brands to tackle counterfeiting,” said Padmakumar Nair, CEO and co-founder of Ennoventure. “With the support of our investors, we’re positioned to drive meaningful change in brand protection.” Key Takeaways Series A Funding Round: Ennoventure raises $8.9 million led by Tanglin Venture Partners, with contributions from Fenice Investment Group. Innovative Technology: Uses AI and cryptography to create invisible digital markers for product authentication. Global Expansion: Funding will support growth in key markets, including the U.S., UAE, and India, in industries where counterfeiting is a significant risk. Industry Impact: Ennoventure’s solution provides real-time authentication, making it a sought-after technology for brands facing counterfeiting threats. Series A Funding Round: Ennoventure raises $8.9 million led by Tanglin Venture Partners, with contributions from Fenice Investment Group. Innovative Technology: Uses AI and cryptography to create invisible digital markers for product authentication. Global Expansion: Funding will support growth in key markets, including the U.S., UAE, and India, in industries where counterfeiting is a significant risk. Industry Impact: Ennoventure’s solution provides real-time authentication, making it a sought-after technology for brands facing counterfeiting threats. As the global fight against counterfeiting intensifies, Ennoventure’s AI-powered platform is poised to play a pivotal role in safeguarding brand integrity and consumer safety. This latest funding round not only accelerates Ennoventure’s mission but also reinforces the company’s standing as a leader in AI-based anti-counterfeit solutions.

Ennoventure Secures $8.9M in Series A to Scale AI-Powered Brand Protection Technology

Abstract: This article presents an advanced framework for processing petabyte-scale datasets using AWS Glue Interactive Sessions, custom libraries, and Amazon Athena. The framework addresses significant challenges in scalability, cost-efficiency, and performance for massive data volumes. Our results demonstrate substantial improvements in processing efficiency and cost reduction, with up to 60% decrease in processing costs and 40% improvement in processing times for compressed datasets. Introduction: The exponential growth of data across industries has created an urgent need for robust processing frameworks capable of handling petabyte-scale datasets. Traditional data processing methods often struggle with scalability, cost-efficiency, and performance when applied to such massive volumes of data. Moreover, the diversity of data formats and compression types introduces additional complexity to data processing pipelines. This article explores the development of a scalable data processing framework utilizing AWS Glue Interactive Sessions and custom libraries, supplemented by Amazon Athena for specific use cases. The framework aims to address the challenges associated with petabyte scale data processing, including efficient handling of various file compression formats, cost effective resource management, maintenance of processing speed of scale, and reliable processing across diverse data formats. Background Previous research has highlighted the limitations of traditional data processing systems in handling large-scale data efficiently. Cloud based solutions, particularly AWS services like Amazon Athena and AWS Glue are two services which provide big data processing and querying. However, there is a need for a cohesive framework that intelligently leverages these services based on data characteristics to optimized performance and cost. Methodology The proposed data processing framework integrates AWS Glue Interactive Sessions as the primary processing engine, with Amazon Athena utilized for specific compression scenarios. The architecture is designed to provide: Interactive Development Capabilities: Allowing data engineers to rapidly iterate and develop data processing skills.


Flexible Processing Options: Selecting the optimal processing engine based on data characteristics such as size and compression type.


Cost optimization: Dynamically allocating compute resources to balance cost and resources.


Scalability: Ensure the framework can handle petabyte scale datasets efficiently. Implementation Details Core Processing Class: The framework centers around a processing class that orchestrates workloads across AWS Glue and Amazon Athena. This class initializes a SparkSession specifically configured for AWS Glue, which is crucial for handling large scale data processing tasks. It also establishes connections to AWS services by creating clients for Athena- used for querying data-and Glue, which handles data processing operations. Intelligent Processing Selection: A standout feature of the framework is its ability to select the most appropriate processing engine based on the characteristics of the dataset, such as compression type and size. The selection criteria include: Gzip-Compressed Data: If the data is compressed with ‘gzip’ and surpasses a certain size threshold, the framework opts for Amazon Athena.
Bzip2-Compressed Data: Datasets compressed with ‘bzips2’ are always processed using Athena.
Snappy and Uncompressed Data: For data compressed with ‘snappy’ or uncompressed data, AWS Glue is typically the chosen engine. Cost Optimized Resource Allocation: To maintain a balance between performance and cost, the framework dynamically adjusts computing resources based on the size of the dataset. Larger datasets are allocated more powerful computing resources, while smaller ones use standard resources. This strategy involves setting the number of involves setting the number of workers and memory requirements in proportion to the data size, ensuring that resources are effectively matched to the workload. Performance Optimization Techniques: The framework incorporates several optimization strategies: Adaptive Query Execution:
i) Enables Spark to optimize queries dynamically based on the data being processed.
ii) Results in improved query performance.


Smart Partition Management:
i) Analysis data partitions to identify optimization opportunities.
ii) Adjusts partitions to enhance processing efficiency when beneficial. Monitoring and Observability: Effective monitoring is essential for maintaining high performance and quickly identifying any issues during processing. The framework integrates with AWS CloudWatch to track metrics related to processing efficiency, such as bytes processed per second. This integration provides valuable insights into the systems’ performance and aids in troubleshooting when necessary. Results: Implementation of the proposed framework has demonstrated significant benefits: Cost reduction: Up to 60% reduction in processing costs through intelligent selection of the processing engine and dynamic resource allocation.
Performance Improvement: Approximately 40% improvement in processing times for compressed datasets, particularly when using Amazon Athena for suitable scenarios.
Scalability: The framework efficiently handles petabyte-scale datasets, providing near real time processing capabilities.
Simplified Development Workflow: Interactive sessions and modular code design simplify the development and maintenance of data processing jobs. Discussion: The framework’s intelligent processing selection ensures that datasets are processed using the most efficient engine based on their characteristics. By leveraging AWS Glue for uncompressed or lightly compressed data and Amazon Athena for heavily compressed data or specific compression types (e.g., bzip2), the framework optimizes both performance and cost. Dynamic resource allocation aligns computing resources with workload demands, preventing over-provisioning and underutilization of resources. The adaptive query execution and smart partition management further enhance performance by optimizing data processing operations at runtime. Future Work: Several enhancements are proposed for future iterations of the framework: Integration with AWS Lake Formation: To provide fine grained access control and data governance capabilities.
Automated Testing Frameworks: Implementation of comprehensive testing to ensure reliability and robustness.
Support for Streaming Data Processing: Extending the framework to handle real-time data streams using technologies such as Apache Kafka and AWS Kinesis.
Complex Orchestration with AWS Step Functions: Integration for orchestrating complex workflows and handling dependencies between data processing tasks. Conclusion: This paper presents a robust and cost-effective solution for processing petabyte scale data by combining AWS Glue Interactive sessions, custom libraries, and Amazon Athena. The framework addresses the challenges of handling diverse data formats and compression types while maintaining optimal performance and cost efficiency. Organizations adopting this framework can expect significant improvements in processing efficiency, cost savings, and scalability. Abstract: Abstract: This article presents an advanced framework for processing petabyte-scale datasets using AWS Glue Interactive Sessions, custom libraries, and Amazon Athena. The framework addresses significant challenges in scalability, cost-efficiency, and performance for massive data volumes. Our results demonstrate substantial improvements in processing efficiency and cost reduction, with up to 60% decrease in processing costs and 40% improvement in processing times for compressed datasets. Introduction: The exponential growth of data across industries has created an urgent need for robust processing frameworks capable of handling petabyte-scale datasets. Traditional data processing methods often struggle with scalability, cost-efficiency, and performance when applied to such massive volumes of data. Moreover, the diversity of data formats and compression types introduces additional complexity to data processing pipelines. This article explores the development of a scalable data processing framework utilizing AWS Glue Interactive Sessions and custom libraries, supplemented by Amazon Athena for specific use cases. The framework aims to address the challenges associated with petabyte scale data processing, including efficient handling of various file compression formats, cost effective resource management, maintenance of processing speed of scale, and reliable processing across diverse data formats. Background Previous research has highlighted the limitations of traditional data processing systems in handling large-scale data efficiently. Cloud based solutions, particularly AWS services like Amazon Athena and AWS Glue are two services which provide big data processing and querying. However, there is a need for a cohesive framework that intelligently leverages these services based on data characteristics to optimized performance and cost. Methodology The proposed data processing framework integrates AWS Glue Interactive Sessions as the primary processing engine, with Amazon Athena utilized for specific compression scenarios. The architecture is designed to provide: Interactive Development Capabilities: Allowing data engineers to rapidly iterate and develop data processing skills. Flexible Processing Options: Selecting the optimal processing engine based on data characteristics such as size and compression type. Cost optimization: Dynamically allocating compute resources to balance cost and resources. Scalability: Ensure the framework can handle petabyte scale datasets efficiently. Interactive Development Capabilities: Allowing data engineers to rapidly iterate and develop data processing skills. Interactive Development Capabilities: Allowing data engineers to rapidly iterate and develop data processing skills. Flexible Processing Options: Selecting the optimal processing engine based on data characteristics such as size and compression type. Flexible Processing Options: Selecting the optimal processing engine based on data characteristics such as size and compression type. Cost optimization: Dynamically allocating compute resources to balance cost and resources. Cost optimization: Dynamically allocating compute resources to balance cost and resources. Scalability: Ensure the framework can handle petabyte scale datasets efficiently. Scalability: Ensure the framework can handle petabyte scale datasets efficiently. Implementation Details Core Processing Class: The framework centers around a processing class that orchestrates workloads across AWS Glue and Amazon Athena. This class initializes a SparkSession specifically configured for AWS Glue, which is crucial for handling large scale data processing tasks. It also establishes connections to AWS services by creating clients for Athena- used for querying data-and Glue, which handles data processing operations. Core Processing Class: Intelligent Processing Selection : A standout feature of the framework is its ability to select the most appropriate processing engine based on the characteristics of the dataset, such as compression type and size. The selection criteria include: Intelligent Processing Selection Gzip-Compressed Data: If the data is compressed with ‘gzip’ and surpasses a certain size threshold, the framework opts for Amazon Athena. Bzip2-Compressed Data: Datasets compressed with ‘bzips2’ are always processed using Athena. Snappy and Uncompressed Data: For data compressed with ‘snappy’ or uncompressed data, AWS Glue is typically the chosen engine. Gzip-Compressed Data: If the data is compressed with ‘gzip’ and surpasses a certain size threshold, the framework opts for Amazon Athena. Bzip2-Compressed Data: Datasets compressed with ‘bzips2’ are always processed using Athena. Snappy and Uncompressed Data: For data compressed with ‘snappy’ or uncompressed data, AWS Glue is typically the chosen engine. Cost Optimized Resource Allocation: Cost Optimized Resource Allocation: To maintain a balance between performance and cost, the framework dynamically adjusts computing resources based on the size of the dataset. Larger datasets are allocated more powerful computing resources, while smaller ones use standard resources. This strategy involves setting the number of involves setting the number of workers and memory requirements in proportion to the data size, ensuring that resources are effectively matched to the workload. Performance Optimization Techniques : Performance Optimization Techniques The framework incorporates several optimization strategies: Adaptive Query Execution:
i) Enables Spark to optimize queries dynamically based on the data being processed.
ii) Results in improved query performance. Smart Partition Management:
i) Analysis data partitions to identify optimization opportunities.
ii) Adjusts partitions to enhance processing efficiency when beneficial. Adaptive Query Execution: i) Enables Spark to optimize queries dynamically based on the data being processed. ii) Results in improved query performance. Adaptive Query Execution: i) Enables Spark to optimize queries dynamically based on the data being processed. ii) Results in improved query performance. Smart Partition Management: i) Analysis data partitions to identify optimization opportunities. ii) Adjusts partitions to enhance processing efficiency when beneficial. Smart Partition Management: i) Analysis data partitions to identify optimization opportunities. ii) Adjusts partitions to enhance processing efficiency when beneficial. Monitoring and Observability: Monitoring and Observability: Effective monitoring is essential for maintaining high performance and quickly identifying any issues during processing. The framework integrates with AWS CloudWatch to track metrics related to processing efficiency, such as bytes processed per second. This integration provides valuable insights into the systems’ performance and aids in troubleshooting when necessary. Results: Results: Implementation of the proposed framework has demonstrated significant benefits: Cost reduction: Up to 60% reduction in processing costs through intelligent selection of the processing engine and dynamic resource allocation. Performance Improvement: Approximately 40% improvement in processing times for compressed datasets, particularly when using Amazon Athena for suitable scenarios. Scalability: The framework efficiently handles petabyte-scale datasets, providing near real time processing capabilities. Simplified Development Workflow: Interactive sessions and modular code design simplify the development and maintenance of data processing jobs. Cost reduction: Up to 60% reduction in processing costs through intelligent selection of the processing engine and dynamic resource allocation. Performance Improvement: Approximately 40% improvement in processing times for compressed datasets, particularly when using Amazon Athena for suitable scenarios. Scalability: The framework efficiently handles petabyte-scale datasets, providing near real time processing capabilities. Simplified Development Workflow: Interactive sessions and modular code design simplify the development and maintenance of data processing jobs. Discussion: The framework’s intelligent processing selection ensures that datasets are processed using the most efficient engine based on their characteristics. By leveraging AWS Glue for uncompressed or lightly compressed data and Amazon Athena for heavily compressed data or specific compression types (e.g., bzip2), the framework optimizes both performance and cost. Dynamic resource allocation aligns computing resources with workload demands, preventing over-provisioning and underutilization of resources. The adaptive query execution and smart partition management further enhance performance by optimizing data processing operations at runtime. Future Work: Future Work: Several enhancements are proposed for future iterations of the framework: Integration with AWS Lake Formation: To provide fine grained access control and data governance capabilities. Automated Testing Frameworks: Implementation of comprehensive testing to ensure reliability and robustness. Support for Streaming Data Processing: Extending the framework to handle real-time data streams using technologies such as Apache Kafka and AWS Kinesis. Complex Orchestration with AWS Step Functions: Integration for orchestrating complex workflows and handling dependencies between data processing tasks. Integration with AWS Lake Formation: To provide fine grained access control and data governance capabilities. Automated Testing Frameworks: Implementation of comprehensive testing to ensure reliability and robustness. Support for Streaming Data Processing: Extending the framework to handle real-time data streams using technologies such as Apache Kafka and AWS Kinesis. Complex Orchestration with AWS Step Functions: Integration for orchestrating complex workflows and handling dependencies between data processing tasks. Conclusion: Conclusion: This paper presents a robust and cost-effective solution for processing petabyte scale data by combining AWS Glue Interactive sessions, custom libraries, and Amazon Athena. The framework addresses the challenges of handling diverse data formats and compression types while maintaining optimal performance and cost efficiency. Organizations adopting this framework can expect significant improvements in processing efficiency, cost savings, and scalability.

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