aws unilever case study

Unilever: An Amazon Web Services Case Study, Repost

About Unilever

At first, Unilever  was formed in 1930 by the merger of Dutch margarine producer Margarine Unie and British soap maker Lever Brothers. Today, the consumer goods giant sells food, home care, refreshments, and personal care products in over 190 countries. Unilever has headquarters in London, United Kingdom, and Rotterdam, the Netherlands, and subsidiaries in over 90 countries employing more than 170,000 people. Previously, in 2012, Unilever reported more than €51 billion in revenue.

The Challenge

To begin with, Unilever North America in Englewood Cliffs, New Jersey, needed to re-design its infrastructure to support Unilever’s digital marketing approach.

Previously, Unilever used on-premises data centers to host its web properties with different technologies and processes. “We needed to standardize our environment to support a faster time-to-market,” says Sreenivas Yalamanchili, Digital Marketing Services (DMS) Global Technical Manager. Simultaneously, Unilever optimizes its business model by testing a marketing campaign in a pilot country. If the drive is successful, the company deploys it to other countries and regions. The IT organization wanted to use  the cloud  to implement the same process.

Why Amazon Web Services

After a comprehensive RFP and review process involving more than 16 companies, Unilever chose Amazon Web Services (AWS). Unilever’s priorities in selecting a digital marketing platform included flexibility, a global infrastructure, technology, and a rich ecosystem of members.

“With AWS, we have the same hosting provider for all regions, which means we don’t have to customize and tweak hosting solutions per region,” says Yalamanchili. “Unilever is focused on delivering great brands to consumers; it’s not an IT shop. We can spend less and get more innovation by working with AWS and members of the AWS Partner Network.”

In general, the Unilever IT team had two goals for the AWS migration. The first is to deliver a standard website technology platform with regional content delivery architecture. Secondly, to migrate existing web properties to the cloud.

To begin with, to develop the platform, Unilever attended an AWS workshop to design the architecture. Then the DMS team built a pilot platform (a disaster recovery site for third-party hosting in Miami) for stakeholder review. After obtaining business approval, Unilever chose CSS Corporation, an Advanced Consulting Partner member of the Amazon Partner Network (APN), for system integration and application development.

The DMS team worked with CSS to develop a global content management system (CMS). The CMS platform lets agencies build brand websites globally and publish them across several AWS regions. Later, Unilever used a HAProxy load balancer to improve the performance of its websites and runs its databases on Microsoft SQL Server and MySQL.

Disaster Recovery

For disaster recovery, Unilever stores backup data, snapshots, product, and recipe media files in  Amazon Simple Storage Service (Amazon S3) and uses   EBS Snapshot Copy  to copy  Amazon Elastic Block Store  (Amazon EBS) snapshots from the US East (Northern Virginia) Region to the US West (Northern California) Region. “We designed a disaster recovery solution to protect our content management system, content deployment architecture, and many GOLD-classified web properties—and to give the business confidence in the AWS Cloud,” says Yalamanchili. Certainly, figure 1 demonstrates Unilever’s architecture on AWS.

Unilever and CSS created  Amazon Machine Images (AMIs) running Windows and Linux on approximately 400 Amazon Elastic Compute Cloud ( Amazon EC2 ) instances.  Amazon Virtual Private Cloud  (Amazon VPC) provides flexibility for deployments and access to the Internet. Nick Morgan, Enterprise Architect for Digital Marketing, comments, “What’s nice about using AWS is how easily we can scale the instances based on the nature of a campaign’s popularity.

We used  Auto Scaling and manual scaling for sites such as Recipedia.com and Axeapollo.com. We can deploy instances across different AWS Regions and Availability Zones and use Amazon EBS snapshots to bring services back.”

To migrate its web properties to the cloud, Unilever built pre-production and production environments on AWS for several existing websites. After that, Unilever’s creative and production agencies certified the website in the pre-production environment. Then Unilever switched the DNS address for the production environment to go live on AWS.

After a successful pilot launch, Unilever migrated over 500 web properties from its data centers to AWS in less than five months. Since then, Unilever has over 1,700 web properties running on AWS worldwide. “Throughout our business globally, we strive to create repeatable models, and it’s easy to standardize our hosting environment with AWS,” says Yalamanchili. “If a marketing campaign that we deploy in the US East (Northern Virginia) Region is successful, we can easily replicate it to Asia Pacific (Singapore) Region for the APAC countries.”

To clarify, CSS supplied Unilever with machine images of different operating systems, APIs, and tools to automate the launching of a new project. “The way CSS automated launching instances reduced the time to launch a project by about 75 percent,” says Morgan. “What used to take four days now only takes one day. We’re not constantly rebuilding web and database servers from the ground up. We can clone and re-use images.”

The Benefits

Specifically, for Unilever, moving to the AWS Cloud improved business agility and operational efficiency. “Previously, requesting a website for a marketing campaign was a lengthy process,” says Yalamanchili. “By using AWS, we improved the time to launch a digital marketing campaign from two weeks to an average of two days. That’s more than seven times faster than our traditional environment. If a brand manager has an idea, they can implement it before the competition,”

“Using AWS saves us time,” he continues. “I can simply go to the AWS website and plug in numbers to calculate costs. That makes it easy for me to set up a standard website billing model. It takes our partner, CSS, less than 12 hours to calculate pricing for a campaign website. I can comfortably say to my marketing folks that we have the capacity for anything we want to do. We can focus on innovation rather than infrastructure.”

“The other advantage is the responsiveness of the AWS Cloud,” says Yalamanchili. “Using AWS, one of the brand managers could completely alter a campaign within 24 hours, which wouldn’t happen with the physical infrastructure.”

“AWS listens to us and helps come up with ideas to do things differently that are beneficial,” says Morgan. “I enjoy the rapid rate of innovation from AWS.” Yalamanchili adds, “With AWS, it’s the customer’s way, always. As shown above, AWS has proved that the customer matters by listening to us and innovating products and services.”

To learn more about how AWS can help you run websites, visit our Marketing Websites page:  http://aws.amazon.com/websites/ .

Also, for more information about how CSS Corporation can help your company run on the AWS Cloud, see  CSS Corporation’s listing  in the  AWS Partner Directory .

Furthermore, the original article can be found here, as well as other case studies: Unilever AirBnB Conde Nast Robinhood Siemens iRobot Brooks Brothers

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How Blueair (Unilever brand) went from idea to product on market within a year, using AWS IoT

Broadcast Date: June 16, 2022

To achieve the global scalability necessary to support a growing number of connected devices around the world, Blueair rapidly developed and deployed its HealthProtect product line using an IoT system on Amazon Web Services (AWS). Building on AWS enables enhanced security and privacy for Blueair’s customers and supports releases of new and upgraded features so that every air purifier continues to deliver value throughout its life span. “Freedom to breathe clean, healthy air as nature intended is our promise to consumers,” says Johan Alvenberg, Head of IoT at Blueair. “By proactively monitoring the air quality and ensuring 24/7 protection against harmful pollutants, we are doing just that. For the tech savvy we have also added smart, premium usability and functions.”

Learning Objectives

• Hear how Blueair (Unilever brand) adapted and scaled globally their connected IoT devices and transformed their business within a year
• Dive into the use case to understand Blueair (Unilever brand) focus on improving Connected devices and enriching customers life with smart features
• Identify how solutions can be deployed to realize business value for Connected IoT devices

Who Should Attend?

Business decision makers including line of business owners and managers, operations and production leaders, Technical decision makers in Engineering, Technology, Product, Architecture, IT professionals , Developer,IT Leader, IoT developer, IoT entrepreneur, IoT Business User, IoT decision makers.

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How Technology Makes Continuous Innovation Possible: A Case Study with Unilever

Imagine a world where all silos of supply chain function work in unison on the same platform, where most decisions are made automatically considering competing objectives of various functions and results are optimized using cost versus service trade-offs, and the partnership model allows for continuous evolution of the model, user interface and analytics as business needs change. This world isn’t a distant dream; it’s the reality crafted by the synergy between Unilever , a global consumer goods giant, and Solvoyo , the norm-busting partner in end-to-end supply chain planning.

Watch the partnership achievements here.

In the corridors of Unilever, a team of dedicated supply chain planners from demand to supply to transportation embarks on a daily journey empowered by Solvoyo’s platform. Their day doesn’t begin with traditional routines but with diving deep into a digital universe where data alerts serve as guiding stars. With the E2E exception-base autonomous planning, the system automates decisions from demand forecasts, production plans, and order fulfillment strategies to delivery with minimal need for manual intervention. 

This is not eliminating the planners entirely but focusing their attention where it’s best. Planners are empowered with more free time to focus on more value-adding strategic and tactical decisions by evaluating Key Performance Indicators (KPIs) dashboards, and when their attention is required, the system will alert them automatically. 

The Three Core Assets of Unilever & Solvoyo Project

At the core of this transformative tale lie three indispensable assets: Digital, the foundation of the platform providing a single source of truth for the entire organization; Intelligent, the infusion of AI and machine learning, transforming raw data into actionable insights, empowering Unilever to predict market trends, anticipate demands, and stay ahead of the ever-shifting market landscape; and Autonomous, the pinnacle where end-to-end supply chain decisions are made seamlessly, free from human touch.

End-to-End Supply Chain Planning Platform

The end-to-end process begins with data. With automatic data diagnostics, Solvoyo Digital Platform ensures the quality of the data it receives, fixes the inconsistencies where it can, and alerts the planners in case their attention is required. The result is an end-to-end planning process operating on the highest quality data possible. 

Then, Demand Planning , driven by AI, predicts market fluctuations by analyzing demand drivers, including weather, market dynamics, and new products, providing forecasts that minimize the need for overrides and provide insights to enable data-driven decisions. Supply Planning crafts production plans that optimize available capacity and resources, not just optimal but synchronized with operational constraints, ensuring the right product mix at the right time and cost. 

Customer Service Fulfillment becomes an art of efficiency, with real-time data optimizing replenishment, allocation, and truckload fill. The system allocates stock based on various parameters, enhancing service and volume maximization, enabling 100% autonomous planning!

Transportation Planning streamlines the intricate web of logistics by ensuring efficient truck planning, reducing fulfillment time and the carbon footprint. With such a level of sophistication and precision delivered by automated recommendations, 90% of them are executed as is, and planners focus only on exceptions. 

Achievements 

Through this transformation, Unilever has achieved multiple milestones.

Centralized Demand Planning became the cornerstone that streamlined operations, leading to not just efficiency but an alignment across functions across the entire organization. Advanced Analytics provides the Unilever team with automatic insights, enabling Unilever to gaze into the future with clarity, foreseeing market trends, and preparing for every twist and turn in the market landscape. 

Real-time Visibility gives the team a bird’s-eye-view of the entirety of the operations, allowing for faster decision-making, erasing data silos, and fostering collaboration among teams previously isolated in their individual silos. End-to-end synchronized Planning ensured every cog in the machinery moved in perfect unison, creating a workflow so seamless that it felt like a choreographed dance. 

Finally, for the cherry on top, Unilever adopted Goal Programming. The proprietary mathematical engine balances the total cost to serve and service levels. It allows the company to set multiple objectives, model constraints simultaneously as opposed to a pre-selected order, and generate an optimal plan that considers all objectives. This process prevents the functions from working against each other and considers the impact of every decision on the whole chain. 

Continuous Innovation Is the Core of Successful Partnerships

What sets this story apart is not just the technology but the support of the dedicated customer success team of Solvoyo that propels it forward. Ultimately, if the environment for continuous improvement and proactive innovation is not present, even the best of technologies lose relevance in today’s dynamic setting. 

The key here is high user acceptance rates, streamlined workflows, reduced manual intervention, and enabling Unilever to achieve the holy grail of supply chain management: exception-based, no-touch planning. It’s a testament to the power of collaboration, innovation, and unwavering dedication to efficiency. It’s not just about machines and algorithms; it’s about the people who understand the soul of the supply chain, who breathe life into every line of code, transforming it from mere instructions into a symphony of efficiency.

End-to-end decision automation is not a distant vision for giants like Unilever but also for mid-market companies.   You, too can make innovation a way of life, not just a buzzword 🌟 

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How Unilever Uses AI & Digital Solutions in its Operations

Every day, 3.4 billion people around the world use Unilever products. The consumer goods leader has been innovating for over 100 years – from re-inventing soap in the late 19th century to pioneering sustainable living today.

And as consumer expectations of our products and brands continue to evolve, Unilever is working to meet these demands, against a backdrop of supply chain disruption, cost inflation and the impact of an escalating climate crisis.

Today, its experts are keeping Unilever at the forefront of innovation by applying the latest digital technologies to help create better experiences for customers. From using AI solutions to make the shopping experience more accessible to trialling blockchain tech to drive sustainability ambitions, we highlight how Unilever is using technology solutions in its operations.

Unilever making significant investments in developing AI solutions

Like many businesses globally, Unilever has been harnessing the power of AI to drive innovation. In November 2023 it announced it had established its first AI Horizon3 Lab in Canada, with Andy Hill , Chief Data Officer of Unilever, saying Unilever was “making a significant investment in developing and deploying AI technology across the business”. 

“The launch of Unilever’s first AI lab will expedite the progress we are making globally and enable us to have the right focus on some of the big questions we want to answer with AI,” he said.

Unilever has also recently partnered with the accessibility app specialist Be My Eyes , the developer of accessibility solutions apps that connects people who are blind or low-vision to a world network of volunteers and AI-powered virtual assistance, to expand its use of AI technology, adding powerful new tools to the cooking experience of their products for shoppers who are blind or have low vision.

Unilever is working with Be My Eyes to train its volunteers and provide specific product information and data to the Chat GPT-4 AI bot. Unilever’s customer support team will also be integrated into the Be My Eyes app experience, allowing shoppers the option to be automatically connected to the Colman’s careline team for further support if needed.

“We’ve accelerated digitising our packs to offer new opportunities for brand engagement and elevated shopping experience and now we’re also focusing on how we can also use digital experiences to make our products more accessible,” said Rachana Dongre , Senior Digital Engagement and Strategy Lead Nutrition & Ice Cream at Unilever. “Zappar’s AQR codes means we can support blind and low vision shoppers to have equal access to information and integrating Be My Eyes into these codes offers a totally new way to make the full experience of our products more inclusive, from the shopping aisle right through to cooking at home.”

In April 2023, Unilever announced that, with Accenture and Microsoft , it had completed one of the largest and most complex cloud migrations in the consumer goods industry, helping Unilever become a cloud-only enterprise .

With Azure as its primary cloud platform, the transformation means Unilever can accelerate product launches, enhance customer service and improve operational efficiency, while helping Unilever meet its sustainability commitments.

“Unilever is a truly data-powered organisation,” commented Steve McCrystal , Chief Enterprise and Technology Officer of Unilever. We’re using advanced analytics to make better-informed decisions quicker than ever before. Working with Accenture and Microsoft on this global transformation project, we can respond to ever-changing consumer needs faster, allocate our resources more effectively to focus on what drives growth, and bring services and products to the market faster.”

Technology helping drive sustainability ambitions

As Unilever describes, sustainability is one of its fundamental principles. Its ambitious targets are powered by its research and development teams globally.

This R&D is powering Unilever’s move away from petro-chemicals, stopping plastic pollution and fixing the broken food system, in collaboration with its extensive network of partners.

Unilever’s goal is to make a Clean Future, which is plans to achieve by replacing 100% of the carbon derived from fossil fuels in its Home Care products with renewable or recycled carbon.

In one initiative, in2018 Unilever ice cream brand Ben & Jerry’s trialled a carbon offsetting scheme using blockchain technology , as part of an effort to reduce its carbon footprint.

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The amazing ways how unilever uses artificial intelligence to recruit & train thousands of employees.

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It’s hard to live a day in the developed world without using a Unilever product. The multinational manufactures and distributes over 400 consumer goods brands covering food and beverages, domestic cleaning products and personal hygiene.

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With so many processes to coordinate and manage, artificial intelligence is quickly becoming essential for organizations of its scale. This applies to both research and development as well as the huge support infrastructure needed for a business with 170,000 employees.

Recently it announced that it had developed machine learning algorithms capable of sniffing your armpit and telling you whether you are suffering from body odors. While this may seem like "using a sledgehammer to crack a walnut," the technology which has been developed could well go on to be used to monitor food for freshness, helping to solve the problem of food overproduction and waste endemic in society .

As well as these smart, public-facing initiatives, though, artificial intelligence is being put to use behind the scenes to help screen and assess the more than one million people per year who apply for jobs with Unilever. If they make the grade and become one of the thousands who are offered a job, they have AI-powered tools to help them adjust to their new role and hit the ground running.

AI-enhanced recruiting

Unilever recruits more than 30,000 people a year and processes around 1.8 million job applications.

This takes a tremendous amount of time and resources. As a multinational brand operating in 190 countries, applicants are based all around the world. Finding the right people is an essential ingredient for success, and Unilever can't afford to overlook talent just because it is buried at the bottom of a pile of CVs.

To tackle this problem, Unilever partnered with Pymetrics, a specialist in AI recruitment, to create an online platform, which means candidates can be initially assessed from their own homes, in front of a computer or mobile phone screen.

First, they are asked to play a selection of games that test their aptitude, logic, and reasoning, and appetite for risk. Machine learning algorithms are then used to assess their suitability for whatever role they have applied for, by matching their profiles against those of previously successful employees.

The second stage of the process involves submitting a video interview. Again, the assessor is not a human being but a machine learning algorithm. The algorithm examines the videos of candidates who answering questions for around 30 minutes, and through a mixture of natural language processing and body language analysis, determines who is likely to be a good fit.

Unilever's chief of HR, Leena Nair, told me that around 70,000 person-hours of interviewing and assessing candidates had been cut, thanks to the automated screening system.

She said, "We look for people with a sense of purpose – systemic thinking, resilience, business acumen. Based on that profile, the games and the video interview are all programmed to look for cues in their behavior that will help us understand who will fit in at Unilever."

Referring to the video interview analytics for their future leaders program, she tells me: “Every screenshot gives us many data points about the person, so we work with a number of partners and use a lot of proprietary technology with those partners, and then we select 3,500 or so people to go through to our discovery center.” After spending a day with real leaders and recruiters, Unilever selects about 800 people who will be offered a job.

The system is also designed to give feedback to all applicants, even those who aren’t successful.

“What I like about the process is that each and every person who applies to us gets some feedback,” Nair says.

“Normally when people send an application to a large company it can go into a ‘black hole’ – thank you very much for your CV, we’ll get back to you – and you never hear from them again.

“All of our applicants get a couple of pages of feedback, how they did in the game, how they did in the video interviews, what characteristics they have that fit, and if they don’t fit, the reason why they didn’t, and what we think they should do to be successful in a future application.

“It’s an example of artificial intelligence allowing us to be more human.”

So while Unilever isn’t quite ready to hand the entire recruitment process over to machines just yet, it has shown that it can assist with the initial “sift” when it comes to preliminary screening of applicants.

Robots to help you settle into the job

After making the grade, another machine-learning-driven initiative is helping new employees get started in their new roles – adapting to the day-to-day routines as well as the corporate culture at the business.

Unabot is a natural language processing (NLP) bot built on Microsoft’s Bot framework, designed to understand what employees need to know and fetch information for them when it is asked.

“We joke about the fact we don’t know whether it’s a man or a woman – it’s Unabot,” Nair tells me.

“Unabot doesn’t only answer HR questions, questions about anything that affects employees should be answered by Unabot, and it is now the front face for any employee question – they might ask it about IT systems, or about their allowances – so we are learning about what matters to employees in real time.”

Through interacting with employees, Unabot has learned to answer questions such as where parking is available, the timing of shuttle buses, and when annual salary reviews are due to take place.

Unlike, for example, Alexa or consumer-facing, customer-service corporate chatbots, Unabot must also be able to filter and apply information based on who it is speaking to. It is capable of differentiating the information it passes on based on both the user's geographical location and their level of seniority within the company.

Unabot was first rolled out for employees based in the Philippines and is now operating in 36 countries. It has been selected as the next AI initiative that will be rolled out globally in all of Unilever’s 190 markets.

“It’s a new way of working,” Nair tells me, “We never go in and say it's perfect so let’s roll it out in all countries,’ we learn what we can in one country and roll it out in the next one.”

Currently, all of its data comes from internal sources, such as company guidelines, schedules, policy documents and questions asked by the employees themselves. In the future, this could be expanded to include external data such as learning materials.

And although it’s early days, the initial analysis seems to show that the initiative is popular with staff – with 36% of those in areas where it is deployed having used it at least once, and around 80% going on to use it again.

One lesson learned early on was that the importance of providing a frictionless experience.

“So we’ve learned that you have to make anything that interacts with employees or consumers effortless,” Nair says.

“People interact in different ways – a policy document is written in a particular way, its three or four pages of what an employee shouldn’t do. But an employee tends to ask questions in very simplistic ways – how does this impact my life, where will I find this, what can I do?”

Machine learning – particularly NLP – can overcome this due to its ability to detect which questions are repeatedly asked, even if they are asked in different ways, and present the right information.

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10 Surprising AWS Use Cases You Didn’t Know About

Amazon Web Services (AWS) is one of the most popular cloud computing platforms on the market today, used by millions of businesses worldwide. While many are familiar with the standard use cases of AWS, such as website hosting and data storage, there are countless other uses for this powerful platform that you may not have considered.

In this article, we’ll explore ten surprising AWS use cases that you probably didn’t know about. From creating smart homes to analyzing satellite imagery, AWS has many applications that can benefit businesses and individuals alike. So, whether you’re an AWS expert or just getting started, read on to discover how this versatile platform can help you achieve your goals unexpectedly.

What is AWS?

AWS is a cloud computing platform that provides a wide range of services, such as computing power, storage, and databases. It is a comprehensive and flexible platform that enables businesses of all sizes to leverage the power of cloud computing. AWS has been designed to be highly scalable, secure, and reliable. It is designed to allow businesses to scale their resources up or down as needed without worrying about the underlying infrastructure.

AWS Use Case #1: Airbnb

Airbnb is a platform that allows people to rent out their homes or apartments to travelers. It is one of the most popular travel platforms worldwide, with millions of users. Airbnb uses Amazon S3 and Amazon EC2 to store and serve images and data. They also use Amazon RDS for their database needs. By leveraging AWS, Airbnb can scale its resources up or down as needed, which helps them save money and be more efficient.

AWS Use Case #2: Netflix

Netflix is a popular streaming service that provides users with access to thousands of movies and TV shows. They use AWS to power their streaming service. Netflix uses Amazon EC2 to host their streaming service, Amazon S3 to store its video content, and Amazon CloudFront to deliver its content to users worldwide. By using AWS, Netflix is able to provide a reliable and scalable streaming service to its users.

AWS Use Case #3: Unilever

Unilever is a multinational consumer goods company that produces a wide range of products such as food, beverages, cleaning agents, and personal care products. They use AWS to power their digital transformation efforts. Unilever uses AWS to host their websites, manage its content, and analyze its data. By using AWS, Unilever is able to improve its customer engagement, reduce costs, and drive innovation.

AWS Use Case #4: NASA

NASA is a government agency that is responsible for space exploration and aeronautics research. They use AWS to analyze satellite imagery. NASA uses Amazon EC2 to run its image analysis software, Amazon S3 to store its images, and Amazon CloudFront to deliver its images to users around the world. By using AWS, NASA is able to analyze satellite imagery more efficiently and effectively.

AWS Use Case #5: Adobe

Adobe is a software company that is well known for its creative software, such as Photoshop, Illustrator, and InDesign. They use AWS to power their Creative Cloud service. Adobe uses Amazon S3 to store and serve its assets, Amazon EC2 to host their web applications, and Amazon CloudFront to deliver its content to users worldwide. By using AWS, Adobe is able to provide a reliable and scalable service to its users.

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AWS Use Case #6: Pfizer

Pfizer is a pharmaceutical company that produces a wide range of products, such as vaccines, medications, and consumer health products. They use AWS to power their digital transformation efforts. Pfizer uses AWS to host websites, manage content, and analyze data. By using AWS, Pfizer is able to improve their customer engagement, reduce costs, and drive innovation.

AWS Use Case #7: Dow Jones

Dow Jones is a media company that provides business and financial news to users around the world. They use AWS to power their content delivery network (CDN) . Dow Jones uses Amazon CloudFront to deliver their content to users worldwide. By using AWS, Dow Jones is able to provide a reliable and scalable service to their users.

AWS Use Case #8: Capital One

Capital One is a financial services company that provides a wide range of products, such as credit cards, loans, and savings accounts. They use AWS to power their digital transformation efforts. Capital One uses AWS to host websites, manage content, and analyze data. By using AWS, Capital One is able to improve its customer engagement, reduce costs, and drive innovation.

AWS Use Case #9: Kellogg’s

Kellogg’s is a multinational food company that produces a wide range of products such as cereal, snacks, and frozen foods. They use AWS to power their digital transformation efforts. Kellogg’s uses AWS to host websites, manage content, and analyze data. By using AWS, Kellogg’s is able to improve their customer engagement, reduce costs, and drive innovation.

AWS Use Case #10: Expedia

Expedia is a travel platform that provides users with access to flights, hotels, and rental cars. They use AWS to power their digital transformation efforts. Expedia uses AWS to host their websites, manage their content, and analyze their data. By using AWS, Expedia is able to improve its customer engagement, reduce costs, and drive innovation.

In conclusion, AWS is a versatile platform that can be used in many surprising ways. From analyzing satellite imagery to powering streaming services, AWS has a wide range of applications that can benefit businesses and individuals alike. By using AWS, businesses can improve their customer engagement, reduce costs, and drive innovation. So, whether you’re an AWS expert or just getting started, consider the many surprising ways that you can use this powerful platform to achieve your goals.

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Technical Case Study: Enhancing Batch Processing Workflows for UCL Spin-Outs with ARC's Expertise

UCL Advanced Research Computing collaborated with startups Chronostics and StoreGene to create an open-source Terraform module for efficient AWS batch-processing pipelines.

6 June 2024

As part of their journey in the UCL CDI Impact Accelerator, startups chronostics and StoreGene  collaborated with Research Software Engineers (RSEs) from UCL Advanced Research Computing to enhance their technical capabilities. ARC developers provided crucial support, helping these startups to develop and implement advanced infrastructure solutions. A key result of this was the development by ARC RSEs of an open-source Terraform module for deploying a batch-processing pipeline on AWS. This innovation allows users to quickly spin up infrastructure to handle a common processing pattern - upload data to S3, trigger containerised processing pipelines, then store the results back into S3. The module, named terraform-aws-batch-processing, is designed using the AWS Well Architected framework, ensuring efficiency in both cost and computational resources. 

Technical Overview: Streamlined Serverless Architecture 

The module, terraform-aws-batch-processing, implements a serverless architecture that uses best practices set out in the AWS Well Architected framework, resulting in an efficient implementation from a cost and computational perspective. The high-level architecture is shown below. 

First, a user uploads an object to S3. This sends an upload event notification to AWS Lambda, which triggers a Lambda function to initiate an AWS Step Functions workflow. AWS Step Functions is a service that can be used to orchestrate workflows involving other AWS services. In this instance, the workflow consists of three steps in serial: 

Data Synchronization: Use AWS DataSync to sync data between the 'Upload' S3 bucket and EFS. 

Batch Processing: Submit a job to an AWS Batch queue to a run a containerised processing pipeline on Fargate. There is an EFS mountpoint on Fargate so the containers can access the necessary data. 

Results Storage: Use AWS DataSync to sync data between EFS and a 'Reports' S3 bucket. 

The Terraform module optimises for cost and performance, but not at the expense of the other Well Architected Pillars. For instance, security in the cloud is a priority for everyone these days. In this respect, terraform-aws-batch-processing defines IAM roles that have only the necessary capabilities for each part of the workflow, following the principle of least privilege. The infrastructure is also automatically replicated in private subnets across multiple Availability Zones, providing regional resilience and improving reliability. 

Application in Startups: chronostics and StoreGene add batch processing capabilities to improve patient outcomes 

The terraform-aws-batch-processing was developed during Cohort 3 of the Impact Accelerator in collaboration with two start-ups from the cohort - chronostics and StoreGene. 

chronostics: Enhancing Clinical Trial Efficiency 

chronotics is a UCL spin-out using data-driven disease progression modelling to improve clinical trials and patient outcomes. Their platform aids clinical researchers recruit the most appropriate patients, streamline the clinical trial process, and in the future will ensure that patients receive the most effective treatment for their needs. 

Challenge: Before collaborating with ARC, chronostics performed all processing locally on an ad-hoc basis, limiting their scalability and responsiveness. 

Solution: As part of Cohort 3 of the Impact Accelerator, chronostics worked with ARC to integrate the terraform-aws-batch-processing module into their platform. This transformation allowed them to scale their processing operations and respond to processing requests more effectively.  

Outcome: The enhanced batch processing capabilities have enabled chronostics to respond to processing requests more effectively, providing a better service to their customers and enabling the startup to support a larger number of clinical trials efficiently. 

StoreGene: Advancing Personalized Healthcare 

StoreGene is another UCL spin-out, providing personalised healthcare based on a patient's whole genome. Their platform helps clinicians to interpret, understand, and make decisions based on the vast amounts of data in their patients' genomes. Given that around 50% of diseases are heritable, this personalised approach to medicine based on whole genome analysis has the potential to improve a person's health at every stage of their life - from birth to old age. 

Challenge: StoreGene needed to improve their data processing pipelines for whole genome analysis and streamline the process of running bioinformatics pipelines upon data upload. 

Solution: During Cohort 3 of the Impact Accelerator, StoreGene collaborated with ARC to integrate the terraform-aws-batch-processing module into their platform. This automation allows them to run bioinformatics pipelines when a clinician uploads a patient's genome data to the site. Additionally, they integrated AWS QuickSight into their platform to present the final reports to clinicians. Together, this provides a streamlined means for clinicians to get actionable insight and provide patients with the most effective care based on their genome. 

Outcome: This integration provided StoreGene with a seamless and efficient way to process and analyze genomic data, offering clinicians actionable insights to deliver personalized care to patients. 

A Leap Forward in Digital Innovation 

The collaboration between ARC's Research Software Engineers and the UCL spin-outs chronostics and StoreGene, facilitated by the UCL CDI Impact Accelerator, highlights the significant impact of advanced batch processing workflows. By leveraging the terraform-aws-batch-processing module, both startups have enhanced their platforms, improved their service delivery, and positioned themselves for greater scalability and success in their respective fields. Through this partnership, ARC has demonstrated its vital role in driving technological innovation and supporting the growth of cutting-edge healthcare solutions. 

Further info 

The terraform-aws-batch-processing module is permissively licensed and available on GitHub here .

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