BURGER KING Germany: Serving Up Marketing Insights and Supply Chain Visibility Easily
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Do hamburgers really come from Hamburg? This may still be a matter of debate, but the popularity of American-style burger joints not just in Hamburg but all over Germany, is clear. Germany’s top two fast food companies are both burger chains. One of them is BURGER KING®, a global brand that welcomes more than 11 million customers worldwide every day. The company arrived in Germany in 1976, when its first restaurant opened in Berlin. It now operates more than 100 restaurants across Germany, with franchisees operating more than 600 restaurants of their own.
“In the fast food industry, being able to move quickly is very important. That means delivering the right promotion to our app or launching a viral campaign within days. To do that across more than 700 restaurants, we need the support of the right technology.”
—Oliver Mielentz, IT Manager, BURGER KING® Deutschland GmbH
Previously a subsidiary of the U.S. business, BURGER KING® Germany became an independent company in 2015. As a result, it needed to develop its own IT infrastructure, and the changeover needed to happen fast. “We had to put in place systems that would work for the entire network of franchisees and enable us to easily roll out campaigns,” explains Oliver Mielentz, IT Manager at BURGER KING® Deutschland GmbH.
With the help of Google Cloud Premier partner Cloudwürdig, BURGER KING® Germany chose Google Cloud and G Suite as the right combination to suit its needs.
“In the fast food industry, being able to move quickly is very important,” says Oliver. “That means delivering the right promotion to our app or launching a viral campaign within days. To do that across more than 700 restaurants, we need the support of the right technology.”
Building a franchisee platform in just three months
When a business has multiple franchisees, it’s important to make sure everyone is on the same page, especially in the fast-paced fast food environment. “We have to collate data from all our franchisees and produce reports quickly in order to react to changes in customer behavior,” explains Oliver. “That means processing every transaction that takes place in our restaurants.” Following the restructure, BURGER KING® Germany also needed to build a secure invoicing system with data storage and optimize its communication channels.
“Using Tableau with BigQuery, we’re able to produce reports very quickly. Previously, it took much longer, as data had to be fetched manually. Our reaction time is now almost a business day faster.”
—Oliver Mielentz, IT Manager, BURGER KING® Deutschland GmbH
With support from Witter-IT, BURGER KING® Germany chose Cloudwürdig to build its BKD Connect internal platform on Google Cloud. Thanks to the ready-to-go tools on Google Cloud, it was able to put its invoicing system and data warehouse in place in just three months.
For the BURGER KING® Germany data warehouse, Cloudwürdig built an ETL pipeline that channels ticket data for every sale into BigQuery. “Data is gathered from the restaurants,” says Oliver, “and using Tableau with BigQuery, we’re able to produce reports very quickly. Previously, it took much longer, as data had to be fetched manually. Our reaction time is now almost a business day faster.”
As the ticket data for every transaction is stored in BigQuery, the marketing team can easily see exactly which products are selling well. That’s crucial for tweaking promotions as well as monitoring the supply chain to make sure enough produce is delivered to restaurants in response to changes in demand.
“Thanks to BigQuery, we have a speedy data pipeline that enables us to react on the same day to changes in the market and eliminate bottlenecks in production,” says Oliver.
Switching to G Suite to improve communication
To enable franchisees to sign in to its BKD Connect Platform, BURGER KING® Germany needed a secure authentication system. To solve that problem, it chose to provide franchisees with G Suite accounts. “It’s really easy to set up a new franchisee on the platform. I just create a new G Suite account and Drive folder for it, and it’s ready to go,” says Oliver. G Suite also helps the franchise network to run efficiently, as daily reports are automatically saved to Drive and shared to the appropriate regional network. “Thanks to that system, it’s much easier for any team at headquarters to access the information it needs,” Oliver explains.
BURGER KING® Germany also recently extended its use of G Suite across the whole company. “Following an evaluation of our previous email and productivity software, I made the decision to switch solely to G Suite,” says Oliver. BURGER KING® Germany employees now use Gmail, Calendar, and Drive for their day-to-day productivity needs. “We only just completed the migration, but already, everyone’s happy,” says Oliver. “It’s so easy to share a file using Drive or set up a meeting on Calendar.”
“We’re big fans of Hangouts Meet, and we have two rooms here at our Hanover headquarters equipped with Hangouts Meet hardware,” Oliver adds. “The speech quality is good, and it’s helpful to be able to see every participant, especially when you’re running a meeting with multiple franchisees.”
Optimizing infrastructure to power innovative campaigns
The BURGER KING® app, available for iOS and Android, helps the company to deliver a great customer experience. Through their MyBK accounts, guests can access coupons and special promotions. “We had a really interesting campaign for Easter: guests used the app to hunt for virtual Easter eggs,” explains Oliver. “We knew it was going to be big, and our previous back end wouldn’t have been able to handle the traffic.”
To enable the marketing campaign to go ahead, BURGER KING® Germany moved the back end of the app, along with its website, to Google Cloud. For developing and running its web and app back ends, it now uses App Engine and virtual machines on Compute Engine, as well as Memorystore and Cloud Functions. For monitoring and logging, it uses Stackdriver, and Cloud CDN and Cloud DNS to easily handle its traffic.
“We ran the campaign without any performance issues, even though we were receiving several million hits a day,” says Oliver. Since migrating the back end to Google Cloud, the marketing team also launched the popular “Escape the Clown” campaign. “That campaign blew our minds!” says Oliver. “It wouldn’t have been possible without Google Cloud, because it required a lot of back end capacity.”
To develop the app infrastructure it needs, BURGER KING® Germany relies on Cloudwürdig. “Working with Cloudwürdig is great because the team has the same agile mindset as us,” says Oliver. “When we have a new idea, we just set up a meeting, and in a couple of days the new infrastructure is in place. For Escape the Clown, it only took a few weeks to get everything ready to launch.”
Leveraging integrated tools to grow the business
Using Google Cloud together with G Suite enables BURGER KING® Germany to run its franchise network efficiently, while keeping its IT team lean. “Google Cloud and G Suite are the perfect fit for the way of working at BURGER KING® Germany,” says Oliver. “Many of the company’s operatives are often on the road, visiting restaurants and franchisees. With these tools, they can work flexibly and react quickly to the situation on the ground.”
“In order to grow the business, we need to use the data we receive every day to understand exactly what is happening in our restaurants. With the tools provided by Google Cloud, we can get more guests through the door and offer them a better experience.”
—Oliver Mielentz, IT Manager, BURGER KING® Deutschland GmbH
It also helps to keep infrastructure costs under control. “With Google Cloud, we only pay for what we use, which is really important for us,” Oliver explains. “It means we can scale up quickly if we see an opportunity to react to a trend in customer behavior and launch a new marketing campaign that resonates with the moment. When it’s finished, we can then scale down again, and that definitely saves us money.”
BURGER KING® is now working with Cloudwürdig to add more functionality to the BURGER KING® app using Google Kubernetes Engine. “We like to work with customers long-term to support their digital transformation. BURGER KING® Germany is a great example of how one project can develop into a great collaboration,” says Benny Woletz, Managing Director of Cloudwürdig.
BURGER KING® also plans to expand its presence in Germany and gain a greater market share by further tailoring both its marketing and the way it runs its restaurants to answer its guests’ needs. “In order to grow the business, we need to use the data we receive every day to understand exactly what is happening in our restaurants,” says Oliver. “With the tools provided by Google Cloud, we can get more guests through the door and offer them a better experience.”
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How to Modernize Your Data infrastructure and Analytics: Case Study
Data modernization is key to digital transformation. Legacy systems are slow, expensive and cannot keep up with changing requirements. Data teams often spend more time on data pipelines than they do on data analysis or data science.
in this video, Harish Ramachandraiah, Director Eng. & Analytics, Sunrun, speaks with Joel Mckelvey, Product Marketing Manager, Google Cloud. They discuss how Sunrun leveraged Looker and BigQuery to reduce the complexity of legacy extract-transform-load processes, improve database performance, and adapt quickly to changes in data.
Now Sunrun makes data accessible where it’s needed, in near-real time.
Mckelvey shows how a modern data stack helps companies simplify the data pipeline, consolidate vital data, and accelerate analytics performance while improving agility, efficiency, and data governance.
Predictive Model Built on Google Cloud Helps You Get a 7-day Mosquito Forecast Report!
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Mosquitoes aren’t just the peskiest creatures on Earth; they infect more than 700 million people a year with dangerous diseases like Zika, Malaria, Dengue Fever, and Yellow Fever. Prevention is the best protection, and stopping mosquito bites before they happen is a critical step.
SC Johnson—a leading developer and manufacturer of pest control products, consumer packaged goods, and other professional products—has an outsized impact in reducing the transmission of mosquito-borne diseases. That’s why Google Cloud was honored to team up with one of the company’s leading pest control brands, OFF!®, to develop a new publicly available, predictive model of when and where mosquito populations are emerging nationwide.
As the planet warms and weather changes, OFF! noticed month-to-month and year-to-year fluctuations in consumer habits at a regional level, due to changes in mosquito populations. Because of these rapid changes, it’s difficult for people to know when to protect themselves. The OFF!Cast Mosquito Forecast™, built on Google Cloud and available today, will predict mosquito outbreaks across the United States, helping communities protect themselves from both the nuisance of mosquitoes and the dangers of mosquito-borne diseases—with the goal of expanding to other markets, like Brazil and Mexico, in the near future.
With the OFF!Cast Mosquito Forecast™, anyone can get their local mosquito prediction as easily as a daily weather update. Powered by Google Cloud’s geospatial and data analytics technologies, OFF!Cast Mosquito Forecast is the world’s first public technology platform that predicts and shares mosquito abundance information. By applying data that is informed by the science of mosquito biology, OFF!Cast accurately predicts mosquito behavior and mosquito populations in specific geographical locations.
Starting today, anyone can easily explore OFF!Cast on a desktop or mobile device and get their local seven-day mosquito forecast for any zip code in the continental United States. People can also sign up to receive a weekly forecast. To make this forecasting tool as helpful as possible, OFF! modeled its user interface after popular weather apps, a familiar frame of reference for consumers.
The technology behind the OFF!Cast Mosquito Forecast
To create this first-of-its-kind forecast, OFF! stood up a secure and production-scale Google Cloud Platform environment and tapped into Google Earth Engine, our cloud-based geospatial analysis platform that combines satellite imagery and geospatial data with powerful computing to help people and organizations understand how the planet is changing.
The OFF!Cast Mosquito Forecast is the result of multiple data sources coming together to provide consumers with an accurate view of mosquito activity. First, Google Earth Engine extracts billions of individual weather data points. Then, a scientific algorithm co-developed by the SC Johnson Center for Insect Science and Family Health and Climate Engine experts translates that weather data into relevant mosquito information. Finally, the collected information is put into the model and distilled into a color-coded, seven-day forecast of mosquito populations. The model is applied to the lifecycle of a mosquito, starting from when it lays eggs to when it could bite a human.
The SC Johnson Center for Insect Science and Family Health is one the world’s leading entomology research centers, studying advanced science of insect biology, insect-borne disease prevention and effective product solutions for consumer use. The science behind brands like OFF! is grounded in knowledge from world-class entomologists who have devoted their careers to SC Johnson’s mission of eradicating diseases like Malaria and Zika.
“We are putting the power in consumers’ hands in providing them with a tool to help predict their exposure and prevent mosquito bites,” said Maude Meier, SC Johnson entomologist. “It’s an exciting time to be working in the field of insect science as we find new opportunities to combine science and technology, like Google Earth Engine, to be a force for good in our mission to prevent the spread of insect-borne diseases.”
It takes an ecosystem to battle mosquitos
Over the past decade, academics, scientists and NGOs have used Google Earth Engine and its earth observation data to make meaningful progress on climate research, natural resource protection, carbon emissions reduction and other sustainability goals. It has made it possible for organizations to monitor global forest loss in near real-time and has helped more than 160 countries map and protect freshwater ecosystems. Google Earth Engine is now available in preview with Google Cloud for commercial use.
Our partner, Climate Engine, was a key player in helping make the OFF!Cast Mosquito Forecast a reality. Climate Engine is a scientist-led company that works with Google Cloud and our customers to accelerate and scale the use of Google Earth Engine, in addition to those of Google Cloud Storage and BigQuery, among other tools. With Climate Engine, OFF! integrated insect data from VectorBase, an organization that collects and counts mosquitoes and is funded by the U.S. National Institute of Allergy and Infectious Diseases.
The model powering the OFF!Cast Mosquito Forecast combines three inputs—knowledge of a mosquito’s lifecycle, detailed climate data inputs, and mosquito population counts from more than 5,000 locations provided by VectorBase. The model’s accuracy was validated against precise mosquito population data collected over six years from more than 33 million mosquitoes across 141 different species at more than 5,000 unique trapping locations.
A better understanding of entomology, especially things like degree days and how they affect mosquito populations, and helping communities take action is critically important to improving public health. Learn more about OFF!Cast Mosquito Forecast and see here to learn more about Google Earth Engine on Google Cloud.
Google Cloud Helps LiveRamp Capture, Manage, Process and Visualize Data at Scale
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Editor’s note: Today we’re hearing from Sagar Batchu, Director of Engineering at LiveRamp. He shares how Google Cloud helped LiveRamp modernize its data analytics infrastructure to simplify its operations, lower support and infrastructure costs and enable its customers to connect, control, and activate customer data safely and securely.
LiveRamp is a data connectivity platform that provides best in class identity resolution, activation and measurement for customer data so businesses can create a true customer 360 degree view. We run data engineering workloads at scale, often processing petabytes of customer data every day via LiveRamp Connect platform APIs.
As we integrated more internal and external APIs and the sophistication of our product offering grew, the complexity of our data pipelines increased. The status quo for building data pipelines very quickly became painful and cumbersome as these processes take time and knowledge of an increasingly complex data engineering stack. Pipelines became harder to maintain as the dependencies grew and the codebase became increasingly unruly.
Beginning last year, we set out to improve these processes and re-envision how we reduce time to value for data teams by thinking of our canonical ETL/LT analytics pipelines as a set of reusable components. We wanted teams to spend their time adding new features which encapsulate business value rather than spending time figuring out how to run workloads at scale on cloud infrastructure. This was even more pertinent with data science, data analyst and services teams whose daily wheelhouse was not the nitty gritty of deploying pipelines.
With all this in mind, we decided to start a data operations initiative, a concept popularised in the last few years, which aims to accelerate the time to value for data-oriented teams by allowing different personas in the data engineering lifecycle to focus on the “what” rather than the “how.”
We chose Google Cloud to execute on this initiative to speed up our transformation. Our architectural optimizations, coupled with Google Cloud’s platform capabilities simplified our operational model, reduced time to value, and greatly improved the portability of our data ecosystem for easy collaboration. Today, we have ten teams across LiveRamp running hundreds of workloads a day, and in the next quarter, we plan to scale to thousands.
Why LiveRamp Chose Google Cloud
Google Cloud provides all the necessary services in a serverless fashion to build complex data applications and run massive infrastructure. Google Cloud offers data analytics capabilities that help organizations like LiveRamp to easily capture, manage, process and visualize data at scale. Many of the Google Cloud data processing platforms also have open source roots making them extremely collaborative. One such platform is CDAP (Cask Data Application Platform), which Cloud Data Fusion is built on. We were drawn to this for the following reasons:
- CDAP is inherently multicloud. Pipeline building blocks known as Plugins define individual units of work. They can be run through different provisioners which implement managed cloud runtimes.
- The control plane is a set of microservices hosted on Kubernetes, whereas the data plane leverages the best of breed big data cloud products such as Dataproc.
- It is built as a framework and is inherently extensible, and decoupled from the underlying architecture. We can extend it both at the system and user-level through “extensions” and “plugins” respectively. For example, we were able to add a system extension for LiveRamp specific authorisation and build a plugin that encompasses common LiveRamp identity operations.
- It is open sourced, and there is a dedicated team at Google Cloud building and maintaining the core codebase as well as a growing suite of source, transform and sink connectors.
- It aligns with our remote execution and non-data movement strategy. CDAP executes pipelines remotely and manages through a stream of metadata via public cloud APIs.
- CDAP supports an SRE mindset by providing out of the box monitoring and observability tooling.
- It has a rich set of APIs backed by scalable microservices to provide ETL as a Service to other teams.
- Cloud Data Fusion, Google Cloud’s fully managed, native data integration platform is based on CDAP. We benefit from the managed security features of Data Fusion like IAM integration, customer manager encryption keys, role based access controls and data residency to ensure stricter governance requirements around data isolation.
How are teams using the Data Operations Platform?
Through this initiative, we have encouraged data science and engineering teams to focus on business logic and leave data integrations and infrastructure as separate concerns. A centralised team runs CDAP as a service, and custom plugins are hosted in a democratized plugin marketplace where any team can contribute their canonical operations.
Adoption of the platform was driven by one of our most common patterns of data pipelining: The need to resolve customer data using our Identity APIs. LiveRamp Identity APIs connect fragmented and inaccurate customer identity by providing a way to resolve PII to pseudonymous identifiers. This enables client brands to connect, control, and activate customer data safely and securely.
The reality of customer data is that it lives in a variety of formats, storage locations, and often needs bespoke cleanup. Before, technical services teams at LiveRamp had to develop expensive processes to manage these hygiene and validation processes even before the data was resolved to an identity. Over time, a combination of bash and python scripts and custom ETL pipelines became untenable.
By implementing our most used Identity APIs, a series of CDAP plugins, our customers were able to operationalise their processes by logging into a Low Code user interface, select a source of data, run standard validation and hygiene steps, visually inspect using CDAP’s Wrangler interface for especially noisy cases, and channel data into our Identity API. As these workflows became validated, they have been established as standard CDAP pipelines that can now be parameterized and distributed on the internal marketplace. These technical services teams have not only reduced their time to value but have also enabled future teams to leverage their customer pipelines without worrying about the portability to other team’s infrastructures.
What’s Next ?
With critical customer use cases now powered by CDAP, we plan on scaling out usage of the platform to the next batch of teams. We plan on taking on more complex pipelines, cross-team workloads, and adding support for the ever growing LiveRamp platform API suite.
In addition to the Google Cloud community and the external community, we have a growing base of LiveRamp developers building out plugins on CDAP to support routine transforms and APIs. These are used by other teams who push the limits and provide feedback — spinning a flywheel of collaboration between those who build and those who operate. Furthermore, teams internally can continue to use their other favorite data tools like BigQuery and Airflow as we continue to deeply integrate CDAP into our internal data engineering ecosystem.
Our data operations platform powered by CDAP is quickly becoming a center point for data teams – a place to ingest, hygiene, transform, and sink their data consistently.
We are excited by Google Cloud’s roadmap for CDAP and Data Fusion. Support for new execution engines, data sources and sinks, and new features like Datastream and Replication will mean LiveRamp teams can continue to trust that their applications will be able to interoperate with the ever evolving cloud data engineering ecosystem.
Google Cloud Tools Help U.S. Forest Department Generate Years of Insights into Earth’s Natural Resources
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For 117 years, the U.S. Department of Agriculture’s Forest Service has been a steward of America’s forests, grasslands, and waterways. It directly manages 193 million acres and supports sustainable management on a total of 500 million acres of private, state, and tribal lands. Its impact reaches far beyond even that, offering its research and learning freely to the world.
At Google, we’re big admirers of the Forest Service’s mission. So we were thrilled to learn in 2011 that its scientists were using Google Earth Engine, our planetary-scale platform for Earth Science data and analysis, to aid its research, understanding, and effectiveness. In the years since, Google has worked with the Forest Service to meet its unique requirements for visual information about the planet. Using both historical and current data, the Forest Service built new products, workflows, and tools that help more effectively and sustainably manage our natural resources. The Forest Service also uses Earth Engine and Google Cloud to study the effects of climate change, forest fires, insects and disease, helping them create new insights and strategies.
Besides gaining newfound depths of insight, the Forest Service has also sped up its research dramatically, enabling everyone to do more. Using Google Cloud and Earth Engine, the Forest Service reduced the time it took to analyze 10 years worth of land-cover changes from three months to just one hour, using just 100 lines of code. The agency built new models for coping with change, then mapped these changes over time, in its Landscape Change Monitoring System (LCMS) project.
Emergency responders can now work better on new threats that arise after wildfires, hurricanes, and other natural disasters. Forest health specialists can detect and monitor the impacts of invasive insects, diseases, and drought. More Forest Service personnel can use new tools and products within Earth Engine, thanks to numerous training and outreach sessions within the Forest Service.
Researchers elsewhere also benefited when the Forest Service created new toolkits, and posted them to GitHub for public use. For example, there’s geeViz, a repository of Google Earth Engine Python code modules useful for general data processing, analysis, and visualization.
This is only the start. Recently, the Forest Service started using Google Cloud’s processing and analysis tools for projects like California’s Wildfire and Forest Resilience Action Plan. Forest Service researchers also use Google Cloud to better understand ecological conditions across landscapes in projects like Fuelcast, which provides actionable intelligence for rangeland managers, fire specialists, and growers, and the Scenario Investment Planning Platform for modeling local and national land management scenarios.
The Forest Service is a pioneer in building technology to help us better understand and care for our planet. With more frequent imaging, rich satellite data sets, and sophisticated database and computation systems, we can view and model the Earth as a large-scale dynamic system.
We are honored and excited to respond to the unique set of requirements of the scientists, engineers, rangers, and firefighters of the USFS, and look forward to years of learning about — and better caring for — our most precious resources.
*Image 1: The USDA Forest Service (USFS) Geospatial Technology and Applications Center (GTAC) uses science-based remote sensing methods to characterize vegetation and soil condition after wildland fire events. The results are used to facilitate emergency assessments to support hazard mitigation, to inform post-fire restoration planning, and to support the monitoring of national fire policy effectiveness. GTAC currently conducts these mapping efforts using long-established geospatial workflows. However, GTAC has adapted its post-fire mapping and assessment workflows to work within Google Earth Engine (GEE) to accommodate the needs of other users in the USFS. The spatially and temporally comprehensive coverage of moderate resolution multispectral data sources (e.g., Landsat, Sentinel 2) and analytical power provided by GEE allows users to create geospatial burn severity products quickly and easily. Box 1 shows a pre-fire Sentinel-2 false color composite image. Box 2 shows a post-fire Sentinel-2 false color composite image with the fire scar apparent in reddish brown. Box 3 shows a differenced Normalized Burn Ratio (dNBR) image showing the change between the pre- and post-fire images in Boxes 1 and 2. Box 4 shows a thresholded dNBR image of the burned area with four classes of burn severity (unburned to high severity), which is the final output delivered to forest managers.
*Image 2: Leveraging Google Earth Engine (GEE), the USDA Forest Service (USFS) Geospatial Technology and Applications Center (GTAC) and USFS Region 8, developed the Tree Structure Damage Impact Predictive (TreeS-DIP) modeling approach to predict wind damage to trees resulting from large hurricane events and produce spatial products across the landscape. TreeS-DIP results become available within 48 hours following landfall of a large storm event to allow allocation of ground resources to the field for strategic planning and management. Boxes 1 and 3 above show TreeS-DIP modeled outputs with varying data inputs and parameters. Box 2 shows changes in greenness (Normalized Burn Ratio; NBR) that was measured with GEE during the recovery from Hurricane Ida and is shown as a visual comparison to the rapidly available products from TreeS-DIP.
*Image 3: Severe drought conditions across the American West prompted concern about the health and status of pinyon-juniper woodlands, a vast and unique ecosystem. In a cooperative project between the USDA Forest Service (USFS) Geospatial Technology and Applications Center (GTAC) and Forest Health Protection (FHP), Google Earth Engine (GEE) was used to map pinyon pine and juniper mortality across 10 Western US States. The outputs are now being used to plan for future work including on-the-ground efforts, high-resolution imagery acquisitions, aerial surveys, in-depth mortality modeling, and planning for 2022 field season work.
Box 1 contains remote sensing change detection outputs (in white) generated with GEE, showing pinyon-juniper decline across the Southwestern US. Box 2 shows NAIP imagery from 2017 with, with box 3 showing NAIP imagery from 2021. NAIP imagery from these years shows trees changing from healthy and green in 2017 to brown and dying in 2021. In addition, box 2 and box 3 show change detection outputs from Box 1 for a location outside of Flagstaff, AZ converted to polygons (in white). The polygon in box 2 is displayed as a dashed line to serve as a reference, while the solid line in box 3 shows the measured change in 2021. Converting rasters to polygons allows the data to be easily used on tablet computers, as well as the ability to add information and photographs from field visits.
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Explore the Innovations and Architecture Powering Spanner and BigQuery
Previously, databases had architectures with tightly coupled storage and compute. This resulted in higher latency, and with faster networks these constraints no longer surface. With Google Cloud’s BigQuery and CloudSpanner, the storage and compute architecture have been separated, allowing for better scalability and availability to address businesses’ high throughput data needs.
Watch the video to understand how these database and analysis products leverage Google’s distributed storage system, in-house custom network hardware and software, internal cluster management system and more!
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