CARTO’s Data Visualization Powered by Google Cloud and deck.g
4087
Of your peers have already read this article.
7:00 Minutes
The most insightful time you'll spend today!
Editor’s note: Today’s blog post is from Alberto Asuero, CTO of CARTO, the location intelligence platform. Today he shares more details about the source of the data for advanced data visualizations created with Google Maps Platform and deck.gl and how the CARTO platform enables this workflow.
During Google Cloud Next in October, the Google team announced the newest release of the deck.gl visualization library, thanks to a collaboration with our geospatial company CARTO and the vis.gl Technical Steering Committee (TSC). The deck.gl release includes a deep integration with the new WebGL-powered features in the Maps JavaScript API that allows deck.gl to render 2D and 3D visualizations directly on the Google basemap.
Our team built an example app that visualizes a variety of data sources that show the potential for electrification of truck fleets in Texas. This app showcases the different types of advanced data visualizations that can be created with Google Maps Platform and deck.gl. Today, I want to share more details about the source of the data for these visualizations and how the CARTO platform enables this workflow.
Google Cloud provides a strong serverless data warehousing solution, BigQuery, with support for geospatial queries. When you are dealing with spatial data, creating maps to explore and visualize these datasets is an important and common need. The CARTO Spatial Extension for BigQuery provides an easy way to create connections to the data warehouse, design a map with data coming from BigQuery tables, and then add these visualizations to a web app using deck.gl.
Making a simple map
To create a simple map using the CARTO platform, you can sign up for a trial account. Once you have signed in, you can set up a connection to your BigQuery instance using a service account. Then, you can go to the Data Explorer and browse the available datasets to find the table you want to use as the datasource in your map. For more information, check out the CARTO documentation.
To create a visualization of power transmission lines in Texas, you can start with the Texas state boundary to provide some context. In the Data Explorer, you can preview the table and click the “Create map” button in the top-right corner to start designing your visualization.
Using the CARTO Builder map making tool, select one of the available Google vector basemap styles and customize the layer style.
You can visualize tables and the results from queries executed in the data warehouse, which is a powerful feature because you can also execute spatial analysis functions using SQL, including those from the CARTO Analytics Toolbox. In this case, you can intersect the lines in the table containing all the U.S. transmission lines within the Texas boundary. Click on the “Add source from…” button and select the “Custom Query (SQL)” option to add the following query:
SELECT *FROM cartobq.nexus_demo.transmission_linesWHERE ST_INTERSECTS(geometry,(SELECT geom FROM cartobq.nexus_demo.texas_boundary_simplified));
Click the “Run” button, and the query is executed in BigQuery. The results are sent back to the Builder tool. Perform some style customizations in the new layer, and your map is ready.
Before adding the map to the Google Maps Platform application, you’ll need to make it public. Click on the “Share” button and select the “Developers” tab to copy the map ID.
Now, you can add the visualization into your Google Maps Platform application, which is as easy as adding these four lines of code:
const cartoMapId = 'b502bf53-877d-4e89-b5ad-71982cac431d'; deck.carto.fetchMap({cartoMapId}).then(({layers}) => { const overlay = new deck.GoogleMapsOverlay({layers}); overlay.setMap(map); });
You can use the map ID copied from CARTO Builder to call the fetchMap function. This function connects to the platform and retrieves all the information needed for the visualization, including a collection of deck.gl layers with all the styling properties you’ve specified. Create an instance of the deck.gl GoogleMapsOverlay with this collection of layers and add it to the map.
You can see the full example in this fiddle.
Visualizing very large datasets
One of the main features of BigQuery is the ability to scale processing to massive datasets. With the CARTO platform, you can also visualize very large datasets using tilesets, an optimized data structure containing pre-generated vector tiles for fast visualization. Tilesets are generated within BigQuery using the Analytics Toolbox functions in a parallelized process that can handle billions of points.
For example, you can create a visualization using tilesets with the whole dataset of transmission lines for the U.S., more than 100MB of geometries.
The issue with these large datasets is that they do not fit in memory all at once, so you need to split them into tiles for them to be rendered progressively. CARTO takes care of this, allowing you to create tilesets directly in BigQuery or dynamically generate them on the fly.
This method for data loading in maps can scale as much as needed; for example, take a look at this 17 billion point visualization of vessel data.
What about live data?
BigQuery supports streaming data that is continuously updated. In these scenarios, you want to be able to update your visualization at regular intervals, as the data changes. It’s easy to update this visualization using deck.gl. You just need to set the autoRefresh parameter to true when fetching the map and specify the function you want to execute when new data is downloaded:
const {layers} = await deck.carto.fetchMap({ cartoMapId, autoRefresh: true, onNewData: (parsedMap) => { … } });
You can add points to a table with an INSERT function on the BigQuery console and see the data updated on the map in real time.
Going further
In addition to the simple ways to create visualizations shown above, deck.gl has the flexibility to create a wide variety of visualizations. The CARTO platform provides you with the functionality to access data from your data warehouse and create these data visualizations with advanced cartographic capabilities, but you can extend it and go beyond that using any of the advanced visualizations available in the deck.gl layer catalog.
There are two additional options that give you more control over the deck.gl code. The first one is to use the CartoLayer directly without fetchMap. You’ll need to indicate the connection to use from the CARTO platform and the data source type and name or query. Then we can specify the styling properties.
const overlay = new deck.GoogleMapsOverlay({layers: [new deck.carto.CartoLayer({connection: 'bqconn',type: deck.carto.MAP_TYPES.TABLE,data: `cartobq.public_account.retail_stores`,getFillColor: [238, 77, 90],pointRadiusMinPixels: 6,}),],});
The second option is to use the fetchLayerData function that allows you to have more control over the format used for data transfer between BigQuery and your application and can be used with advanced visualizations that require an specific data format like ArcLayer, H3HexagonLayer or TripsLayer.
deck.carto.fetchLayerData({type: deck.carto.MAP_TYPES.TABLE,source: `cartobq.geo_for_good_meetup.texas_pop_h3`,connection: 'bqconn',format: deck.carto.FORMATS.JSON,credentials: {accessToken: 'eyJhbGciOiJIUzI1NiJ9.eyJhIjoiYWNfbHFlM3p3Z3UiLCJqdGkiOiI1YjI0OWE2ZCJ9.Y7zB30NJFzq5fPv8W5nkoH5lPXFWQP0uywDtqUg8y8c'}}).then(({data}) => {const layers= [new deck.H3HexagonLayer({id: 'h3-hexagon-layer',data,extruded: true,getHexagon: d => d.h3,getFillColor: [182, 0, 119, 150],getElevation: d => d.pop,elevationScale: 2.5,parameters: {blendFunc: [luma.GL.SRC_ALPHA, luma.GL.DST_ALPHA],blendEquation: luma.GL.FUNC_ADD}})];const overlay = new deck.GoogleMapsOverlay({layers});overlay.setMap(map);});
For complete code using both options, take a look at these examples.
Learn more
You can access demos and documentation on the deck.gl docs website and the CARTO Documentation Center. If you have questions, you can ping the CARTO team on the CARTO Users Slack workspace.
For more information on Google Maps Platform, visit the Google Maps Platform website.
Cloud-Native Observability: Google Cloud and Chronosphere Join Forces
1350
Of your peers have already read this article.
2:30 Minutes
The most insightful time you'll spend today!
As digital transformation continues apace, cloud native adoption has skyrocketed — and so has the volume, velocity, and variety of data that organizations must monitor to maintain visibility over their IT environments. Rather than a fixed number of virtual machines (VMs) running a single application each, systems are more flexible and ephemeral — with thousands of containers and microservices emitting data — and services and systems have greater interdependencies. All of this has led to an explosion in complexity that makes it nearly impossible to reliably and efficiently operate cloud-native services using traditional observability solutions without dramatically increasing overhead.
Built on Google Kubernetes Engine (GKE), Chronosphere is the only cloud-native observability solution that helps teams quickly resolve incidents before they affect the customer experience and the bottom line. And the relationship between Google Cloud and Chronosphere has developed into a growing partnership. Chronosphere brings the ability to rapidly and reliably control observability data and costs to Google Cloud customers, while maintaining open source compatibility. Recently, GV (Google Ventures) participated in Chronosphere’s $115 million round of Series C funding. Chronosphere is also available on Google Cloud Marketplace, making it easy for customers to procure and deploy.
Why Chronosphere chose to build on Google Cloud
Chronosphere’s founders, CEO, Martin Mao, and CTO, Rob Skillington, wanted to build an observability solution that solved some of the same problems they encountered when leading the observability team at Uber. The challenges at Uber focused on scale, reliability and cost, with a key emphasis on how these were not easily solvable for monitoring of cloud-native applications. Martin and Rob were looking to build a solution that was cloud-native, highly reliable, and capable of managing the high volume of data that modern organizations process every day. For those reasons, Martin and Rob chose to build Chronosphere on top of Google Kubernetes Engine (GKE):
Cloud-native mindset: When Martin and Rob launched Chronosphere, one of the first considerations was this: A cloud-native problem needs a cloud-native solution. Google Cloud and Chronosphere work together to provide a purpose-built software-as-a-service (SaaS) observability solution platform for cloud-native technologies like Kubernetes. Chronosphere runs much of its critical infrastructure on Google Cloud, using the service’s global infrastructure to deliver secure and reliable services to hypergrowth customers.
Open platform: Martin and Rob have a commitment to openness — a commitment that Google Cloud makes to its customers as well. All of the ins and outs of Chronosphere are open source compatible –built on GKE, the platform ingests both metric and trace data in a variety of open source formats, such as Prometheus, OpenTelemetry, and StatsD, so teams can get onboard easily while avoiding vendor lock-in. And they remain fully in control of how much data to ingest, how it’s ingested, how it’s stored, and for how long. That way, customers remain in control of their observability costs while their systems and data continue to grow, and can troubleshoot and remediate issues more quickly.
Mission-critical performance: Observability is a mission-critical service because it is essential for the reliability and performance of mission-critical systems. For this reason, Martin and Rob wanted to offer the strongest service level agreements (SLAs) possible for availability, reliability, and performance. Google Cloud provides the secure global infrastructure that allows them to do so.
Data volume: Modern organizations emit huge volumes of data that they have to sift through in near-real time to gain full visibility. Google Cloud offers the same infrastructure and networks that Google uses to support billions of users every day, capable of processing petabytes of data with ease.
How Chronosphere works with Google Cloud
GKE provides specific technical benefits that Chronosphere leverages to offer customers the performance and availability they need to spend less time managing their observability systems and more time creating value.
The Chronosphere collector is a Kubernetes-native, Prometheus-compatible agent that collects, processes, and exports telemetry data, including metrics and traces. When you install the collector in a Kubernetes cluster, it automatically discovers all your workloads and starts to gather data from them.
The Chronosphere UI tailors the experience to each user based on what services that individual owns or which teams that person belongs to. This makes the overall user experience friendlier and less overwhelming while helping people get to the bottom of issues faster.
Running on GKE means that Chronosphere can leverage Google Cloud’s multiple zones in each geographic region to distribute workloads evenly, ensure customer data stores are isolated from each other, and build tolerance for zonal failures. It also allows for data persistence across multiple zones. At the same time, Google Cloud’s global load balancers bring the data physically closer to the customer, shortening the time it takes for a data point to be visible to a user after its emission.
What this means for customers
The partnership brings together the best in cloud-native services and cloud-native observability. With the power of Google Cloud and Chronosphere, observability teams can transform their observability data based on need, context, and utility, storing only the useful data to reduce costs and improve performance. With purpose-built solutions for a cloud-native world, teams gain faster issue detection and resolution, and also benefit from Chronosphere’s 99.99% availability and open source compatibility — eliminating vendor lock-in.
“Companies growing their online infrastructure risk huge hits to their bottom line if they don’t also manage increased complexity and cost,” says Martin. “Our partnership with Google Cloud brings together the world’s leading cloud services platform with our powerful observability solution to unlock the benefits of a cloud-native world, while optimizing for efficiency, reliability, and cost.”
Google Cloud and Chronosphere: The perfect match
Google Cloud and Chronosphere offer an industry-leading observability solution built from the ground up to abstract away the complexities of cloud computing for already stressed teams. And the future of this partnership is sure to bring new innovations around performance, reliability, and cost efficiency as both companies continue to invest in these three areas. The goal: to deliver the best performance for cost in the industry.
Learn more about what Chronosphere and Google Cloud are doing for customers, and check out the Chronosphere listing on Google Cloud Marketplace.
How Machine Learning in G Suite Helps Employees of Dalmia Bharat Discover the Next Big Idea
DOWNLOAD CASE STUDY5537
Of your peers have already downloaded this article
5:30 Minutes
The most insightful time you'll spend today!
Creating expense reports, Email management, formatting documents: Your time is caught in the quicksand of formatting, tracking, analysis or other mundane tasks. These are just some of the time-sinks that can affect your—and your employees’—productivity at work. At Google Cloud, this is referred to as “overhead”—time spent working on tasks that do not directly relate to creative output—and it happens a lot.
According to a Google Cloud study, the average worker spends only about 5 percent of his or her time actually coming up with the next big idea.
That’s where machine learning can help.
Machine learning algorithms observe examples and make predictions based on data. In G Suite—Google Cloud’s collaboration and productivity platform—machine learning models make your workday more efficient by taking over menial tasks, like scheduling meetings, or by predicting information you might need and surfacing it for you.
“Smart Reply, (in Gmail) for example, uses machine learning to generate three natural language responses to an email. So if you find yourself on the road or pressed for time and in need of a quick way to clear your inbox, let Smart Reply do it for you,” says Sunil Tewari, Head of Technology and Business Services, Dalmia Bharat.
Ease Your Migration and Modernization Journey with Microsoft and Windows on Google Cloud Demo Center
8293
Of your peers have already read this article.
2:00 Minutes
The most insightful time you'll spend today!
If you’re looking to migrate and modernize your Microsoft and Windows workloads, Google Cloud is your premiere destination. No matter what migration strategy you’ve selected or what value you’re looking to achieve, with Google Cloud you’re able to:
- simplify your migration and modernization journey
- reduce your on-prem footprint and increase agility
- optimize license usage to reduce costs
- modernize to reduce single-vendor dependencies
- rely on enterprise-class support backed by Microsoft
Whether you’re looking to migrate applications running on Windows virtual machines, adopt Windows containers in Google Kubernetes Engine (GKE), convert SQL databases to Cloud SQL, or something else, Google Cloud offers you the first-class experience you need.
But we don’t want you to take our word for it. Try it out yourself with our new online Microsoft and Windows on Google Cloud Demo Center without any commitment or friction.
The demo center uses hands-on guided simulations to walk you through several scenarios for solving business critical challenges with Google Cloud’s Microsoft and Windows solutions. Because these are all simulated, you’ll see how it works without any deployment, configuration, or commitment. It’s a seamless way for you to see exactly how Google Cloud can help you.
Run dedicated hardware and optimize with sole tenant
Sometimes you might want to run your workloads on dedicated hardware (with oversubscription options) for compliance, licensing, and management. Google Cloud provides sole-tenant nodes that allow you to easily deploy your virtual machines onto dedicated machines to avoid “noisy neighbor” issues, address regulatory or licensing constraints, and optimize inter-VM communications.
Plus, the CPU Overcommit option allows oversubscribing sole-tenant node resources by up to 2x, therefore helping save on per-physical core licensing for many licensed workloads like SQL Server.
Learn how to set up a sole tenant group and node.
Optimize license costs with premium images & custom VMs
One of the easiest ways to optimize your cloud experience with virtual machines (VM) is to pick the right VM image. Google Cloud provides premium license-included VM images that are thoroughly tested and optimized, including SQL Server options with pay-as-you-go licensing. These are great for workloads that don’t need to run all the time or when you do not have spare licenses for bring your own license (BYOL).
Explore some Windows & SQL images and learn how CPU/Memory options can help optimize deployment and save on licensing.
Modernize your databases with Managed SQL Server
Sometimes you need to manage your SQL Server instance to achieve certain business or operational goals. But more often, managing SQL Server deployments can be undifferentiated: backups, high-availability, updates, and patching are just some of the many things you have to take care of when going the do-it-yourself route. One way to modernize your database tier is to migrate to a managed service like Cloud SQL, which is a fully managed Relational Database service for SQL.
Explore the process of creating an instance in just a few clicks!
Extract apps from VMs and move to containers in GKE with Migrate for Anthos
Many Windows workloads running on virtual machines such as Internet Information Services (IIS) are ideal candidates for migrating to containers without major changes like rewriting or rearchitecting. However, doing this migration manually can be tedious, which is why Migrate for Anthos can help easily re-platform a .NET app running on IIS into a container-based app.
Simulate intelligently extracting, migrating, and modernizing applications to run natively on containers in GKE and Anthos clusters.
Move .NET applications to GKE on Windows without code changes
When you’re looking to go fully cloud native, you can leverage Windows containers in GKE without rewriting your .NET applications. Simply create clusters with Windows nodes and deploy containerized Windows workloads in a few clicks, even alongside Linux containers. These deployments reduce operational overhead with features such as auto-upgrade, auto-repair, and release channels.
Learn how easy it is to build a GKE cluster with a Windows node and deploy an app.
Now that you’ve gotten a feel for what’s available, go check out the Demo Center. You can also visit us at Windows and Microsoft on Google Cloud to learn more.
Iroquois Healthcare Association Leverages Chrome OS to Keep Staff, Patients and Families Remotely Connected
3973
Of your peers have already read this article.
3:00 Minutes
The most insightful time you'll spend today!
Editor’s note: Today’s post is by Eileen Murphy, Senior Director of Special Projects, and Andrew Nault, Director of Business Development, both from Iroquois Healthcare Association Workforce Investment Organization (IHA WIO). The health association represents 44 hospitals in 32 counties in New York State, providing advocacy, cost-saving initiatives, and business solutions. IHA WIO has provided its participating long-term care organizations with more than 5,000 Chromebooks to support 80 organizations in several ways, including training staff, connecting nursing home residents to their families, and helping administrative staff work safely from home.
Since early 2020, clinicians and staff at IHA WIO participant organizations have had to adapt to constant change—everything from implementing pandemic safety protocols to helping nursing-home residents stay in touch with families. We’re proud of the wonderful work they’ve done and continue to do.
During the pandemic, our use of Chrome OS and Chrome devices was expanded and changed to meet the needs of organizations dealing with an unprecedented event. Chrome devices were used to not only train staff but to enable remote work, as well as to connect residents to their friends and families during long periods of isolation. In the morning, a Chromebook could be in a managed guest session mode, helping a grandmother virtually attend her grandkids’ birthday party; at lunch, a nursing assistant could log in to her latest continuing education classes on that same Chromebook.
That evening, the Chrome OS devices could be found at check-in desks, helping visitors fill out a COVID-19 survey. All it took to change a device’s purpose were a few clicks in the Google Admin console, all of which could be done remotely.
Chrome OS for training
Most of our 5,000 Chrome OS devices came from a New York State grant to provide continued education training for long-term care organizations and its employees. We wanted to offer online classes, which meant we’d have to give people the technology to access that training. Our partner, CDW, recommended Chrome OS for us because it’s an operating system built for the cloud. In addition, devices running Chrome OS are easy to use, easily shared, and cost-effective.
Best of all, Chromebook Enterprise came with all the enterprise capabilities of Chrome OS unlocked. This allowed us to use the Google Admin console to manage our entire Chromebook Enterprise fleet remotely. We were able to set up one high-level organizational unit across IHA, with almost 80 organizational units for facilities and groups.
To support training, Lenovo helped us create a five-minute video that we added to our HealthStream learning platform so staff could learn the basics of using Chromebooks. This video was created because we didn’t realize there were people who have never touched a laptop before. The video and Chrome OS ease of use made it possible to get the folks who needed the most help up to speed quickly.
Chrome OS for connecting residents to loved ones
Once the pandemic began and people could no longer visit nursing home residents, the staff for one of our facilities asked about using a training Chromebook for video calls between residents and families. We loved this idea as it was still in line with our mission: to enable the workforce to provide higher-quality care.
With the Admin console, the process was easy. We changed the managed guest session to auto load, and changed what is populated on startup. With help from healthcare staff, residents were able to choose from several video apps including Google Meet, Zoom, and Facebook.
Chrome OS for remote workers
When office and administrative staff had to work remotely, Chromebooks again came to the rescue. Since staff could access all their work applications via Citrix—like Outlook, office phone systems, and other public documents—we placed the remote-work laptops in managed guest session mode and loaded the Citrix app at startup. All the employees had to do was connect to Wi-Fi and log into Citrix, and they were ready to work.
With our training program still running and virtual remote ability on the rise, we realized we would need more Chromebooks. CDW informed us that Chrome OS device supply was running low, but they located 1,000 Chromebooks for us and sent them to our home office in Clifton Park, New York.
Normally, the job of configuring and shipping 1,000 laptops would strike fear into the hearts of the IT team. Not this time: With zero-touch enrollment, all of the Chromebooks were enrolled in our high-level organizational unit before the boxes arrived at our Clifton Park office. We created a list of Chromebook serial numbers for each IHA location getting a shipment, then packed up the boxes. We never had to open a Chromebook box, or even turn the devices on before preparing them for use by employees.
Chrome OS for telehealth and clinic kiosks
There were more Chromebook transformations to come. In the interest of keeping residents and medical professionals safe, we enabled Chromebooks for non-urgent telehealth sessions. Doctors and nurses could see and talk to nursing home residents, using the same video conferencing apps that residents used to talk to loved ones. In these cases, we had PointClickCare, our electronic medical records app, loaded at startup on doctors’ Chromebooks.
We even turned a few Chromebooks into guest kiosks at IHA member hospitals that have walk-in clinics. If the clinics required visitors to fill out a COVID-19 screening form, the Chromebook, in kiosk mode, was stationed at the front desk. Once visitors submitted the form, a new blank form appeared, ready for the next visitor without any data stored on the device.
Chrome OS for whatever’s needed next
What’s amazing is that we could help people in all these different ways in just a few minutes—without visiting a nursing home, getting someone to ship laptops back to us, or loading software. Chromebooks are so versatile that sometimes they’re used in various modes several times a day. One user can log in, while another user can use managed guest sessions to login as a guest user. No matter what changes come to our healthcare association in the future, we’ll be ready.
4911
Of your peers have already watched this video.
9:37 Minutes
The most insightful time you'll spend today!
L’Oréal: Managing Big-data Complexity with Google Cloud
L’Oreal is a global company with a presence in 150 countries worldwide. Between managing all of its brands and requirements for different countries, L’Oreal looks to data to make insightful business decisions. How does L’Oreal unify its data across all its systems and databases? How does L’Oreal make the data accessible to thousands of employees? In this video, Antoine Castex, Enterprise Architect at L’Oreal, discusses with Martin Omander how L’Oreal built a serverless, multi-cloud warehouse based on Google Cloud.
Chapters:
0:00 – Intro
0:23 – Why does L’Oreal need a new data warehouse?
0:51 – Who is the L’Oreal group?
1:35 – Which systems does L’Oreal use?
2:14 – How does L’Oreal manage complexity?
3:59 – What is ELT?
4:57 – Who are L’Oreal’s data consumers?
5:41 – How L’Oreal built the data warehouse
8:51 – L’Oreal’s future plans
9:10 – Wrap up
Google Cloud Workflows → https://goo.gle/3q20M1V
Cloud Run → https://goo.gle/3CSWbXG
Eventarc → https://goo.gle/3B7qhFy
BigQuery → https://goo.gle/3KHgyJ3
Looker → https://goo.gle/3Rx4Ind
Checkout more episodes of Serverless Expeditions → https://goo.gle/ServerlessExpeditions
Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech
More Relevant Stories for Your Company
Bigbasket: Delivering Groceries Across 25 Cities in India
When Bigbasket was founded in December 2011, it guaranteed to deliver goods within a one-hour delivery slot of its customers' choosing or it would refund them 10 percent of their orders. The company also introduced an express service, delivering groceries within 90 minutes of an order being placed. Bigbasket needed
Collaboration Helps Nielsen Gain Better Consumer Insights and Save Costs by 20%
The Nielsen Company is one of the world’s most well-known and respected marketing research firms, with operations in over 100 countries. Its 56,000 employees work to provide insights and data that give customers a better understanding of what people watch, listen to, and buy. Those insights are essential to companies in a
If You Run Your Country’s Largest Retail Franchise, How Do You Pull Together, in Sync?
In 1984, Mario Maio set up a small business manufacturing electrical transformers in his Johannesburg garage. Over the next two decades, the ACDC Dynamics company he created came to dominate manufacturing, import, and distribution in South Africa’s electrical goods sector. Then, in 2007, Mario’s son, Ricardo Maio, founded a retail arm to
Video: How Whirlpool’s 90,000 Employees Collaborate to Bring 100 New Products to Market Every Year
As one of the world’s largest manufacturing companies, Whirlpool needs no introduction. The 104-year-old company manufactures home appliances from over 70 manufacturing and technology research centers around the world. Innovation has been the company’s focus since its inception and that’s evident from the fact that it introduced close to 100
