Enterprise agility and the ability to innovate, adapt and respond quickly to the ever-changing risk and regulatory landscape is no longer a choice, but the cornerstone of successful digital transformation and commercial growth. Traditional access to and ways of managing data invariably create challenges in dealing with multiple data repositories, reconciliations, fire-drills, etc.

In response, the move to cloud is increasing significantly. It enables risk analytics and regulatory reporting at scale in a secure environment with data storage, management and encryption capabilities as a standard. In addition, as regulatory reporting requirements become more granular, machine learning can help facilitate new insights and allow for risk management to become more embedded into operational processes.

This webinar will address the day-to-day challenges in risk management and regulatory compliance, while also exploring how technological innovations can provide massive improvements and potential.

Key themes

• Real-life data challenges in the eyes of risk managers: can compliance, fraud detection and identifying liquidity positions be improved through the use of AI?
• Innovative approaches to streamline regulatory reporting to derive deeper customer insights from data at the moment of truth.
• Reimagining operations: how to modernise the data infrastructure to accommodate data explosion, drive flexibility and deliver a more cost effective outcome.

The ability to gain real-time insights from IoT data can redefine competitiveness for businesses. Intelligence allows connected devices and assets to interact efficiently with applications and with human beings in an intuitive and non-disruptive way. After your IoT project is up and running, many devices will be producing lots of data. You need an efficient, scalable, affordable way to both manage those devices and handle all that information. 

IoT Core is a fully managed service for managing IoT devices. It supports registration, authentication, and authorization inside the Google Cloud resource hierarchy as well as device metadata stored in the cloud, and the ability to send device configuration from other GCP or third-party services to devices. 

Main components

The main components of Cloud IoT Core are the device manager and the protocol bridges:

• The device manager  registers devices with the service, so you can then monitor and configure them. It provides:
  • Device identity management 
  • Support for configuring, updating, and controlling individual devices
  • Role-level access control
  • Console and APIs for device deployment and monitoring
• Two protocol bridges (MQTT and HTTP) can be used by devices to connect to Google Cloud Platform for:
  • Bi-directional messaging
  • Automatic load balancing
  • Global data access with Pub/Sub

How does Cloud IoT Core work?

Device telemetry data is forwarded to a Cloud Pub/Sub topic, which can then be used to trigger Cloud Functions as well as other third-party apps to consume the data. You can also perform streaming analysis with Dataflow or custom analysis with your own subscribers.

Cloud IoT Core supports direct device connections as well as gateway-based architectures. In both cases the real time state of the device and the operational data is ingested into Cloud IoT Core and the key and certificates at the edge are also managed by Cloud IoT Core. From Pub/Sub the raw input is fed into Dataflow for transformation, and the cleaned output is populated in Cloud Bigtable for real-time monitoring or BigQuery for warehousing and machine learning. From BigQuery the data can be used for visualization in Looker or Data Studio and it can be used in Vertex AI for creating machine learning models. The models created can be deployed at the edge using Edge Manager (in experimental phase). Device configuration updates or device commands can be triggered by Cloud Functions or Dataflow to Cloud IoT Core, which then updates the device.  

Design principles of Cloud IoT Core

As a managed service to securely connect, manage, and ingest data from global device fleets, Cloud IoT COre is designed to be:

• Flexible, providing easy provisioning of device identities and enabling devices to access most of Google Cloud
• IThe industry leader in IoT scalability and performance
•  Interoperable, with supports for the most common industry-standard IoT protocols

Use cases

IoT use cases range across numerous industries. Some typical examples include:

• Asset tracking, visual inspection, and quality control in retail, automotive, industrial, supply chain and logistics
• Remote monitoring and predictive maintenance in oil & gas, utilities, manufacturing, and transportation
• Connected homes and consumer technologies.
• Vision intelligence in retail, security, manufacturing, and industrial sectors
• Smart living in commercial, residential, and smart spaces 
• Smart factories with predictive maintenance and real-time plant floor analytics

 For a more in-depth look into Cloud IoT Core check out the documentation.  

https://youtube.com/watch?v=76v16P-Wqe4%3Fenablejsapi%3D1%26

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Enterprises today have a very broad mix of networks — from SD-WANs, dedicated WANs such as MPLS, cloud interconnects, to VPNs. At the same time, they’re moving those WANs to the cloud to take advantage of faster turn-up, lower cost, and increased feature velocity. As workloads migrate to the cloud and multi-cloud environments, we believe that it’s critical to simplify enterprises’ networking model.

Each major cloud provider uses distinct abstraction models to configure networks or connections between your resources. Some use gateways, some use connections or links. Network Connectivity Center, launched last year, provides a simple management solution for your network connection, and is now Generally Available.

In this post, we outline the typical connectivity use cases for customers to help you select and set up the best connectivity option for your environment.

Understanding cloud network connectivity

Cloud networking refers to the ability to connect two resources together inside a cloud, across clouds and with on-premises data centers. A cloud provider needs to provide three main types of connectivity:

• Site-to-cloud – Between on-premises equipment and cloud resources
• Site-to-site – To connect on-premises resources together
• VPC-to-VPC – Connectivity between cloud resources
• Let’s take a look at each one.

Site-to-cloud connectivity

Site-to-cloud connectivity traditionally is done via a cloud interconnect or a cloud VPN. The automatic exchange of routes between on-premises and multiple VPCs can be done using a transit VPC.

A newer approach is to add cloud providers into an SD-WAN mesh using a router virtual appliance in Google Cloud. Network Connectivity Center brings the capacity to synchronize the appliance routes dynamically via BGP to Cloud Router and hence their VPCs. It enables connectivity between on-premises data centers and branch offices and their cloud workloads via SD-WAN-enabled connectivity. This capability is available globally across all 29+ Google Cloud regions. Several of our partners also support this capability in their router appliances.

Site-to-site connectivity

Site-to-site connectivity enables network connectivity directly between two or more hybrid connection points (VPN, Interconnect or SD-WAN). Network Connectivity Center simplifies this model by automating the routing announcements in this environment, such that all sites connected to a single global Network Connectivity Center hub are able to communicate freely in any-any fashion. You can see an example of this for a specific market vertical use case in a recent blog, Voice trading in the cloud — digital transformation of private wires.

VPC-to-VPC connectivity

You can create a full or partial mesh of VPC connections using multiple technologies, with VPC peering being the most common. VPC peering provides highly performant, low latency, private connectivity for customer networks connected via hybrid connectivity and Network Connectivity Center to multiple VPCs containing workloads, which can be segmented via granular firewall policies as needed. Alternatively, you can use a transit VPC model to connect multiple VPCs together in a hub and spoke topology.

With tight integration with third-party router appliances as mentioned earlier, you can also leverage their third-party supported solutions such as next-generation firewalls to connect your VPCs together to meet specific compliance and segmentation requirements. Network Connectivity Center allows you to synchronize the routing tables of these appliances with your VPC’s routing table, simplifying the process of setting up redundant configurations.

What’s next for cloud networking connectivity in Google Cloud?

As enterprises continue to migrate different types of workloads to public cloud providers, networking topologies are becoming more complex. In summary, we have solutions for all connectivity needs. We aim to keep our models and solutions understandable and simple. Over time, look for Network Connectivity Center to become Google Cloud’s single point of configuration for all your connectivity needs, with capabilities to handle the most complex network.

Editor’s note: PedidosYa is the market leader for online food ordering in Latin America, serving 15 markets and over 400 cities. It’s also one of the largest brands within the German multinational company Delivery Hero SE. With over 20 million app downloads, PedidosYa provides the best online delivery experience through 71,000+ online partners, including restaurants, shops, drugstores, and specialized markets. 

Having constant access to fresh customer data is a key requirement for PedidosYa to improve and innovate our customer’s experience. Our internal stakeholders also require faster insights to drive agile business decisions. Back in early 2020, PedidosYa’s leadership tasked the data team to make the impossible possible. Our team’s mission was to democratize data by providing universal and secure access while creating a comprehensive information ecosystem across PedidosYa. We also had to achieve this goal while keeping costs under control— even during the migration stage and removing operational bottlenecks. 

Challenges with legacy cloud infrastructure

PedidosYa first built its data platform on top of AWS. Our data warehouse ran on Redshift, and our data lake was in S3. We used Presto and Hue as the user interfaces for our data analysts. However, maintaining this infrastructure was a daunting task. Our legacy platform couldn’t keep up with the increasing analytics demands. For example, the data stored on S3 complemented by Presto/Hue required high operational overhead. This was because Presto and our IAM (identity access management) didn’t integrate well in our legacy ecosystem. Managing individual users and mapping IAM roles with groups and Kerberos was operationally time-consuming and costly. Further, sharding access on the S3 files was far too complicated to enable seamless ACLs (access control lists).  

There were also challenges with workload management. Our data warehouse had batch data loaded overnight. If one analyst scheduled a query to run during the overnight ETL (extract, transform, load) workload, it would disrupt the current ETL task. This could stop the entire data pipeline. We’d have to wait until data engineers intervened with a manual fix.

It was also difficult to understand whether a query error was due to performance issues or platform resource exhaustion. This lack of clarity affected our data analysts’ ability to autonomously improve querying efficiency. Data team members needed to manually inspect personal queries looking for performance issues. Also,  the current architecture was prone to a ‘tragedy of the commons’ situation; it was seen as an unlimited and free resource. As a result, it was impossible to disentangle the infrastructure from different stakeholder teams, as all had very different needs. 

The decision to modernize our data warehouse

Given the growing challenges from our legacy platform, our tech team decided to transform our analytics environment with a modern data warehouse. They required the following key criteria from their next data platform: 

• Scalability – The ability to grow with elastic infrastructure.
• Cost control – Cost management and transparency. These factors promote efficiency and ownership—both key aspects of data democratization.
• Metadata management – Intuitive data platform focusing on users’ previous SQL knowledge. Plus, being able to enrich the informational ecosystem with metadata,  to diminish data gatekeepers.
• Ease of management – The team needed to reduce operational costs with a serverless solution. Data engineers wanted to focus on their key roles rather than acting as database administrators and infrastructure engineers. The team also wanted much higher availability, and to reduce the impact of maintenance windows and vacuum/analysis.
• Data governance and access rights – With a growing employee base with varying data access requirements, the team needed a simple yet comprehensive solution to understand and track user access to data.

Migrating to Google Cloud

After exploring other alternatives, we concluded Google Cloud had an answer to each of our decision drivers. Google Cloud’s serverless, managed, and integrated data platform, coupled with its seamless integration across open-source solutions, was the perfect answer for our organization. In particular, the natural integration with Airflow as a job orchestrator and Kubernetes for flexible on-demand infrastructure was key.  

We  used Dataflow together with Pub/Sub and Cloud Functions for our data ingestion requirements, which has made our deployment process with Terraform seamless. Because we set up everything in our environment programmatically, operation time has diminished. Google Cloud reduced the deployment process from about 16 hours in our legacy platform to 4 hours.  This is partly due to the friendliness of automating the deployment (such as schema check, load test, table creation, build.) process with Terraform, Cloud Functions, Pub/Sub, Dataflow, and BigQuery on GCP. Input messages processed with Dataflow allow us to abstract and plan the schema changes according to the needs of the functional team. For example, schema changes raise an alarm, and then we can modify the raw layer table schema. By doing this, we ensure that backend modifications that we don’t control do not affect upper layers.

A key reason why we picked Google Cloud was because of its advanced cost and workload management coupled with its transparent log analytics. This information gives us a complete view into any query performance issues to make improvements on the fly. Further, we achieved a significant amount of cost savings by consolidating multiple tools to BigQuery.With BigQuery, we’ve been able to reduce our total cost per query by 5x.

This was due to a number of reasons:

• Automating pipeline deployment made it much simpler to maintain the data processing processes. 
• Analysts are conscious about what queries they’re running, resulting in running better, more optimized queries. 
• Analysts use a Data Studio dashboard to see their queries and all the associated costs. As a result, there’s a lot more transparency for each persona.

 With these changes, we can easily manage and assign costs associated with each workload with their own cost centers using specific Google Cloud projects.

Change management is always challenging. However, BigQuery is intuitive and doesn’t have a steep learning curve from Hue/Hive on SQL basics. BigQuery also allowed the team to expand its capabilities and enabled them to properly work with nested structures, avoiding unnecessary joins and improving query efficiency. Additionally, we now use Data Catalog as our unique point of truth for metadata management. This allows our team to break the data access barriers and enable federation of data across the organization. By using Airflow to orchestrate everything, we keep track of every data stream. With this information, each end user can see their regularly used data entities’ status via the dashboard. This also adds transparency to our everyday data processes.

Finally, with Google Cloud’s IAM rules applied across the different products, data sharing and access is close to a noOps experience. We have programmatically implemented access according to roles and level access within the company. This allows certain pre-validated roles to view more sensitive information. These solutions help drive a more automated data governance experience. 

Up next: Google Cloud AI/ML

The new stack based on BigQuery has created significant productivity gains. Freed from the burden of operational management, PedidosYa’s data team can now focus on adding value through data tools and products.  

• Our data engineers are better equipped to integrate constantly changing transactional and operational data.
• The dataOps team can automate the infrastructure and provide autonomy to the end user.
• Our data quality team can focus on bringing added value to data stakeholders. 
• Data scientists and data analytics can spend more time analyzing data and less time asking data gatekeepers for data access.

PedidosYa can now democratize data access with a well-governed architecture. We are still at the beginning of our journey, but we are closer to achieving our vision of building a data-driven organization. Up next: expanding our artificial intelligence and machine learning capabilities.

Tune in to Google Cloud’s Applied ML Summit on June 10th, 2021, or listen on-demand later, to learn how to apply groundbreaking machine learning technology in your projects.

As ecommerce for home goods exploded in popularity during COVID, furniture and DIY retailers looked to find new ways to grow online transaction sizes to in-store levels. Shopping for furniture and home improvement projects has always been challenging online. Furniture, kitchen cabinets, fixtures, and appliances become a part of daily life, are challenging to return, and have a low purchase frequency. Once a shopper makes a decision, they tend to live with it for many years. These are visual, tactile, decisions that require measurements, style choices, budgeting, an understanding of available products, and an involved consideration processes. During the pandemic, retailers turned to 3D to enhance these virtual shopping experiences. 

Inspiration and visualization cultivate confidence

Our 3D Room Commerce company, Marxent offers 3D visualization and configuration solutions that help retailers sell complex, configurable products online through consumer-facing 3D design and visualization apps. The Marxent 3D Room Planner with HD Renders helps shoppers to visualize how furniture or kitchen cabinet configurations will look in the specific floorplan of their home. Founded in 2011, Marxent envisions a world where buying a dining table or remodeling an entire kitchen is as easy as buying a car from Carvana or ordering dinner through  Bite Squad. Our 3D apps create a streamlined inspiration to transaction to advocacy model that cultivates shopper confidence and allows retailers to sell the whole room, not just individual items. 

Using Google Cloud as a foundation, we help some of the largest retail and home goods companies in the world provide exceptional customer journeys. We trust Google Cloud because our clients trust us to make it faster and easier for shoppers to buy semi-custom, configurable projects.

Embracing the power of 3D to super-charge ecommerce 

It’s inarguable: e-commerce is on the rise. More people than ever before are shopping online for furniture, kitchen cabinets, decking, and other large-scale configurable products. 

Customers who design with a retailer, usually buy from them. When shoppers visit stores and showrooms, they find inspiration in merchandised scenes that illustrate how products like sofas, chairs, rugs and lamps work together to accomplish a look. Skilled sales people make suggestions and offer advice on how to put pieces together. They may even work up a quick floor plan to show how multiple items work together in a room. In store, the inspiration phase is intimately tied to consideration and, ultimately, to driving transactions.

By contrast, online shoppers typically start home projects by seeking inspiration and ideas from Pinterest, Instagram and unbranded online image searches. Once they have formed their style preferences, shoppers keep searching to compare products across multiple retailers, plan their project, and put together a final budget. 

To own the whole project sale, retailers need to own the entire inspiration to transaction to advocacy journey. With both online and in-store applications, Retailers leverage Marxent’s 3D Room Planner app to build shopper confidence, capture the whole room sale and win the customer over.

https://youtube.com/watch?v=_svOdVvX5LA%3Fenablejsapi%3D1%26

PRE-RENDERED MID-POLY 3D SCENE

POST-RENDERED MID-POLY 3D SCENE – This is a slightly different angle of the same room that has been rendered into a “Raw Render” (rendered in under 2 minutes).

Through Marxent’s 3D Room Commerce solution, users experience a cyclical inspiration to transaction to advocacy journey. It starts with shoppers viewing inspirational images and media online. Using Marxent’s applications, they can design directly from inspirational images to create a custom, configured space without any product catalog knowledge. Shoppers can visualize the products they love together and in the context of their own floor plan instead of navigating product pages and wondering if items will work together. 

Then, they can add the whole room to a shopping cart with a single click. While this virtual experience does lead to a transaction, it also allows users to save, collaborate on, and share the spaces they’ve created. They become advocates by sharing their projects on social media, starting the inspirational content cycle again. 

Putting an end to manual operations

To deliver renders at scale, Marxent needed to update their cloud render solution. Initially, our 3D Art team operated a manual on-premise render fleet. However, this required many hours of manual setup, configuration, and operation. We also had to manage expensive graphics servers—bare metal, CPUs, GPUs, RAM, HDD, and more. The only solution was to automate the 3D rendering process and empower end-users to rapidly create their own 3D room renders. 

When we were evaluating new solutions, we saw distinct advantages in the Google Cloud Platform that would help them safely and securely scale their business, while strengthening their partnerships with end customers. For example, in moving to Google Cloud, we could automate and scale our rendering process without having to manage fleets of physical servers. We also viewed the platform as an asset due to Google’s secure-by-design infrastructure, agility, data analytics capabilities, and potential for joining the Marketplace.

Creating magical customer experiences that inspire purchase

To provide our customers and shoppers with contextual experiences, Marxent’s applications use mid-poly 3D models that balance speed and realism. These models provide a latency-free, real-time design experience that can be rendered into scenes that are realistic enough to be perceived as photos on social media. 

The complex process of rendering these images requires a combination of efficiency, speed, analytics, and consistent performance that Google Cloud provides. When configured with powerful and speedy gaming GPUs, Marxent can provide fast rendering that meets customers and shopper demands. Here’s a look at our HD Renders application.

Before a user can request an HD Render, they must create a room in the Marxent 3D Room Planner. Once requested, Marxent pulls the saved project from the database and kicks off the process with Cloud Pub/Sub. The project loads into a gaming GPU, using the same platform code running when the user first creates the room in the application. It boots up the app in the cloud to load the room.

The code then scours the space and prepares it for rendering, adjusting texture formats, and adding in lighting. After going through the render engine, the project automatically uploads to Cloud Storage. Finally, the user receives a link to the final product. Throughout, Cloud Pub/Sub handles messages ensuring the right event processes are happening, such as rendering success or failure.

Using this process, it’s possible to create dozens of images out of a single scene, trading products in and out of a floor plan by leveraging a complete catalog of content geometries and covers, textures, and finishes.

Utilizing Google Cloud throughout the buying journey 

Today, Marxent’s applications power world-class retailers with AR, VR, and 3D commerce experiences. We use cutting-edge graphics hardware to create renderings in less than 2 minutes per screenshot, often much faster. We’re also saving money as we no longer have to manage expensive servers or purchase expensive hardware upfront. Our clients are happier because we have passed on the cost savings to them while now having limitless scaling capabilities to meet demand.

By partnering with Google Cloud, Marxent can confidently offer our customers secure applications built on infrastructure with advanced security tools that support compliance and data confidentiality. Backed by a globally consistent platform, we can also help brands build reliable purchasing experiences across customer touchpoints—without fear of downtime during peak sales periods. This strategic partnership has allowed us to provide a best-in-breed, customer-first experience that our customers demand while providing the reliability that our partners expect.

With customers demanding seamless shopping experiences, Marxent’s 3D technologies open doors to new, easier, more convenient, and more satisfying shopping experiences that empower consumers to buy the right products the first time. 

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