This is a guest post authored by Firebase customer, Anton Ivanov, Founder & CEO of DealCheck

Real estate investing is a fantastic way to build a stream of passive income and grow your wealth. Numerous studies have pointed out that real estate investing has created more millionaires throughout history than any other form of investing (like this one and this one). So why don’t more people do it?

I asked myself this very question a few years ago after talking to a group of friends about the success I’ve had with real estate, and listening to their reasons why they think it’s out of their reach.

A common theme among them was that they viewed it as something too difficult to learn and master. There were too many steps, the learning curve was steep and there was a lot of room for mistakes for somebody just starting out, especially when analyzing the financial performance of potential investment properties.

Traditionally, most investors used spreadsheets to do the math – which works only if you know what and how you’re calculating something. But if you don’t know that, it’s very easy to make mistakes and overlook things. And no one wants to make mathematical errors before a huge purchase like an investment property.

Where do I even begin?!

And that’s when I had the idea to build DealCheck – a cloud-based, easy-to-use property analysis tool for real estate investors and agents. I wanted to create a platform that would help new investors learn the ropes and avoid costly mistakes, but at the same time provide the flexibility to perform more advanced analysis with a click of a button.

Making real estate investing easier and more accessible.

The Challenges of Solo Development

I was working as a front-end engineer at the time, so I knew I could build the UI myself, but what about the back-end, data storage, authentication, and a bunch of other things you need for a full-functioning cloud app?

I didn’t know anybody I could bring on as a co-founder, so I set out to research what technologies and platforms I could leverage to help me with the back-end and server infrastructure.

Firebase kept popping up again and again and I began to look at it in more detail. It was then recently acquired by Google and its collection of BaaS (backend-as-a-service) modules seemed to offer the exact solution I needed to build DealCheck.

I was especially impressed with the documentation for each feature and how well all of the different technologies could be tied together to create one unified platform.

It wasn’t long before I signed up and started building the first MVP of the app.

Using Firebase to Quickly Build a Scalable Backend

As the only developer on the project, I had limited time and resources to spend on building the back-end, so I set out to use every Firebase feature that was available at the time to my advantage.

My goal was actually to write as little server-side code as possible and instead focus on leveraging the different Firebase modules to solve three specific challenges:

Challenge #1 – Authentication and User Management

The first one was authentication and user management. DealCheck’s users needed the ability to create their accounts so they can view and analyze properties on any device (more on that later). I wanted to have the ability to sign in with email, Facebook or a Google account.

Firebase Authentication was designed specifically for this purpose and I used it to handle pretty much the entire authentication flow. Out-of-the-box, it has support for all the major social networks, cross-network credential linking and the basic account management operations like email changes, password resets and account deletions.

There was no server-side code required at all – I just needed to build the UI on the front-end.

Email, Facebook and Google sign in powered by Firebase.

And as an added benefit, Firebase Authentication ties directly into the Realtime Database product to create a declarative permissions and access control framework that’s easy to implement and maintain. This helped me make sure user data was protected from unauthorized access, but also facilitate data sharing among users.

Challenge #2 – Cloud Storage with Cross-Device Sync

Next up was data storage. I knew that I wanted DealCheck’s users to be able to use the app and analyze properties online, on iOS and Android. So I needed a real-time, cloud-based database solution that could sync data across any device.

Syncing data across web and mobile is not easy!

Firebase Realtime Database is a NoSQL, JSON-based database solution that was designed exactly for this purpose, and I was actually surprised how great it worked. I used the official AngularJS bindings for Firebase on the front-end to read and write to it directly from the client.

I had to do some extra work on mobile to implement an offline mode with syncing after reconnections, but all-together the code required to make everything work was minimal.

As I mentioned, Firebase Authentication tied directly to the database to facilitate access control, so I really didn’t need to do anything extra there. And I was able to set up automatic daily backups of all the data with a click of a button.

Challenge #3 – Third-Party Integrations

Up to now, I had written exactly 0 lines of server-side code and everything was handled by the client directly. As DealCheck’s development progressed, however, I knew that I would need a server to handle some operations that could not be done in the client.

I wasn’t very experienced with server maintenance and DevOps, but fortunately the Firebase Cloud Functions product was able to solve all of my needs. Cloud Functions are essentially single-purpose functions that can be triggered (or executed) based on a specific HTTP request or events coming from the Authentication, Realtime Database or other Firebase products.

Each function can be run once based on a specific event trigger to perform its prescribed task. You don’t have to worry about provisioning a server instance or managing load – everything is done automatically for you by Firebase.

What’s even cooler, is that Cloud Functions can access the Realtime Database and Cloud Storage buckets of the same project, performing operations on them server-side, as needed.

This is how DealCheck processes subscription payments through Stripe, validates Apple and Google Play mobile subscription receipts, integrates with third-party APIs and updates database records without user interaction.

Bringing in sales comparable data from third-party providers into DealCheck.

Cloud Functions became the “glue” that tied the entire back-end infrastructure together.

Growing from an MVP to 100,000 Users with Firebase

The first version of the DealCheck app was built and launched in less than 5 months with just me on the development team. I definitely don’t think that would have been possible without Firebase powering the back-end infrastructure. Maybe the project wouldn’t have ever launched at all.

While Firebase is awesome for quick MVP development, it’s definitely designed to power production applications at scale as well. As DealCheck grew from a small side-project to one of the most popular real estate apps with over 100k users, all of the Firebase products that we use scaled to support the increasing load.

Moreover, the fantastic interoperability of all Firebase modules allows us to develop and release new features much faster because of the reduced coding requirements and ease of configuration.

So next time you’re looking to build an ambitious project with a small team – take a look at how Firebase can help you reduce development time and provide a suite of powerful tools that scale as your business grows.

This is exactly how DealCheck grew from a simple idea to make property analysis easier and faster, to an app that is helping tens of thousands of people grow their wealth and passive income through real estate investing. It’s a truly awesome and fulfilling experience to see your work positively impact so many people and it wouldn’t have been possible without Firebase.

Editor’s note: To launch a new mobile payment platform, Mercari needed a database solution strong on scalability, availability, and performance. Here’s how Cloud Spanner delivered those results. 

E-commerce companies need to connect customers to their services securely, reliably, with zero downtime. When Mercari, Inc. launched a new mobile payment platform, we chose Cloud Spanner as part of our data portfolio, which provided us with easy scalability to handle millions of new users, a fully managed service that minimized overhead costs, and deep integration with other Google Cloud services. 

Mercari, Inc., Japan’s largest C2C marketplace, launched its app in 2013, which allows 18.2million monthly users to easily and securely buy new and used items. Mercari expanded into the United States and in 2014 and launched Merpay, a mobile payment service that can be used through Mercari in Japan in 2017. With more than 85 million users, Merpay is now accepted at 1.8 million merchants and e-commerce sites in Japan, supporting payments via the DOCOMO ID contactless system and QR code.

Prioritizing availability and scalability

When we started building Merpay, we were looking for a new database. In the past, Mercari had used MySQL with bare metal hardware. Because of the amount of data, we required additional expertise to manage and maintain the hardware, software and MySQL implementation. Having built much of the microservices architecture for our Mercari app using Google Kubernetes Engine (GKE), it was natural to look at Google Cloud’s managed services when deciding upon our new database infrastructure for Merpay. 

For the database, we were focusing especially on requirements around availability, scalability, and performance. To do a single payment transaction, there were multiple steps each with writes, requiring high-write-throughput and low-latency from the database. Needing a solution that would support reliable payment processing 24/7/365, we chose Spanner, which offers up to 99.999% availability with zero downtime for planned maintenance and schema changes. 

We worked closely with Google Cloud’s Premium Support, Technical Account Management (TAM), and Strategic Cloud Engineer and Cloud Consultant teams to implement Spanner, including separating the payment processing— originally one of the functions in Mercari—as a microservice.

A nod to easier nodes and better scale

After launch, the number of Merpay users reached 2 million in only a few months. We had 45 Spanner nodes at the time of launch of Merpay and about 50 nodes four months later. Because we’d optimized the application side during those four months, we didn’t have to add many nodes to keep up with the growing traffic. 

Whenever we needed to scale up the serving and storage resources in our instances, Spanner made it easy to increase nodes as needed in the Cloud Console. To optimize costs, it’s easy to add nodes during a marketing campaign and remove them afterward. Even if the traffic count is different from expected, we can change the number of nodes immediately. That’s one incredible convenience of Cloud Spanner.

Building powerful pipelines

Our data pipelines are used for KPI analytics, fraud detection, credit scoring, and customer support use cases. Because Cloud Spanner integrates so easily with other Google Cloud data services, the data in Spanner is easily accessible for analytics. From the Spanner database for each microservice, we create both batch and streaming data pipelines using Pub/Sub and Dataflow, as well as Apache Flink, and load the data into BigQuery and Google Cloud Storage. We’re able to quickly aggregate the data required for data platforms such as BigQuery and Cloud Storage. The original data is stored in Spanner and managed by microservices, but analysis through BigQuery is centrally managed by the Data Platform team. This team determines the confidentiality level of data and centrally manages BigQuery permissions using Terraform. By centrally managing data access permissions on the data platform rather than on individual microservices, we’re able to set appropriate security for individual users.

Our full data portfolio also includes Looker, which we use for product analysis, accounting analysis, test performance visualization, operation monitoring, development efficiency analysis, and HR analysis. We also use Dataproc, Cloud Composer, Data Catalog, and Data Studio. The Dataflow template created for the pipeline is also published as OSS. 

With Spanner and other Google Cloud services, our Merpay platform is flexible, secure, scalable and highly available. As Spanner eliminates overhead, we can devote engineering resources to developing new tools and solutions for our customers. We’re looking ahead at providing cryptocurrency service, for example, and are now working on a project to migrate Mercari’s monolithic system that is still on premises entirely over to Google Cloud.

Read more about Mercari and Merpay. Or check out our recent blog: three reasons to consider Cloud Spanner for your next project.

Over 2.14 billion people worldwide are expected to buy online this year, according to Statista. Online retail sales will account for 22% of all purchases by 2023. But in a competitive retail landscape, positive interactions can mean the difference between a sale and an abandoned shopping cart.

One of the leading global marketplaces – Kinguin.net is a haven for gamers. Their bustling ecommerce business conducts over 500,000 new transactions monthly. Users will encounter over 50,000 unique digital products, from video games, gift cards, in-game items to computer software and services. With over 10 million registered users, Kinguin improved their experience by helping users find items quickly and deliver service at scale.

Helping customers find what they want, fast

Because of Kinguin’s high volume of users—both buyers and sellers—and breadth of digital products, browsing and shopping can be challenging. “Customers shop online for choice and convenience, but it can sometimes be overwhelming. We want anyone who shops at Kinguin to find what they are looking for quickly and easily,” says Viktor Romaniuk Wanli, Kinguin CEO and Founder.

Today’s retailers know that creating personalized shopping experiences is crucial for establishing and maintaining customer loyalty. Kinguin discovered their users were getting a rather standard retail experience. They wondered how they could offer them a more tailored, personalized experience.

They knew product recommendations were a great way to personalize experiences because they help customers discover products that match their tastes and preferences. But it’s not that easy to recommend products. Various shifting factors make recommendations much more complex:

• Customer behavior. Understanding customers is tough. How do you recommend something to a cold start user who’s never been to your site before? What happens when their behavior changes?
• Omnichannel context. According to Harvard Business Review, 73% of all customers use many channels when they buy. What happens when they go from desktop to mobile or from social media shopping to a proprietary app?
• Product data challenges. How do you recommend new products within a large catalog of items? What if your product data has sparse labeling or unstructured metadata?

Data wasn’t a problem for Kinguin. They had data orders, history, wishlists, and could collect events based on their platform interactions. It was the machine learning model expertise they lacked. So rather than building their own solution, they determined it was more cost effective for them to find a reliable partner. It was also essential that the solution integrated easily with Kubernetes, which enabled their global network.

With these considerations in mind, they applied for the Google Recommendations AI beta program. Kinguin became the first gaming e-commerce platform in Europe to use Recommendations AI when it launched in 2020.

Pro gamer move: using a fully managed AI service 

Google Recommendations AI uses algorithms to deliver highly personalized suggestions tailored to a customer’s preferences. Google Cloud based these algorithms on the same research that powers models by YouTube search and Google Shopping. Algorithms are always being tuned and adjusted to focus on individuals themselves—not just items.

Many shopping AIs rely on manually provisioning infrastructure and training machine learning models. Instead, Recommendations AI’s deep learning models use item and user metadata to gain insights. It processes Kinguin’s thousands of products at scale, iterating in real time. First, Kinguin pieces together a customer’s history and shopping journey. Then, using Recommendations AI, they can serve up personalized products—even for long-tail products and cold-start users. 

By leveraging internal tools, Kinguin didn’t need to start implementation from scratch. After a few trial sessions with Google Cloud engineers, they got started right away. Due to the fast-paced nature of a marketplace—i.e., price changes, out-of-stock items—Kinguin needed their recommendations to be as close to real time as possible. They used internal event buses to stream events and their product catalog directly to the recommendations API.

Kinguin rolled out in high-traffic areas, including their home page, product page, and category pages. They analyzed heat maps and scroll maps to figure out where to test placements. They also experimented with different recommendation models such as “recently bought together” and “you may like.” Engineers also factored in where they were implementing the models. For example, the “others you might like” model would fit best on the homepage, while “frequently bought together” made sense at checkout.

Understanding how product recommendations influence financials is critical for demonstrating the impact of personalization. Using BigQuery, Kinguin could analyze different cost projection models. BigQuery helped them dig into specific financial data to understand their margins and revenue gains.

Playing to win: enhanced customer experience

Since adopting Recommendations AI, Kinguin has improved both customer experience and satisfaction. Search times have shortened by 20 seconds. Additionally, their average cart value has increased by 5 EUR. Conversion rates have quadrupled since the outset. Click-thru rates have doubled, increasing by 2.16 on product pages and 2.8 times on recommendations pages.

“Google Recommendations AI has helped us evolve our service, increase customer loyalty and satisfaction. It has also contributed to a significant rise in sales,” says Wanli. Kinguin is already thinking about other ways of enhancing user experiences with recommendations. Ideas include their checkout process, other landing pages, and email marketing.

Kinguin’s journey with Google Cloud shows how companies can leverage AI to optimize sales and deliver high-performing, low-latency recommendations to any customer touchpoint. 

Learn more about Recommendations AI and Google Cloud AI and machine learning solutions.

With over ⅓ of the food in the USA ending up as waste according to the USDA, it is a compelling challenge to address this travesty. What will happen to hunger, food prices, trash reduction, water consumption, and overall sustainability when we stop squandering this abundance?

Beginning with the departure from the farm to the back of the store, the freshness clock continues to run. Grocers work very hard to purchase high quality produce items for their customers and the journey to the shelf can take a toll in both quality and remaining shelf life. Suppliers focus on delivering their items through the arduous supply chain journey to the store with speed and gentle handling. The baton is then passed to the store to unload and present the items to customers with care to sell through each lot significantly before the expiration or sell by date. This is to ensure that the time spent in the customer’s home is ample to ensure a great eating experience as well. Food waste is a farm to fork problem with opportunity at every step of the chain, but today we will focus on the segment that the grocery industry oversees.

With the complexities of weather, geopolitical issues, distribution, sales variability, pricing, promotions, and inventory management, it seems daunting to impact waste. Fortunately, data analytics and machine learning in the cloud is a powerful weapon in the fight against food waste. Data Scientists harness knowledge to draw meaning from data turning that data into decision driving information.

One key Google has been working on to accelerate value is to break down data silos and leverage machine learning to realize better outcomes, using our Google Data Cloud platform. This enables better planning through demand forecasting, Inventory management, assortment planning, and dynamic pricing and promotions.

That sounds great but how does it work?

Let’s walk through a day in the life journey to see how the integrated Google Data Cloud platform can change the game for good. Our friendly fictitious grocer FastFreshFood is committed to selling high quality perishable items to their local market. Their goal is to minimize food waste and maximize revenue by selling as much perishable fresh food as possible before the sell by date. Our fictitious grocer in partnership with Google Cloud could build a solution that will take a significant bite out of their food waste volume and better satisfy customers.

1. Sales through the register and online are processed in real time with Datastream, Dataflow to keep an accurate perpetual inventory by minute of every single item.
2. A Demand forecasting model using machine learning algorithms in BigQuery then identifies needs for back room replenishment, so Direct Store Delivery and daily store Distribution Centers manage ordering more efficiently to ensure just the right amount of each product each day.
3. Realtime reporting dashboards in Looker with alerting capabilities enable the system to operate with strong associate support and understanding. The reporting suite shows inventory levels into the future, daily orders, and at risk items.

The pricing algorithm could also alert store leadership concerning any items that will not sell through and suggest real time in store specials resulting in zero waste at shelf and maximized revenue.

This approach is not just for perishable categories and is a pattern that works well for in-store produced items and center store items. The key point is that by bringing ML/AI to difficult business problems grocers are reinventing what is possible for both their profitability and sustainability.

The technical implementation of this design pattern in Google Cloud leverages Datastream, Dataflow, BigQuery and Looker products, it is detailed in a technical tutorial accompanying this blog post.

In partnership with Google Cloud, retailers can solve complex problems with innovative solutions to achieve higher quality, lower cost, and provide great customer experiences. To learn more from this and other use cases, please visit our Design Patterns website.

Curious to learn more?

We’re excited to share what we know about tackling food waste at Google, a topic we’ve been working on in the last decade as we’ve embarked on reducing our own food waste in our operations in over 50 countries in the world. The Google Food for Good team works exclusively on Google Cloud Platform with our partners on this topic. Two additional articles below.

Silos are for food, not for data – tackling food waste with technology
This business Cloud blog directly addresses information silos that currently exist across many nodes in the food system and how to break down cultural and organizational barriers to sharing.

“Unsiloing” data to work toward solving food waste and food insecurity
This follow-on technical Cloud blog articulates the path to setting up data pipelines, translating between data sets (not everyone calls a tomato a tomato!) and making sense of emergent insights.

I’m so excited to be part of Google Cloud. Data has been a longstanding part of my career and it is at the heart of business transformation. Many companies have mastered the ability to collect data and have mechanisms in place to draw on some of it to solve business problems.  But most data collected piles up and is never put to a useful purpose. Accessing it and mining it for helpful insights is practically impossible at many companies. It’s always stuck in hard to reach places, fragmented across departments and unavailable when you need it the most.  

Our mission at Google Cloud is to accelerate your ability to digitally transform your business with data. Solving data challenges is in our DNA, and over the last two decades we’ve been in a unique position to help our customers get the most out of data to drive real business value.  

Google products are used and loved by billions of people across the globe. These products bring together the complex web of disconnected, disparate, and rapidly changing data that makes up the internet. When you get an answer in milliseconds from google.com via a simple search bar, you know we have this down to a science. Google Cloud brings this expertise in data and software together for businesses of all sizes so that you can gain advantage from your data. We call this the data cloud. 

Enter the data cloud

A data cloud offers a comprehensive and proven approach to cloud and embraces the full data lifecycle, from the systems that run your business, where data is born, to analytics that support decision making, to AI and machine learning (ML) that predict and automate the future. A data cloud allows you a way to securely unify data across your entire organization, so you can break down silos, increase agility, innovate faster, get value from your data, and support business transformation. This is the heart of the data cloud.

Why a data cloud is essential

Building a data cloud using Google Cloud’s technologies helps organizations accelerate business transformation by giving everyone access to the right information at the right time so that they can act more intelligently based on it.

Since I’ve joined Google, I’ve been not only inspired by the work that the team has done to build products with a user-first mindset, but our customers have been an inspiration to each of us in what’s possible. 

• The Home Depot built a data cloud using Google Cloud technologies to help keep 50,000+ items stocked at over 2,000 locations. They’re making their 400,000+ associates smarter by giving them visibility into the things each customer needs, like item location within a local store. By leveraging BigQuery, their query performance dropped from hours and days to seconds and minutes. The Home Depot also uses Cloud SQL, Spanner, and Bigtable for their operational use cases and AI to help locate goods using their mobile apps for in-store navigation. 
• Major League Baseball (MLB) is reimagining the fan experience with their data cloud. To build engagement with today’s fans, drive engagement with future generations, and lay the groundwork for future innovation, MLB consolidated its infrastructure and migrated to Google Cloud’s Anthos, Google Kubernetes Engine, Cloud SQL, and BigQuery. MLB tracks every moment of every game for an audience on seven continents with Cloud SQL,  this valuable data to drive deeper engagement with fans.
• Vodafone is using their data cloud to offer their customers new, personalized products and services across multiple markets. By identifying more than 700 use cases to deliver new products and services, Vodafone can support fact-based decision-making, reduce costs, remove duplication of data sources, and simplify operations.  With Google Cloud,  Vodafone’s operating companies in multiple countries can access improved data analytics, intelligence, and machine-learning capabilities. 

Here are four reasons why customers trust  Google Cloud to build their data cloud strategy: 

First, Google  delivers insights at planet scale  

Customers often gravitate to Google Cloud for our specific data tools that were built for Google’s internal data needs and are unmatched for speed, scale, security, and capability for any size organization. BigQuery is the leading solution for analytics and allows you to run analytics at scale with a 99.99% SLA and up to 34% lower TCO than cloud data warehouse alternatives. Spanner provides unlimited scale, global consistency across regions, and high availability up to 99.999%, at a TCO that is 78% lower compared to on-prem databases and 37% lower than other cloud options. Firestore continues to see rapid adoption with over 2M databases created to power mobile, web, and IoT applications across customer environments. And finally Looker, an API for all your data, offers a single shared place for people and apps to interact with it, no matter the cloud environment. 

Second, Google’s AI helps your business be more intelligent

Google was built on pioneering AI research and the principle of making the world’s information useful to people and businesses everywhere. AI powers some of Google’s most popular products, such as Search, Maps, Ads, and YouTube. We have leveraged this expertise to deliver a new, unified AI platform that gives every data scientist, data analyst, and ML engineer access to the same AI toolkit Google uses. Automated machine learning, accelerated experimentation and custom training, and more deployed models than any other platform enable your entire data team to drive business outcomes at any scale. 

Third, Google is the open data platform 

Google Cloud’s open platform gives customers maximum flexibility for managing transactional, analytical, and AI-based applications. Customers can choose from a wide range of transactional, processing, and analytics engines, open source tools, open APIs, and ML services to eliminate lock-in. This includes choice of deployment across multi-cloud and hybrid environments and easy interoperability with existing partner solutions and investments. With BigQuery Omni, organizations can choose to deploy their data warehousing solution to work natively with AWS or with Azure (coming soon). Looker supports 50+ distinct SQL dialects across multiple clouds and our database services like Cloud SQL, one of the fastest growing services at Google Cloud, offers familiar open source MySQL and PostgreSQL standard connection drivers, so you can work with your preferred tools and stay up-to-date with the latest community enhancements. In addition, Google offers an unrivaled developer community across the fields of AI, machine learning, mobile, application development, microservices, and access to third party solutions and open source systems. 

Fourth, Google offers a trusted platform for your data needs

Customers can take advantage of the same secure-by-design infrastructure, built-in data protection, and global network that Google uses to ensure compliance, redundancy, and reliability. All of Google’s data is encrypted in transit and at rest, by default. Google offers industry-leading reliability across regions so you’re always up and running. Spanner offers a 99.999% SLA and BigQuery offers a 99.99% SLA. For BI and embedded analytics, Looker supports data governance via a semantic layer that organizes your data and stores your business logic centrally, delivering consistent and trusted KPIs. And finally, our multi-layered security approach across hardware, services, user identity, storage, internet communication and operations provides peace of mind that your data is protected.

Learn more at Data Cloud Summit

We are committed to helping you build a data cloud that gives you deep insights into your business and process automation. Join me as I welcome Anders Gustafsson, CEO of Zebra and 

Gil Perez, CIO of Deutsche Bank at the Data Cloud Summit on May 26, 2021 to learn and share new ways to use data for good. I can’t wait to hear what you accomplish.