Finding ways to increase customer loyalty and profitability in a post-COVID world is top of mind for hotels, bars, and restaurants. Unfortunately, many food and beverage services providers struggle to deliver the personalized experiences that keep guests coming back for more. The reality is that most traditional hospitality apps offer only limited insight into guest activities, preferences, underlying operating costs, and other essential details.

We built SevenRooms to help food and beverage operators create truly memorable experiences by managing and personalizing every step of the guest journey. With SevenRooms, restaurants in more than 250 cities globally leverage technology and data to provide unforgettable hospitality experiences with a human touch. That includes seating guests at their favorite table, pouring complimentary glasses of wine from a preferred vintage, offering special menu options, and personalized experiences for special occasions.

Scaling guest experience and retention on Google Cloud

When developing SevenRooms, we needed a technology partner that would enable our small team to securely scale services, automate manual tasks, and accelerate time to market while reducing IT costs. That’s why we started working with Google App Engine—and later became more involved with the Google for Startups Cloud Program.

We soon realized many traditional apps lacked the integrations and capabilities needed to respond to the unique challenges facing food and beverage operators that the Google for Startups Cloud Program services provide. With guidance from experts on the Google Startups Success team, we quickly transformed SevenRooms from a beta restaurant and nightclub reservation and guest management point solution into a full-fledged guest experience and retention platform that analyzes actionable data to automatically build and continuously update detailed guest profiles.

The combination of Google App Engine and other Google Cloud solutions makes everything easier to build and scale. We’re seeing our total cost of ownership (TCO) decline by 10-15%, so we can shift additional resources to R&D to help customers create one-of-a-kind interactions with their guests. And importantly, we can bring new products to market 200-300% faster than competitors.

Because SevenRooms handles a lot of sensitive guest data, stringent security protocols are vital. We store all data on Google Cloud, taking advantage of its highly secure-by-design infrastructure and built-in support for international data privacy laws such as GDPR. We use BigQuery and Looker for data analysis and reporting, and power our NoSQL database with Firestore. We also scale workloads and run Elasticsearch on Google Compute Engine (GCE)—and seamlessly integrate our reservation booking engine with Google Maps and Google Search.

In the future, we’re looking to further market actionable guest data with the help of advanced machine learning (ML) models with TensorFlow and Google Cloud Tensor Processing Units (TPUs). Cloud TPUs and Google Cloud data and analytics services are fully integrated with other Google Cloud offerings, including Google Kubernetes Engine (GKE). By running ML workloads on Cloud TPUs, SevenRooms will benefit from Google Cloud’s leading networking and data analytics technologies such as BigQuery.

We’re also exploring additional Google Cloud solutions such as Anthos to unify the management of infrastructure and applications across on-premises, edge, and multiple public clouds, as well as Google Cloud Run to deploy scalable containerized applications on a fully managed serverless platform. These solutions will enable us to continue to quickly expand our services and offer customers a variety of new benefits.

Building a profitable, sustainable future in food and beverage

Our work with the Google Startups Success team has been instrumental to helping us get where we are today. Their responsiveness is incredible and stands out compared to services from other technology providers. Google Cloud gives us a highly secure infrastructure and next-level training to evolve our infrastructure using solutions such as BigQuery and Backup and Disaster Recovery.

We also work with Google Cloud partner, DoiT International, to further scale and optimize our operations. In particular, DoiT provided expert scripts to short-cut lengthy processes, while actively troubleshooting any issues or questions that came up. DoiT continues to share guidance in key areas for future products and features. They have provided expertise for architecture, infrastructure and cost management. Moving forward, we’re excited to work with Google Cloud and DoiT to handle a growing surge in users which we anticipate in 2023 and beyond.

With Google Cloud, SevenRooms is revitalizing food and beverage service delivery by enabling businesses to cultivate and maintain direct guest relationships, deliver exceptional experiences, and encourage repeat visits. We’ve compiled many case studies that demonstrate how our customers see great results by personalizing their interactions with guests, from $1.5M in cost savings, to $400K of additional revenue, to a 68% jump in email open rates.

Demand for our guest experience and retention platform keeps growing as we help our customers take a people-first approach by delivering unique and tailored dining experiences. We can’t wait to see what we accomplish next as we expand our team and reach new markets worldwide.

If you want to learn more about how Google Cloud can help your startup, visit our page here to get more information about our program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more.

Picking the right database for your application is not easy. The choice depends heavily on your use case—transactional processing, analytical processing, in-memory database, and so on—but it also depends on other factors. This post covers the different database options available within Google Cloud across relational (SQL) and non-relational (NoSQL) databases and explains which use cases are best suited for each database option. 

Relational databases 

In relational databases information is stored in tables, rows and columns, which typically works best for structured data. As a result they are used for applications in which the structure of the data does not change often. SQL (Structured Query Language) is used when interacting with most relational databases. They offer ACID consistency mode for the data, which means:

• Atomic: All operations in a transaction succeed or the operation is rolled back.
• Consistent: On the completion of a transaction, the database is structurally sound.
• Isolated: Transactions do not contend with one another. Contentious access to data is moderated by the database so that transactions appear to run sequentially.
• Durable: The results of applying a transaction are permanent, even in the presence of failures.

Because of these properties, relational databases are used in applications that require high accuracy and for transactional queries such as financial and retail transactions. For example: In banking when a customer makes a funds transfer request, you want to make sure the transaction is possible and it actually happens on the most up-to-date account balance, in this case an error or resubmit request is likely fine.

There are three relational database options in Google Cloud: Cloud SQL, Cloud Spanner, and Bare Metal Solution.

Cloud SQL: Provides managed MySQL, PostgreSQL and SQL Server databases on Google Cloud. It reduces maintenance cost and automates database provisioning, storage capacity management, back ups, and out-of-the-box high availability and disaster recovery/failover. For these reasons it is best for general-purpose web frameworks, CRM, ERP, SaaS and e-commerce applications.

Cloud Spanner: Cloud Spanner is an enterprise-grade, globally-distributed, and strongly-consistent database that offers up to 99.999% availability, built specifically to combine the benefits of relational database structure with non-relational horizontal scale. It is a unique database that combines ACID transactions, SQL queries, and relational structure with the scalability that you typically associate with non-relational or NoSQL databases. As a result, Spanner is best used for applications such as gaming, payment solutions, global financial ledgers, retail banking and inventory management that require ability to scale limitlessly with strong-consistency and high-availability. 

Bare Metal Solution: Provides hardware to run specialized workloads with low latency on Google Cloud. This is specifically useful if there is an Oracle database that you want to lift and shift into Google Cloud. This enables data center retirements and paves a path to modernize legacy applications. 

Non-relational databases

Non-relational databases (or NoSQL databases) store compex, unstructured data in a non-tabular form such as documents. Non-relational databases are often used when large quantities of complex and diverse data need to be organized. Unlike relational databases, they perform faster because a query doesn’t have to access several tables to deliver an answer, making them ideal for storing data that may change frequently or for applications that handle many different kinds of data. 

For example, an apparel store might have a database in which shirts have their own document containing all of their information, including size, brand, and color with room for adding more parameters later such as sleeve size, collars, and so on.

Qualities that make NoSQL databases fast:

• Eventual consistency: stores usually exhibit consistency at some later point (e.g., lazily at read time)
• Horizontal scaling, usually using hashed distributions
• Typically, they are optimized for a specific workload pattern (i.e., key-value, graph, wide-column)
• Typically, they don’t support cross shard transactions or flexible isolation modes.

Because of these properties, non-relational databases are used in applications that require large scale, reliability, availability, and frequent data changes.They can easily scale horizontally by adding more servers, unlike some relational databases, which scale vertically by increasing the machine size as the data grows. Although, some relations databases such as Cloud Spanner support scale-out and strict consistency.

Non-relational databases can store a variety of unstructured data such as documents, key-value, graphs, wide columns, and more. Here are your non-relational database options in Google Cloud: 

• Document databases: Store information as documents (in formats such as JSON and XML). For example: Firestore
• Key-value stores: Group associated data in collections with records that are identified with unique keys for easy retrieval. Key-value stores have just enough structure to mirror the value of relational databases while still preserving the benefits of NoSQL. For example: Datastore, Bigtable, Memorystore
• In-memory database: Purpose-built database that relies primarily on memory for data storage. These are designed to attain minimal response time by eliminating the need to access disks. They are ideal for applications that require microsecond response times and can have large spikes in traffic. For example: Memorystore
• Wide-column databases: Use the tabular format but allow a wide variance in how data is named and formatted in each row, even in the same table. They have some basic structure while preserving a lot of flexibility. For example: Bigtable
• Graph databases: Use graph structures to define the relationships between stored data points; useful for identifying patterns in unstructured and semi-structured information. For example: JanusGraph

There are three non-relational databases in Google Cloud:

• Firestore: Is a serverless document database which scales on demand and acts as a backend-as-a-service. It is DBaaS that increases the speed of building applications. It is perfect for all general purpose uses cases such as ecommerce, gaming, IoT and real time dashboards. With Firestore users can interact with and collaborate on live and offline data making it great for real-time application and mobile apps.  
• Cloud Bigtable: Cloud Bigtable is a sparsely populated table that can scale to billions of rows and thousands of columns, enabling you to store terabytes or even petabytes of data. It is ideal for storing very large amounts of single-keyed data with very low latency. It supports high read and write throughput at sub-millisecond latency, and it is an ideal data source for MapReduce operations. It also supports the open-source HBase API standard to easily integrate with the Apache ecosystem including HBase, Beam, Hadoop and Spark along with Google Cloud ecosystem.
• Memorystore: Memorystore is a fully managed in-memory data store service for Redis and Memcached at Google Cloud. It is best for in-memory and transient data stores and automates the complex tasks of provisioning, replication, failover, and patching so you can spend more time coding. Because it offers extremely low latency and high performance, Memorystore is great for web and mobile, gaming, leaderboard, social, chat, and news feed applications.

Conclusion

Choosing a relational or a non-relational database largely depends on the use case. Broadly, if your application requires ACID transactions and your data structure is not going to change much, select a relational database. 

In Google Cloud use Cloud SQL for any general-purpose SQL database and Cloud Spanner for large-scale globally scalable, strongly consistent use cases. In general, if your data structure may change later and if scale and availability is a bigger requirement than consistency then a non-relational database is a preferable choice.  Google Cloud offers Firestore, Memorystore, and Cloud Bigtable to support a variety of use cases across the document, key-value, and wide column database spectrum.

For more comparison resources on each database check out the overview. For more hands-on experience with Bigtable, check out our on-demand training here and learn about migrating databases to managed services check out this whitepaper.  

https://youtube.com/watch?v=2TZXSnCTd7E%3Fenablejsapi%3D1%26

For more #GCPSketchnote, follow the GitHub repo. For similar cloud content follow me on Twitter @pvergadia and keep an eye out on thecloudgirl.dev.

Today we’re announcing the HarbourBridge Schema Assistant, which provides a guided schema-design workflow for migrating from MySQL or PostgreSQL to Spanner. HarbourBridge imports dump files (from mysqldump or pg_dump) or directly connects to your source database, and converts the source database schema to an equivalent Spanner schema. The new Schema Assistant capability displays the source schema and Spanner schema side-by-side, highlights errors and walks you through a series of steps to validate and optimize your Spanner schema. It also produces a browsable assessment report with an overall migration-fitness score for Spanner, a table-by-table detailed analysis of type mappings and a list of features used in the source database that aren’t supported by Spanner. It supports editing of table and column names, column types, primary keys and constraints, as well as dropping of tables, columns, foreign keys and secondary indexes.

The new Schema Assistant complements HarbourBridge’s existing data and schema migration capabilities and is a critical step towards our goal of building a complete open-source migration toolkit. HarbourBridge continues to support command-line schema and data migration and turn-key Spanner evaluation.

Complementing the bulk data migration capabilities of HarbourBridge, we are also announcing the ability to migrate change events from MySQL to Cloud Spanner.

Supported Features in Schema Assistant

1. Global type mapping. Users can customize the global mapping for how types should be mapped to Spanner consistently across the schema. For example, mapping large integers in source schema to Spanner’s NUMERIC.
2. Local type mapping. Users can override the custom type mapping for a given table/column.
3. Session management. A session keeps track of all the changes made to the schema mapping.
4. Customization of secondary indexes. Users can add, edit and delete secondary indexes to optimize their Spanner performance.
5. Customization of foreign keys and interleaved tables. Table interleaving is an important design consideration when migrating to Cloud Spanner as explained in more detail in this blog post.

Features in the pipeline

We are already working to further expand the supported set of schema editing features and welcome your feedback. We are particularly excited to expand the Schema Assistant’s design recommendations for optimizing Spanner schemas e.g. in-depth recommendations for primary key design.

HarbourBridge is open source and we gladly accept contributions from the wider community.

Like countless other industries, farming is going digital and undergoing big changes—driven by access to more actionable information. The agriculture business can now gather and analyze georeferenced data from satellites, combined with data from IoT sensors in fields, crop rotation and yield histories, weather patterns, seed genotypes and soil composition to help increase the quantity and quality of crops.

This is essential for businesses in the agriculture industry, but it’s also critical to address growing food shortages around the world. 

At S4, we create technology to de-risk crop production. We provide customers seeking agricultural risk management solutions with the tools to make better, data-driven decisions for their crop planning, based on machine learning and proprietary algorithms.

We interpret plant evolution on a global scale with predictive modeling and analytics, and offer super-efficient risk-transferring solutions. Our multi-cloud platform includes a petabyte-scale database, an open source stack, and—after 50 proof-of-concept evaluations—BigQuery for our data warehouse and the Cloud SQL database service to handle OLTP queries to our PostgreSQL database.

These PoCs included, among others, Microsoft Azure Data Lake Analytics, IBM Netezza, Postgres/PostGIS running on IBM bare-metal servers with SATA SSDs and on Google’s Compute Engine with NVMe disks, and on-premises memSQL, CitusData and Yandex ClickHouse. 

Weeding out risk in an uncertain market

According to recent research, climate extreme events like drought, heat waves, and heavy precipitation are responsible for 18-43% of global variation in crop yields for maize, spring wheat, rice, and soybeans. This is a clear trend for other crops as well. Such variation poses risks of food shortages as well as large financial risks to farmers, insurers, and regions dependent on successful crop yields. Also, it creates vast humanitarian difficulties.

Our mission at S4 is to help de-risk crop production by matching the right data with analytics tools so farmers and other participants in the agricultural value chain can plan better, resulting in more reliable food supplies.

In a nutshell, we create indices out of biological assets. These indices measure yield losses on crops that are caused by the effects of weather and other factors, which are then used as underlying assets for products, such as swap/derivative contracts and parametric insurance policies, to transfer risk to the financial markets.

We enable insurers and lenders to buy and sell agricultural risks through the futures market. Also, our other products help farmers and seed and fertilizer companies provide customized genotype recommendations and fertilization requirements. This helps to optimize planting by geography, resources, and crop species, monitor phenological, pests and humidity evolution throughout the crop season, and estimate yields.

Local communities benefit from S4’s technology, as the ability to manage weather risks allows farmers to stabilize their cash flows, invest more to produce more with fewer risks, and develop in a more sustainable manner.

Growing data sources, reducing costs, accelerating performance

With the volume of diverse data sources and analytical complexity both growing at a very fast pace, we decided that using a major cloud services provider with a broad roadmap and global partnerships would be beneficial to S4’s future evolution.

At the same time, we wanted to bring our services to users faster and cut costs by consolidating our on-premises technology stack. When we started evaluating providers, our leading criteria included a powerful geospatial database and data analytics tools along with excellent support, all at a competitive price. GCP prevailed in nearly all criteria categories among the 50 companies we measured. 

Our previous platform architecture included a hybrid relational database that used Compute Engine for virtual machines and Cloud Storage for database backup. The RDBMS was slow. Maintaining our own data warehouse was complex and expensive.

We wanted to use machine learning and neural networks, but couldn’t do so easily and affordably. The complexity of that system meant that products or services requiring small changes or additions to the data model translated to expensive expansions of infrastructure or project time.

Also, agronomical or product teams couldn’t test these changes by themselves, always requiring the intervention on no small part of the IT team, which led to further delays.

We added GCP services like BigQuery as S4’s cloud data warehouse and use BigQuery GIS for geospatial analysis, Cloud Dataflow for simplified stream and batch data processing, and Cloud SQL for queries to the S4 database platform, which have all made a huge impact on our services and bottom line.

Database and analytics costs have decreased by 40% and customers are receiving our analytical results 25% faster. In addition, we’ve eliminated the time-consuming downloading of images, reducing storage and processing costs by 80%, because we no longer need expensive tools licenses, and have greatly reduced classification processing times.

Our customers working in the agriculture industry are also benefiting from this infrastructure change. They are now able to speed up their data analytics using our GCP-based platform.

“S4 products and technologies unlock the full potential of satellite imagery for crop prescriptions, monitoring and yield estimates,” says Nicolás Loria, Manager of Marketing Services, Southern Cone, Corteva Agriscience.

“We’ve worked with S4 for the last three (and starting year number four) crop seasons as its team capabilities, data integration capacities, and analytics insights have allowed Corteva to perform an entire new solution. Thanks to S4’s customized 360° approach, fast response and delivery times, we have safely outsourced our remote crop analytic technical needs.”

Also, this new architecture has allowed us to scale our models and databases with almost no limits, at a fraction of the cost vs. the previous models.

We’ve saved a lot of time on executing processes and reduced work needed by our internal teams to do certain tasks, like preparing images, converting them, validating results, and more. Using Google Earth Engine has decreased the execution time of daily tasks anywhere from 50% to 90% of the previous time, going from an average time of 30 minutes to between four and 15 minutes, depending on the task.

In addition to saving money and time, we are able to focus on innovation with the GCP performance and features we’re using. We’re able to seamlessly add satellite data to analytics using both public datasets and our own private data, and deliver GIS data management, analytics, crop classification and monitoring in real time.

We can do semi-automatic crop classification and classification using spectral signatures with Google Earth Engine. Later this year, we’ll be using neural networks for pattern recognition and machine learning in new applications to improve crop yields and fine-tune risk models. And using GCP and Google Earth Engine infrastructure means we can run models for customers in South America and around the world, since Google Earth Engine has global satellite imagery available. 

We’ve heard from our customer Indigo Argentina that they’re able to bring customers data insights faster.

“We are working with S4 in the development of two different applications for satellite crop monitoring and yield assessment,” says Carlos Becco, CEO, Indigo Argentina. “S4’s technology allowed us to manage and analyze multiple sources and layers of information in real time, letting us uncover valuable insights in Indigo’s own microbiome technologies, and at a very competitive cost.” 

Analytical products and app development thrive with GCP

With GCP, we are updating and improving algorithms that we built manually with machine learning processes to develop drought indices for upcoming crop seasons. Algorithms can recognize specific phases of crop phenology (e.g., bud burst, flowering, fruiting, leaf fall) and correlate them with photosynthetic activity, light, water, temperature, radiation, and plant genetics factors. Other analytical products like crop monitoring, pre-planting recommendations, financial scoring, and yield estimation can now do a lot more for users by offering multiple layers and datasets, faster image processing, and real-time access via APIs.

We also replaced our bare-metal S4 app deployment with the App Engine serverless application platform. It provides tighter integration between the S4 platform and our BigQuery data warehouse for integration with marketplaces and third-party solutions.

We get all of these Google Cloud features with all the benefits of managed cloud services, from multiversioning and security to automatic backups and high availability.

At S4, we trust technology to decode plant growth and help protect farmers and their communities from climate change. With growing food shortages due to increasing populations and intensifying weather, data and analytics can have a huge impact in lowering financial risks and improving agricultural yields. It’s one sector where cloud, database, analytics, and other technologies are combining to improve business outcomes and affect the lives of billions of people. Learn more about S4’s work and learn more about data analytics on Google Cloud.

Many people are visual learners; I am definitely one of them, and judging by the tremendous response you all showed to Google Cloud Sketchnotes on LinkedIn and Twitter, you are too.

-Priyanka Vergadia, Lead Developer Advocate, Google

“A picture is worth a thousand words”

Shortly after I started creating and sharing visual explanations of Google Cloud concepts in late 2020, I began receiving overwhelmingly positive feedback from fellow cloud architects and enthusiasts. That feedback led me to think about pulling these sketches together into a reference guide — a one-stop shop for cloud learning fun. So here it is!

🥁 Introducing “Visualizing Google Cloud: 101 Illustrated References for Cloud Engineers and Architects”

🤔 What is this book about?

This book is my way of making Google Cloud technical concepts fun and interesting via visual storytelling. It covers Google Cloud essentials from end to end, with a visual explanation of each concept, how it works, and how you can apply it with other concepts to meet the needs of your real-world use case. After reading thousands of pages of Google Cloud documentation and experimenting with virtually every Google Cloud product, I distilled that experience down to this book of accessible, bite-sized visuals.

The sketches are spread across five broad categories:

• Infrastructure, storage, and databases: Run and scale applications seamlessly
• Data analytics and machine learning: Derive actionable insights from data
• Networking: Connect, scale, secure, modernize, and optimize your infrastructure
• Application development and modernization: Build apps using containers and microservices
• Security: Protect data, users, and applications in cloud

🙋‍♀️ Who is this book for?

It is for all cloud enthusiasts. It is for anyone who is planning a cloud migration or new cloud deployment, preparing for cloud certification, or looking to make the most of Google Cloud. If you are a cloud solutions architect, an IT decision-maker, or a data and machine learning engineer, you will find this book a good starting point. In short, this book is for YOU!

When you get the book you’ll not only help yourself, you’ll also help provide meals for schoolchildren who need them. All the books’ proceeds go directly to the awesome folks at Wiley and a charity that fights malnutrition and supports the right to education.

🪜 Next Steps

I hope this book helps you on your Google Cloud journey by making it both easier and more fun. Are you ready to dive in? Order here, learn more about how the idea of the book came about here and please share your thoughts with me on Twitter or LinkedIn.