Over the past two years, the general population has become more acquainted with cryptocurrencies and the first iterations of NFTs, which were among the earliest use cases for blockchain technology. This public awareness and participation has led to a growing interest in, and demand for, Web3 technology at the enterprise level.

But building trust in a new wave of technology, especially in large organizations, doesn’t happen overnight. That is why it’s critical for Web3 technologists to bring the broader benefits, use cases, and core capabilities of blockchain to the forefront of the conversation. If businesses don’t understand how this new technology can help them, how can they prioritize it among competing tech plans and resources? And without baseline protocols that account for privacy, confidential data, and IP, how can they future-proof a business?

Answering these questions and delivering trustworthy infrastructure is exactly why Scott Purcell and I founded Fortress Web3 Technologies — to bring about the next wave of Web3 utility. The company’s goal is to provide infrastructure that eliminates barriers to Web3 adoption with RESTful APIs and widgetized services that enable businesses to quickly launch and scale their Web3 initiatives.

Our tools include embeddable wallets for NFTs and fungible rewards tokens; NFT minting engines; and core financial services. These include payments, compliance, and crypto liquidity via our wholly-owned financial institution, Fortress Trust. Being overseen by a chartered, regulated entity ensures privacy, compliance and business continuity.

Fortress chose Google Cloud to help usher in this new-wave technology because no other cloud provider is better suited to helping regulated industries get up to scale on our Web3 infrastructure and blockchain technology. I’ll get into more specifics below, but at the highest level: IPFS (the current standard distributed storage) is going to face major resistance when it comes to industries that are heavily regulated or deal in ownership rights. By leveraging Google Cloud, which has critical certifications such as HIPPA, Department of Defense, ISO, and Motion Picture, we’re striking the appropriate balance between decentralization and centralization, using the best of both technologies.

The Fortress Vault on Google Cloud is a huge and necessary step forward as the first ever NFT-database solution to protect intellectual property, confidential documents, and other electronic records. It represents the first technology that marries privately stored content with the accessibility, privacy, portability, and provenance that blockchain provides.

Understanding Non-Fungible Tokens (NFTs)

An NFT is not an expensive jpeg. From a technical point of view, an NFT is a unique key stored in a distributed and trustless ledger we call a blockchain. This blockchain token is uniquely identifiable from any other token and acts as a digital key to authenticate ownership and unlock data held in a database.

While different blockchains have adopted different standards, Ethereum standards are a good proxy to represent overall concepts. Going back to the primitives, if you read the EIP 721 proposal, metadata is explicitly optional. While today’s NFT hype has indeed leveraged that technology to monetize and distribute digital art, the potential of blockchain is in the ability to digitally represent ownership of a wide variety of different asset classes on a decentralized ledger.

Unique, non-fungible tokens are not a new concept. We use them every day in technical systems for things like authentication, database keys, idempotency, and much more. Now, thanks to blockchain technology, you can take those out of their walled gardens and into an open platform that can lead to transformational utility and applications.

Take real estate, for example. Instead of a paper-based title documenting you as the owner of your home, imagine that the title is tokenized with an NFT on a blockchain. Any platform could cryptographically verify the authenticity of that form of title along with its provenance in real time and confirm that you’re the rightful owner of that property.

But, perhaps you don’t want the title of your property visible to others, nor the associated permits, tax documents, architectural drawings, contractor lists, and other documents. Maybe you just want banks, insurance companies, and others to be able to confirm that you are indeed the owner without revealing the details of those records. The NFT metadata records immutable public-facing provenance, while the underlying data remains private and protected using Fortress Vault on Google Cloud.

Apply that same utility to other sensitive information such as medical records, intellectual property, estate documents, corporate contracts, and other confidential information and it’s easy to see how enterprises are just now exploring how to hold traditional assets as NFTs.

Fortress Vault: Intellectual Property, Confidential Documents, and Other Electronic Records

What NFTs and Web3 have been lacking is the ability to make the tokenized data accessible exclusively by the owner — and only the owner. NFTs are a digital key to unlock everything ranging from music and event tickets, to real estate deeds and healthcare records, to estate documents, and to everything in the world that’s digital.

This is why we created the Fortress Vault. When building it, we had to make a fundamental decision: Either go with a distributed and permissionless storage protocol like IPFS, filecoin, or other blockchain-based database offerings, or work with an industry-leading cloud platform that understands data integrity and is establishing itself as the leader in the space.

Ultimately, we chose Google Cloud for its industry-leading object storage, professional management, fault tolerance, and myriad of certifications for architecture and data integrity.

Some of the challenges faced when vaulting a vast variety and quantity of digital content at scale include:

• Balancing data availability versus cost of storage
• Data redundancy
• Long term archival needs
• Business continuity
• Flexibility to meet current and future needs of the rapidly evolving Web3 industry.

Google Cloud is the clear leader across all of these pain points. The object lifecycle management of Google Cloud Storage enables efficient transition between storage classes when either the data matures to a certain point or it’s updated with newer files. Content in the Fortress Vault can range from on-demand data to long-term uses, such as estate planning documents that won’t be accessed for 30 years.

When storing NFT data, robust disaster recovery is table stakes. We quickly gravitated to the automatic redundancy options and multi-region storage buckets that let us customize where we store our data without massive devops and management overhead. By leveraging Google Cloud, we can offer industry leading retention, redundancy, and integrity for our customers’ NFT content.

Working with a leader in data storage was key to making this a reality. Additionally, Google Cloud shares our vision of bringing every industry forward into the world of Web3. We are both focused on building the critical infrastructure that allows everyone from Web3 native companies to Fortune 500 brands navigate the strategic shift to blockchain technology.

Why Web3 Matters

“Web3” is shorthand for the “third wave” of the internet and the technological innovation that brought us here.

Web 1 — the earliest internet — democratized reading and access to information, opening the doors to mass communication. Web 2 expanded on that with the ability to read and “write.” It democratized publishing by letting people directly engage in producing information through blogs, social media, gaming, and contributions to collective knowledge.

Web 3 expands our technological capabilities even more with the ability to read, write, and “own.” With blockchain, we can now establish clear provenance with visibility into the origination of ownership of any tokenized asset, and we can see the chain of ownership. We can rely on this next-generation technology to track, authenticate, protect, and keep a ledger of our assets.

With the Fortress Vault on Google Cloud, we have the capability to ensure the integrity of non-public data while making it accessible via NFTs. This is a game changer for Web3 adoption, particularly in industries like music, event ticketing, gaming, finance, transportation, real estate, and healthcare. Every industry can benefit from the ability to tokenize assets on blockchain technology without leaving the trusted safety of Google Cloud data storage.

The market for NFTs is everyone. And the Fortress Vault on Google Cloud is the technology evolution that makes it possible for Web3 innovators to confidently build, launch, and scale their initiatives across every industry imaginable.

As software-defined vehicles continue to advance and the quantity of digital services grows to meet consumer demand, the data required to provide these services continue to grow as well. This makes automotive manufacturers and suppliers look for capabilities to log and analyze data, update applications, and extend commands to in-vehicle software.

The challenges the automotive sector faces can be quantified. A modern vehicle contains upwards of 70 electronic control units (ECUs), most of which are connected to one or more sensors. Not only is it now possible to exactly measure many aspects of vehicle performance, but new options become available. Using LIDAR (light detection and ranging), for example, vehicles are achieving higher levels of autonomy; this leads to a data stream from such demanding applications that may reach 25 GB per hour. For the in-vehicle processing of data, 100 million lines of software code may be present — more than a fighter jet. This in-vehicle code will have to be maintained with updates and new functionalities.

Access to the data will allow manufacturers to gain valuable insights into operational details of their vehicles. The use of this data can help to reduce costs and risks, increase ROI, support ESG initiatives, and provide valuable insights to develop innovative solutions and shorten the time to value for Electric Vehicle innovations.

Sibros’ Deep Connected Platform (DCP) makes it possible for these manufacturers to build and launch new connected vehicle use cases from production to post-sale at scale by connecting and managing all software and data throughout every life cycle stage. A key component of this platform is the Sibros Deep Logger that provides capabilities like the following:

• Full configurability of what to record, when to record it, and how fast to record it.
• High resolution timestamps of all Controller Area Network (CAN) messages.
• Dynamic application of live log configurations to receive new data points without deploying new software.

For example, properly analyzed engine data enables true predictive maintenance for the first time, which creates the option to repair or replace components before failure happens. Another example would be the evaluation of data regarding the use of certain in-car features with the goal to redesign its interior.

Two other components of the DCP are software updates and remote commands to ECUs. The DCP on Google Cloud enables seamless integration with any vehicle architecture and provides OEMs and suppliers with the platform to manage connected vehicle data at rest and in transit using a proven and secure way on a global scale.

OEMs can pull data through APIs provided by Sibros into Google Data Cloud (including BigQuery) to gain access to the rich information data sets provided by the DCP within their environment and blend this data with their first party data sets to provide value insights for their business. Some of the Connected Vehicle insights that DCP information enables are:

• Damage prevention, improved operation, or development of the next generation of engines with insights from complex analyses that could consider parameters like model, engine type, mileage, overall speed, temperature, air pressure, load, services, and more.
• The combination of electric vehicle battery usage data like charging cycles, engine performance, and battery age with contributing factors as the use of the air conditioning to determine if such factors contribute to hazardous battery conditions and for improved battery development.
• Cross-organization collaboration in R&D by the provision of information on all these metrics and more from real-world driving, like engine knock data and even tire pressure.
• Google Cloud’s unified data cloud offering provides a complete platform for building data-driven applications like those from Sibros — from simplified data ingestion, processing, and storage to powerful analytics, AI, ML, and data sharing capabilities — integrated with Google Cloud. With a diverse partner ecosystem and support for multi-cloud, open-source tools and APIs, Google Cloud provides Sibros the portability and the extensibility they need to avoid data lock-in.

“Software has an ever increasing importance in the automotive world, even more so with electric vehicles and new mobility services. Google Cloud is partnering with Sibros to bring their award winning Deep Connected Platform to deliver high frequency, low latency over-the-air software updates, data logging & diagnostics capabilities to our automotive customers, leveraging the security and scale of Google Cloud. This is revolutionizing everything from development cycles to business models and customer relationships.” — Matthias Breunig, Director, Global Automotive Solutions, Google Cloud

Through Built with BigQuery, Google Cloud is helping tech companies like Sibros build innovative applications on Google’s Data Cloud with simplified access to technology, helpful and dedicated engineering support, and joint go-to-market programs.

“Sibros is looking forward to partnering with Google Cloud, which will enable vehicle manufacturers and suppliers to reach the next level in their use of data. Sibros solutions for Deep Data Logging and Updating on the Google Data Cloud, combined with Google BigQuery, will help them to mitigate risks, reduce costs, add innovative products, and introduce value-added use cases.” — Xiaojian Huang, Chief Digital Officer, Software, Sibros

Sibros and Google Cloud are driving Connected Mobility transformation to help our customers accelerate R&D innovation, power efficient operations, and unlock software-defined vehicle use cases with a full stack connected vehicle platform. Click here to learn more about Sibros on Google Cloud.

The Home Depot, Inc., is the world’s largest home improvement retailer with annual revenue of over $151B. Delighting our customers—whether do-it-yourselfers or professionals—by providing the home improvement products, services, and equipment rentals they need, when they need them, is key to our success.

We operate more than 2,300 stores throughout the United States, Canada, and Mexico. We also have a substantial online presence through HomeDepot.com, which is one of the largest e-commerce platforms in the world in terms of revenue. The site has experienced significant growth both in traffic and revenue since the onset of Covid-19.

Because many of our customers shop at both our brick-and-mortar stores and online, we’ve embarked on a multi-year strategy to offer a shopping experience that seamlessly bridges the physical and digital worlds. To maximize value for the increasing number of online shoppers, we’ve shifted our focus from event marketing to personalized marketing, as we found it to be far more effective in improving the customer experience throughout the sales journey. This led to changing our approach to marketing content, email communications, product recommendations, and the overall website experience.

Challenge: launching a modern marketing strategy using legacy IT

For personalized marketing to be successful, we had to improve our ability to recognize a customer at the point of transaction so we could—among other things—suspend irrelevant and unnecessary advertising. Most of us have experienced the annoyance of receiving ads for something we’ve already purchased, which can degrade our perception of the brand itself. While many online retailers can identify 100% of their customer transactions due to the rich information captured during checkout, most of our transactions flow through physical stores, making this a more difficult problem to solve.

Our old legacy IT system, which ran in an on-premises data center and leveraged Hadoop, also challenged us since maintaining both the hardware and software stack required significant resources. When that system was built, personalized marketing was not a priority, so it took several days to process customer transaction data and several weeks to roll out any system changes. Further, managing and maintaining the large Hadoop cluster base presented its own set of issues in terms of quality control and reliability, as did keeping up with open-source community updates for each data processing layer.

Adopting a hybrid approach

As we worked through the challenges of our legacy system, we started thinking about what we wanted our future system to look like. Like many companies, we began with a “build vs. buy” analysis. We looked at several products on the market and determined that while each of them had their strengths, none was able to offer the complete set of features we needed.

Our project team didn’t think it made sense to build a solution from scratch, nor did we have access to the third-party data we needed. After much consideration, we decided to adopt a solution that combined a complete rewrite of the legacy system with the support of a partner to help with the customer transaction matching process.

Building the foundation on Google Cloud

We chose Google Cloud’s data platform, specifically BigQuery, Dataflow, DataProc, Cloud Storage, and Cloud Composer. Google Cloud platform empowered us to break down data silos and unify each stage of the data lifecycle from ingestion, storage, and processing to analysis and insights. Google Cloud offered best-in-class integration with open-source standards and provided the portability and extensibility we needed to make our hybrid solution work well. The open standards of BigQuery’s BQ Storage API allowed us to leverage fast BQ storage layers to be utilized with other compute platforms, e.g., DataProc.

We used BigQuery combined with Dataflow to integrate our first- and third-party data into an enterprise data and analytics data lake architecture. The system then combined previously siloed data and used BigQuery ML to create complete customer profiles spanning the entire shopping experience, both in-store and online.

Understanding the customer journey with the help of Dataflow and BigQuery

The process of developing customer profiles involves aggregating a number of first- and third-party data sources to create a 360-degree view of the customer based on both their history and intent. It starts with creating a single historical customer profile through data aggregation, deduplication, and enrichment. We used several vendors to help with customer resolution and NCOA (Change of Address) updates, which allows the profile to be house-holded and transactions to be properly reconciled to both the individual and the household. This output is then matched to different customer signals to help create an understanding of where the customer is in their journey—and how we can help.

The initial implementation used Google Dataflow, Google’s streaming analytics solution, to load data from Google Cloud Storage into BigQuery and perform all necessary transformations. The Dataflow process was converted into BQML (BigQuery Machine Learning) since this significantly reduced costs and increased visibility into data jobs. We used Google Cloud Composer, a fully managed workflow orchestration service, to help orchestrate all data operations and DataProc and Google Kubernetes Engine to enable special case data integration so we could quickly pivot and test new campaigns. The architecture diagram below shows the overall structure of our solution.

Taking full advantage of cloud-native technology

In our initial migration to Google Cloud, we moved most of our legacy processes in their original form. However, we quickly learned that this approach didn’t take full advantage of the cloud-native and more improved features Google Cloud offered such as auto scaling of resources, flexibility to decouple storage from the compute layer, and a wide variety of options to choose the best tool for the job. We refactored our Hadoop-based data pipelines written in Java-based Map Reduce and our Pig Latin jobs to Dataflow and BigQuery jobs. This dramatically reduced processing time and made our data pipeline code concise and efficient.

Previously, our legacy system processes ran longer than intended, and data was not used efficiently. Optimizing our code to be cloud-native and leveraging all the capabilities of Google Cloud services resulted in reduced run times. We decreased our data processing window from 3 days to 24 hours, improved resource usage by dramatically reducing the amount of compute we used to possess this data, and built a more streamlined system. This in turn reduced cloud costs and provided better insight. For example, DataFlow offers powerful native features to monitor data pipelines, enabling us to be more agile.

Leveraging the flexibility and speed of the cloud to improve outcomes

Today, using a continuous integration/continuous delivery (CI/CD) approach, we can deploy multiple system changes each week to further improve our ability to recognize in-store transactions. Leveraging the combined capabilities of various Google Cloud systems—BigQuery, DataFlow, Cloud Composer, Dataproc, and Cloud Storage–we drastically increased our ability to recognize transactions and can now connect over 75% of all transactions to an existing household. Further, the flexible Google Cloud environment coupled with our cloud-native application makes our team more nimble and better able to respond to emerging problems or new opportunities.

Increased speed has led to better outcomes in our ability to match transactions across all sales channels to a customer and thereby improve their experience. Before moving to Google Cloud, it took 48 to 72 hours to match customers to their transactions, but now we can do it in less than 24 hours.

Making marketing more personal—and more efficient

The ability to quickly match customers to transactions has huge implications for our downstream marketing efforts in terms of both cost and effectiveness. By knowing what a customer has purchased, we can turn off ads for products they’ve already bought or offer ads for things that support what they’ve bought recently. This helps us use our marketing dollars much more efficiently and offer an improved customer experience.

Additionally, we can now apply the analytical models developed using BQML and Vertex AI to sort customers into audiences. This allows us to more quickly identify a customer’s current project, such as remodeling a kitchen or finishing a basement, and then personalize their journey by offering them information on products and services that matter most at a given point through our various marketing channels. This provides customers with a more relevant and customized shopping journey that mirrors their individual needs.

Protecting a customer’s privacy

With this ability to better understand our customers, we also have the responsibility to ensure we have good oversight and maintain their data privacy. Google’s cloud solutions provide us the security needed to help protect our customers’ data, while also being flexible enough to allow us to support state and federal regulations, like the California Customer Privacy Act. This way we can provide a customer the personalized experience they desire without having to fear how their data is being used.

With flexible Google Cloud technology in place, The Home Depot is well positioned to compete in an industry where customers have many choices. By putting our customers’ needs first, we can stay top of mind whenever the next project comes up.

Today at Google Cloud Next we are announcing innovations that will enable data teams to simplify how they work with data and derive value from it faster. These new solutions will help organizations build modern data architectures with real-time analytics to power innovative, mission-critical, data-driven applications. 

Too often, even the best minds in data are constrained by ineffective systems and technologies. A recent study showed that only 32% of companies surveyed gained value from their data investments. Previous approaches have resulted in difficult to access, slow, unreliable, complex, and fragmented systems. 

At Google Cloud, we are committed to changing this reality by helping customers simplify their approach to data to build their data clouds. Google Cloud’s data platform is simply unmatched for speed, scale, security, and reliability for any size organization with built-in, industry-leading machine learning (ML) and artificial intelligence (AI), and an open standards-based approach.

Vertex AI and data platform services unlock rapid ML modeling 

With the launch of Vertex AI in May 2021, we empowered data scientists and engineers to build reliable, standardized AI pipelines that take advantage of the power of Google Cloud’s data pipelines. Today, we are taking this a step further with the launch of Vertex AI Workbench, a unified user experience to build and deploy ML models faster, accelerating time-to-value for data scientists and their organizations. We’ve integrated data engineering capabilities directly into the data science environment, which lets you ingest and analyze data, and deploy and manage ML models, all from a single interface.

Data scientists can now build and train models 5X faster on Vertex AI than on traditional notebooks. This is primarily enabled by integrations across data services (like Dataproc, BigQuery, Dataplex, and Looker), which significantly reduce context switching. The unified experience of Vertex AI let’s data scientists coordinate, transform, secure and monitor Machine Learning Operations (MLOps) from within a single interface, for their long-running, self-improving, and safely-managed AI services.

“As per IDC’s AI StrategiesView 2021, model development duration, scalable deployment, and model management are three of the top five challenges in scaling AI initiatives,” said Ritu Jyoti, Group Vice President, AI and Automation Research Practice at IDC. “Vertex AI Workbench provides a collaborative development environment for the entire ML workflow – connecting data services such as BigQuery and Spark on Google Cloud, to Vertex AI and MLOps services. As such, data scientists and engineers will be able to deploy and manage more models, more easily and quickly, from within one interface.”

Ecommerce company, Wayfair, has transformed its merchandising capabilities with data and AI services. “At Wayfair, data is at the center of our business. With more than 22 million products from more than 16,000 suppliers, the process of helping customers find the exact right item for their needs across our vast ecosystem presents exciting challenges,” said Matt Ferrari, Head of Ad Tech, Customer Intelligence, and Machine Learning; Engineering and Product at Wayfair. “From managing our online catalog and inventory, to building a strong logistics network, to making it easier to share product data with suppliers, we rely on services including BigQuery to ensure that we are able to access high-performance, low-maintenance data at scale. Vertex AI Workbench and Vertex AI Training accelerate our adoption of highly scalable model development and training capabilities.”

BigQuery Omni: Breaking data silos with cross-cloud analytics and governance

Businesses across a variety of industries are choosing Google Cloud to develop their data cloud strategies and better predict business outcomes — BigQuery is a key part of that solution portfolio. To address complex data management across hybrid and multicloud environments, this month we are announcing the general availability of BigQuery Omni, which allows customers to analyze data across Google Cloud, AWS, and Azure. Healthcare provider, Johnson and Johnson was able to combine data in Google Cloud and AWS S3 with BigQuery Omni without needing data to migrate. 

This flexible, fully-managed, cross-cloud analytics solution allows you to cost-effectively and securely answer questions and share results from a single pane of glass across your datasets, wherever you are. In addition to these multicloud capabilities, Dataplex will be generally available this quarter to provide an intelligent data fabric that enables you to keep your data distributed while making it securely accessible to all your analytics tools.

Spark on Google Cloud simplifies data engineering 

To help make data engineering even easier, we are announcing the general availability of Spark on Google Cloud, the world’s first autoscaling and serverless Spark service for the Google Cloud data platform. This allows data engineers, data scientists, and data analysts to use Spark from their preferred interfaces without data replication or custom integrations. Using this capability, developers can write applications and pipelines that autoscale without any manual infrastructure provisioning or tuning. This new service makes Spark a first class citizen on Google Cloud, and enables customers to get started in seconds and scale infinitely, regardless if you start in BigQuery, Dataproc, Dataplex, or Vertex AI.

Spanner meets PostgreSQL: global, relational scale with a popular interface

We’re continuing to make Cloud Spanner, our fully managed, globally scalable, relational database, available to more customers now with a PostgreSQL interface, now in preview. With this new PostgreSQL interface, enterprises can take advantage of Spanner’s unmatched global scale, 99.999% availability, and strong consistency using skills and tools from the popular PostgreSQL ecosystem. 

This interface supports Spanner’s rich feature set that uses the most popular PostgreSQL data types and SQL features to reduce the barrier to entry for building transformational applications. Using the tools and skills they already have, developer teams gain flexibility and peace of mind because the schemas and queries they build against the PostgreSQL interface can be easily ported to another Postgres environment. Complete this form to request access to the preview.

Our commitment to the PostgreSQL ecosystem has been long standing. Customers choose Cloud SQL for the flexibility to run PostgreSQL, MySQL and SQL Server workloads. Cloud SQL provides a rich extension collection, configuration flags, and open ecosystem, without the hassle of database provisioning, storage capacity management, or other time-consuming tasks.

Auto Trader has migrated approximately 65% of their Oracle footprint to Cloud SQL, which remains a strategic priority for the company. Using Cloud SQL, BigQuery, and Looker to facilitate access to data for their users, and with Cloud SQL’s fully managed services, Auto Trader’s release cadence has improved by over 140% (year-over-year), enabling an impressive peak of 458 releases to production in a single day.

Looker integrations make augmented analytics a reality

We are announcing a new integration between Tableau and Looker that will allow customers to operationalize analytics and more effectively scale their deployments with trusted, real-time data, and less maintenance for developers and administrators. Tableau customers will soon be able to leverage Looker’s semantic model, enabling new levels of data governance while democratizing access to data. They will also be able to pair their enterprise semantic layer with Tableau’s leading analytics platform. The future might be uncertain, but together with our partners we can help you plan for it. 

We remain committed to developing new ways to help organizations go beyond traditional business intelligence with Looker. In addition to innovating within Looker, we’re continuing to integrate within other parts of Google Cloud. Today, we are sharing new ways to help customers deliver trusted data experiences and leverage augmented analytics to take intelligent action. 

First, we’re enabling you to democratize access to trusted data in tools where you are already familiar. Connected Sheets already allows you to interactively explore BigQuery data in a familiar spreadsheet interface and will soon be able to leverage the governed data and business metrics in Looker’s semantic model. It will be available in preview by the end of this year. 

Another integration we’re announcing is Looker’s Solution for Contact Center AI, which helps you gain a deeper understanding and appreciation of your customers’ full journey by unlocking insights from all of your company’s first-party data, such as contextualizing support calls to make sure your most valuable customers receive the best service. 

We’re also sharing the new Looker Block for Healthcare NLP API, which provides simplified access to intelligent insights from unstructured medical text. Compatible with Fast Healthcare Interoperability Resources (FHIR), healthcare providers, payers, and pharma companies can quickly understand the context and relationships of medical concepts within the text, and in turn, can begin to link this to other clinical data sources for additional AI and ML actions. 

Bringing the best of Google together with Google Earth Engine and Google Cloud

We are thrilled to announce the preview of Google Earth Engine on Google Cloud. This launch makes Google Earth Engine’s 50+ petabyte catalog of satellite imagery and geospatial data sets available for planetary-scale analysis. Google Cloud customers will be able to integrate Earth Engine with BigQuery, Google Cloud’s ML technologies, and Google Maps Platform. This gives data teams a way to better understand how the world is changing and what actions they can take — from sustainable sourcing, to saving energy and materials costs, to understanding business risks, to serving new customer needs. 

For over a decade, Earth Engine has supported the work of researchers and NGOs from around the world, and this new integration brings the best of Google and Google Cloud together to empower enterprises to create a sustainable future for our planet and for your business.

At Google Cloud, we are deeply grateful to work with companies of all sizes, and across industries, to build their data clouds. Join my keynote session to hear how organizations are leveraging the full power of data, from databases to analytics that support decision making to AI and ML that predict and automate the future. We’ll also highlight our latest product innovations for BigQuery, Spanner, Looker, and Vertex AI.

I can’t wait to hear how you will turn data into intelligence and look forward to connecting with you.

97 zettabytes was the estimated volume of data generated worldwide in 2022¹. This sort of explosion in data volume is happening in every enterprise. Now imagine being able to access all this data you own from anywhere, at any time, analyze it, and leverage its insights to innovate your products and services? One of the biggest barriers to fulfilling this vision is the complexity inherent in dealing with data trapped across silos in an enterprise. Google Data Cloud offers a unified, open, and intelligent platform for innovation, allowing you to integrate your data on a common architectural platform. Across industries, organizations are able to reimagine their data possibilities in entirely new ways and quickly build applications that delight their customers.

Siloed data: The barrier to speed and innovation

Digital technologies ranging from transaction processing to analytics and AI/ML use data to help enterprises understand their customers better. And the pace of innovation has naturally accelerated as organizations learn, adapt, and race to build the next generation of applications and services to compete for customers and meet their needs. At Next 2022, we made a prediction that the barriers between transactional and analytical workloads will mostly disappear.

Traditionally, data architectures have separated transactional and analytical systems—and that’s for good reason. Transactional databases are optimized for fast reads and writes, and analytical databases are optimized for analyzing and aggregating large data sets. This has siloed enterprise data systems, leaving many IT teams struggling to piece together solutions. The result has been time consuming, expensive, and complicated fixes to support intelligent, data-driven applications. 

But, with the introduction of new technologies, a more time-efficient and cost-effective approach is possible. Customers now expect to see personalized recommendations and tailored experiences from their applications. With hybrid systems that support both transactional and analytical processing on the same data, without impacting performance, these systems now work together to generate timely, actionable insights that can be used to create better experiences and accelerate business outcomes.   

According to a 2022 research paper from IDC, unifying data across silos via a data cloud is the foundational capability enterprises need to gain new insights on rapidly changing conditions and to enable operational intelligence. The modern data cloud provides unified, connected, scalable, secure, extensible, and open data, analytics, and AI/ML services. In this platform, everything is connected to everything else.

Why reducing data barriers delivers more value 

The primary benefit of a unified data cloud is that it provides an intuitive and timely way to represent data and allows easy access to related data points. By unifying their data, enterprises are able to: 

1. Ingest data faster – for operational intelligence
2. Unify data across silos – for new insights
3. Share data with partners – for collaborative problem solving
4. Change forecasting models – to understand and prepare for shifting markets
5. Iterate with decision-making scenarios – to ensure agile responses
6. Train models on historical data – to build smarter applications

As generative AI applications akin to Bard, an early experiment by Google, become available in the workplace, it will be more important than ever for organizations to have a unified data landscape to holistically train and validate their proprietary large language models. 

With these benefits enterprises can accelerate their digital transformation in order to thrive in our increasingly complex digital environment. A survey of more than 800 IT leaders indicated that using a data cloud enabled them to significantly improve employee productivity, operational efficiency, innovation, and customer experience, among others. 

Build modern apps with a unified and integrated data cloud

Google Cloud technologies and capabilities reduce the friction between transactional and analytical workloads and make it easier for developers to build applications, and to glean real-time insights. Here are a few examples. 

1. AlloyDB for PostgreSQL, a fully managed PostgreSQL-compatible database, and AlloyDB Omni, the recently launched downloadable edition of AlloyDB, can analyze transactional data in real time. AlloyDB is more than four times faster for transactional workloads and up to a 100 times faster for analytical queries compared to standard PostgreSQL, according to our performance tests. This kind of performance makes AlloyDB the ideal database for hybrid transactional and analytical processing (HTAP) workloads.
2. Datastream for BigQuery, a serverless change data capture and replication service, provides simple and easy real time data replication from transactional databases like AlloyDB, PostgreSQL, MySQL and Oracle directly into BigQuery, Google Cloud’s enterprise data warehouse.
3. And, query federation with Cloud Spanner, Cloud SQL, and Cloud Bigtable, make data available right from the BigQuery console allowing customers to analyze data in real-time in transactional databases. 

Speed up deployments and lower costs

By reducing data barriers, we’re taking a fundamentally different approach that allows organizations to be more innovative, efficient, and customer-focused by providing: 

• Built-in industry leading AI and ML that helps organizations not only build improved insights, but also automate core business processes and enable deep ML-driven product innovation. 
• Best-in-class flexibility. Integration with open source standards and APIs ensures portability and extensibility to prevent lock-in. Plus, choice of deployment options means easy interoperability with existing solutions and investments.  
• The most unified data platform with the ability to manage every stage of the data lifecycle, from running operational databases to managing analytics applications across data warehouses and lakes to rich data-driven experiences. 
• Fully managed database services that free up DBA/DevOps time to focus on high-value work that is more profitable to the business. Organizations that switch from self-managed databases eliminate manual work, realize significant cost savings, reduce risk from security breaches and downtime and increase productivity and innovation. In an IDC survey, for example, Cloud SQL customers achieve an average three-year ROI of 246% because of the value and efficiencies this fully managed service delivers. 

Google Data Cloud improves the efficiency and productivity of your teams, resulting in increased innovation across your organization. This is the unified, open approach to data-driven transformation bringing unmatched speed, scale, security, and with AI built in. 

In case you missed it: Check out our Data Cloud and AI Summit that happened on March 29th, to learn more about the latest innovations across databases, data analytics, BI and AI. In addition, learn more about the value of managed database services like Cloud SQL in this IDC study.

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