Cloud Firestore is a NoSQL document database that simplifies storing, syncing, and querying data for your mobile and web apps at global scale.

Cloud Firestore is a fast, fully managed, serverless, cloud-native NoSQL document database that simplifies storing, syncing, and querying data for your mobile, web, and IoT apps at global scale.

Its client libraries provide live synchronization and offline support, while its security features and integrations with Firebase and Google Cloud Platform (GCP) accelerate building truly serverless apps.

Here’s other stuff it’s good at:

Sync data across devices, on or offline

With Cloud Firestore, your applications can be updated in near real time when data on the back end changes. This is not only great for building collaborative multi-user mobile applications, but also means you can keep your data in sync with individual users who might want to use your app from multiple devices.

With Firebase Realtime Database, we felt we had built the best force-plate testing software on the market. Thanks to Cloud Firestore, in only two weeks, we built a system that’s significantly better and includes features we never thought possible to ship on Day 1.

Chris Wales, CTO, Hawkin Dynamics

Cloud Firestore has full offline support, so you can access and make changes to your data, and those changes will be synced to the cloud when the client comes back online. Built-in offline support leverages local cache to serve and store data, so your app remains responsive regardless of network latency or internet connectivity.

Simple and effortless

Cloud Firestore’s robust client libraries make it easy for you to update and receive new data while worrying less about establishing network connections or unforeseen race conditions. It can scale effortlessly as your app grows. Cloud Firestore allows you to run sophisticated queries against your data. This gives you more flexibility in the way you structure your data and can often mean that you have to do less filtering on the client, which keeps your network calls and data usage more efficient.

Enterprise-grade, scalable NoSQL

Cloud Firestore is a fast and fully managed NoSQL cloud database. It is built to scale and takes advantage of GCP’s powerful infrastructure, with automatic horizontal scaling in and out, in response to your application’s load. Security access controls for data are built in and enable you to handle data validation via a configuration language.

Google Cloud Results

• Improves performance and reliability, enabling Pantheon to serve larger customers
• Supports 99.95% uptime across 200,000+ websites
• Reduces cloud infrastructure costs by 40%
• Enables future analytics offerings based on machine learning and big data analytics

Nearly every business needs a web presence—but the vast majority of companies don’t want to be involved in the technical aspects of coding, deployment, hosting, security, and scaling websites. To stay focused on the business value and creative aspects of their websites and avoid managing infrastructure, thousands of companies turn to Pantheon, a website operations and hosting platform that powers over 200,000 websites.

Pantheon promises its customers speed, reliability, and scalability plus world-class collaboration and workflow tools. For five years, the company was able to deliver high service levels in all three areas running its platform on bare-metal virtual cloud servers. However, as its business grew, network links began to saturate under heavy load, risking instability. As Pantheon’s business evolved to focus on servicing some of the largest websites in the world, the company wanted to partner with a more innovative cloud services provider.

“We wanted a partner that could give us what we offer to our own customers: the flexibility to scale smoothly and consume services without building them from scratch,” says David Strauss, CTO, Pantheon. “It was time to move beyond custom containers on managed VMs and extend our cloud strategy to include next-generation technologies for container management and analytics.”

Pantheon evaluated several leading cloud providers and determined that Google Cloud Platform would be the best fit for its business and customers. Engineering had the final say, running a battery of functionality and performance tests at the storage, database, and web server layers.

“In every test our engineers did, Google Cloud Platform came in as better, faster, and more cost effective than the competition,” says Niall Hayes, COO, Pantheon. “We compared MariaDB to Google Cloud SQL and Cassandra to Google Bigtable, and container density improved from 250 to 400 containers per server.”

Migrating 200,000+ sites in 2 weeks

Pantheon wanted to make the transition transparent to its customers, so a fast and smooth migration to Google Cloud Platform was essential. With help from Google, Pantheon completed the migration quickly and moved 500TB of databases, code, and files with zero customer impact.

“We migrated over 200,000 websites to Google Cloud Platform in 2 weeks, including 50,000 that are heavily trafficked and actively developed, and nobody noticed,” says Josh Koenig, Co-founder and Head of Products at Pantheon. “The speed was incredible. There was no downtime, and we filed no additional support tickets with Google during the entire process.”

The platform for platforms

For its content management system runtime environment, Pantheon runs its own homegrown container management technology on Google Compute Engine. To automate scaling for other core services such as its routing layer and distributed file system, it uses Google Kubernetes Engine for cluster management and orchestration.

“Google is the clear leader in Kubernetes and container management, which aligns very well with our open source values and our vision for the future,” says Niall. “With Google Kubernetes Engine we get better resource efficiency, and automated operations and autoscaling take a lot of administration off our plate.”

In addition to smooth scaling, Pantheon and its customers benefit from improved performance and reliability thanks to the high-quality private network offered by Google.

Further, the Google partnership with Fastly enables direct connectivity to Google Cloud Platform to improve performance for edge caching. As a result of these improvements, Pantheon raised its availability service level agreement (SLA) from 99.9% to 99.95% and can now take on even larger customers.

“Google’s network topology, both locally and globally, performs better and more reliably than competing solutions, making Google Cloud Platform the best choice for us and for our customers,” says David. “Google beats any other cloud provider as the best platform-for-platforms.”

Adds Josh: “Google has unbelievable technology around persistence and replication between zones and regions, and that is not something we could find anywhere else. This allows us to offer advanced disaster recovery and failover services to our customers.”

Strengthening customer relationships

Pantheon uses Google BigQuery, a fully managed, cloud-based data warehouse, to integrate with Fastly and analyze website traffic on behalf of its customers. Previously, Pantheon was unable to ingest edge data quickly enough from Fastly, limiting its ability to identify issues and provide the best customer service. Today, Fastly streams logs in real time into Google BigQuery for analysis, giving Pantheon a wealth of insights.

“We use Google BigQuery to identify customers that have outgrown their infrastructure or need to right-size for business growth,” says David. “We can have proactive conversations and add a lot of value to the relationships. Soon, we plan to make Google BigQuery available to our customers so they can better understand their own traffic.”

Adds Niall: “Google Cloud Platform is more data-oriented than other cloud providers, making it a better match for our needs and our customers’ strategic initiatives.”

Integrated, granular security

Pantheon appreciates that Google Cloud services are built for public cloud, with granular security as a core design and development requirement. Employees simply use their G Suite credentials to gain access to Google Cloud Platform infrastructure and services.

“We’ve been a G Suite shop for years because of the paperless collaboration benefits,” says Josh. “G Suite connects our distributed company, and it was very natural to use those same logins for Google Cloud Platform.”

Staying competitive and productive

Moving to Google Cloud Platform opens up new possibilities for services Pantheon can offer to customers in the future, including machine learning and big data analytics, to give them a more complete view of how digital experiences are driving their businesses. Internally, engineers can move faster, do more effective capacity planning, and provide better service as Pantheon moves its products upmarket.

Pantheon expected to save 20% on cloud infrastructure costs by moving to Google Cloud Platform, but was able to double that savings with resource optimization and managed services.

“Since moving to Google Cloud Platform, our platform is more secure, reliable, and scalable than ever. We reduced our cloud infrastructure costs by 40%, and our customers’ sites run 45% faster than industry benchmarks,” says Niall. “Our engineers are Google fans for a reason—they’re happier, more efficient, and more productive on Google Cloud Platform.”

In the fragmented world of U.S. healthcare, patients often have to wait in line or on hold, navigate multiple patient portals, and fill out numerous request forms—all in pursuit of their own medical history. Healthcare technology startup PicnicHealth is on a mission to put control back with the patient, where it belongs.

PicnicHealth’s growth, from closing successful venture rounds to winning machine learning (ML) competitions, speaks to not only improvements and opportunities in healthcare, but also how startups are leveraging Google Workspace and Google Cloud services to accelerate momentum.

The company does the heavy lifting of collecting records and leverages human-in-the-loop ML to transcribe and validate them with an abstraction team of medical professionals. The records are then structured into a complete medical history that patients can access and share with providers to get better care.

But PicnicHealth helps to improve patient health on more than one front. It allows patients to contribute their data to de-identified medical research, building high-quality, anonymized datasets that researchers and life sciences companies can use to better understand disease progression and treatment in the real world.

Google Workspace has been part of PicnicHealth from day one, helping the founders collaborate and shape the company’s vision using cloud-synced documents and spreadsheets to collaborate and model predictions. “I’ve had a Gmail account since 2006, and in college 100% of people worked out of Google Docs. Workspace continues to be the best choice for online collaboration, and that’s why it’s still the default standard for startups,” said Troy Astorino, CoFounder & CTO of PicnicHealth. “When we started PicnicHealth, my co-founder Noga Leviner was in San Francisco and I was in Southern California, and of course we used Workspace.”

Today, Google Workspace continues to play a central role in the company’s collaboration. “We create design documents in Google Docs, primarily for engineering and product changes, and get really healthy, vibrant discussions through comments,” noted Astorino. “This practice has grown beyond engineering and is used for everything from how the company operates to communication norms.”

Google Workspace offers everything the team needs to collaborate, no matter where employees are. PicnicHealth’s team has spread from San Francisco to being distributed across the country and around the world. Instant collaboration is crucial.

“We work in a complex domain where people need a lot of information to make good decisions,” said Astorino. “Google Workspace allows us to operate in a mode of default transparency, where people can easily get the information they need even if it wasn’t intentionally or directly shared with them. Whether it’s working in Docs or scheduling in Calendar, we can operate much more effectively than we could otherwise.”

By any measure, PicnicHealth’s trajectory is one of record success. The startup is a 2014 alumni of Y Combinator, a program that helped launch household names like Airbnb, DoorDash, and Dropbox. Three years later, the team went on to win the $1 million grand prize at Google’s Machine Learning Competition. And the momentum has continued— PicnicHealth has recently announced a $60 million Series C round, bringing the amount raised to date to over $100 million. With the Series C, PicnicHealth is investing in expanding its reach to more patients across over 30 diseases.

As a healthcare startup, PicnicHealth faced a very particular set of challenges, especially when working with and accessing data. Data fragmentation and interoperability are only some of the challenges of realizing the value of big data in the cloud. The healthcare industry is notoriously difficult to navigate due to sensitive data protection laws and regulations like the Health Insurance Portability and Accountability Act (HIPAA).

PicnicHealth started in the cloud on Amazon Web Services (AWS). However, after migrating over to Kubernetes and facing an expanding list of requirements for HIPAA compliance, the company started to explore alternatives.

“We needed to be HIPAA compliant, which was going to be painful on AWS, and we wanted to get away from managing and operating our own Kubernetes clusters,” recalled Astorino. “We had heard good things about GKE (Google Kubernetes Engine). And particularly valuable for us, — many technical requirements you need for HIPAA compliance are configured by default on Google Cloud.”

PicnicHealth would have had to implement a lot of changes and get specialized instance types to get their existing configuration to work. So, they began experimenting with Google Cloud and discovered a much smoother experience.

“It was a lot easier to manage in terms of product setup and developer experience,” said Astorino. “There is a sane product hierarchy of resources you can access and use through Google Cloud and the relationships between them, from coordinated IAM (identity and access management) to using Google Groups for granting permissions. Overall, it’s cleaner.”

Astorino added that the move has also opened the doors to taking advantage of other services in the Google Cloud ecosystem like Cloud SQL, BigQuery, and Cloud Composer. PicnicHealth also uses Security Command Center because it easily integrates with everything but also helps meet various compliance frameworks’ requirements, providing visibility, near-real-time asset discovery, and security information and event management.

But most importantly, the integrated ecosystem has simplified the work needed for PicnicHealth to create a secure environment for employees to use when working with sensitive medical records while still providing all the tools they need. For example, abstractors not only use Google Workspace but also have Chromebooks because they are easy to manage and secure.

Altogether, Google Cloud helps form a technology stack that has enabled the startup to build a massive labeled dataset containing over 100 million labeled medical data concepts. In turn, it accelerates PicnicHealth’s ability to generate highly-performant AI models and feed other ML pipelines, which has been vital for processing and reviewing data at scale.

To learn more about how Google Workspace and Google Cloud help startups like PicnicHealth accelerate their journey, visit our startups solutions pages for Google Workspace and Google Cloud.

High Performance Computing (HPC) is prevalent today across many industries, including financial services, life sciences, higher education research, manufacturing, and energy. More and more businesses are deploying HPC workloads in the cloud to take advantage of its elasticity, scalability, and availability. Job schedulers are critical for HPC applications given the nature of these workloads that create, process, and tear down thousands, and sometimes millions, of vCPU and network resources with TB to PB of storage capacity. Job scheduling tools lead to improved operational efficiency and a degree of certainty that a particular HPC job, which can run for hours to weeks, will complete successfully. 

IBM Spectrum LSF is used extensively in the manufacturing and semiconductor industry to manage Electronic Design Automation (EDA) workloads. Dynamically running workloads on-premises and in the cloud, also known as cloud bursting, is becoming a more common practice to address capacity and provisioning time constraints within data centers and enable enterprises to take advantage of virtually unlimited resources. 

However, the challenge with cloud bursting is integrating and maintaining operational consistency across on-premises and cloud environments. IBM Spectrum LSF, in combination with Google Cloud, addresses this problem head on. 

Google Cloud is excited to announce, in collaboration with IBM, enhanced capabilities to IBM Spectrum LSF that enables organizations to integrate their on-premises job scheduling scripts with resources deployed in Google Cloud. Customers are now able to fully leverage Google Cloud’s highly scalable and secure Compute Engine, networking and storage infrastructure. 

The LSF-Google Cloud resource connector patch supports key Google Cloud differentiators including Local SSDs, GCE instance templates, Preemptible VMs, and more: 

• Bulk API support– Deploy large fleets of VM instances in a matter of seconds.
• Instance Templates – Simplify VM configuration by creating reusable templates. 
• All Machine Types – Supports all GCE VM families and machine types, including Custom Machine Types.
• GPUs – Attach up to 16 GPUs per instance, including the largest A2 instances with up to 16 NVIDIA A100 GPUs
• Preemptible VMs – Preemptible VMs are provisioned from excess Compute Engine capacity and is a significant way to save money on GCE resources.
• Local SSD – Attach up to 9TB of NVMe SSD per instance
• Hyperthreading – Supports the “threads-per-core” option in GCE, which allows per-VM hypervisor level Hyperthread configuration (when supported by Instance Templates)
• Images – Supports custom disk images, including full support for Windows, for all attached Persistent Disks
• Placement Policies – Control where the instances are physically located relative to each other within a zone for improved low-latency performance
• Labels – Supports GCE Labels, which can be used for management of firewall rules, tracking billing, etc
• Minimum CPU Platform – Supports the ability to specify a minimum CPU Platform for your Virtual Machines.

Getting Started

This improvement to the IBM LSF Resource Connector was developed by IBM in coordination with Google. Find out more about the new supported features and their operation in the official IBM Spectrum LSF Resource Connector Documentation. You can also find additional documentation and download the software in the IBM Spectrum Computing Community. If you have further questions, you can contact IBM, or Google Cloud Sales.

Special thanks to Annie Ma-Weaver, Mark Mims, and Wyatt Gorman for their contributions.

The Home Depot (THD) is the world’s largest home-improvement chain, growing to more than 2,200 stores and 700,000 products in four decades. Much of that success was driven through the analysis of data. This included developing sales forecasts, replenishing inventory through the supply chain network, and providing timely performance scorecards.

However, to compete in today’s business world, THD has taken this data-driven approach to an entirely new level of success on Google Cloud, providing capabilities not practical on legacy technologies.

The pressures of contemporary growth that drove much of the work are familiar to many businesses. In addition to everything it was doing, THD needed to better integrate the complexities in its related businesses, like tool rental and home services. It needed to better empower teams, including a fast-growing data analysis staff and store associates with mobile computing devices. It wanted to better use online commerce and artificial intelligence to meet customer needs, while maintaining better security.

Even before addressing these new challenges, THD’s existing on-premises data warehouse was under stress as more data was required for analytics and data analysts were utilizing the data with increasingly complex use cases. This drove rapid growth of the data warehouse, but also created constant challenges for the team in managing priorities, performance, and cost.

In order to add capacity to the environment, it was a major planning, architecture, and testing effort. In one case, adding on-premises capacity took six months of planning and a three-day service outage. Within a year, capacity was again scarce, impacting performance and ability to execute all the reporting and analytics workloads required. The capacity refresh cycles were shrinking, and the expecations for data were growing. There had to be a better way.

Still, THD did not take its move to the cloud lightly. A large-scale enterprise data warehouse migration involves tremendous effort among people, process, and technology. After careful consideration, THD chose Google Cloud’s BigQuery for its cloud enterprise data warehouse.

BigQuery, a scalable serverless data warehouse, was better on cost, infrastructure agility, and analytics capability, driving better insights with improved performance. There are no service interruptions when capacity is added, and that capacity can be added within a week (and soon same day). It doesn’t require complex system administration, and its standard SQL support means people can easily ramp up quickly. Valuable BigQuery products like Identity and Access Management meant THD could create many separate Google Cloud projects, while ensuring that different teams weren’t interfering with each other or accessing protected data.

THD also utilizes BigQuery’s flat-rate monthly pricing model that allows teams to budget their capacity based on need and provides billing predictability. The capacity not being used by a given project is available for enterprise use. This ensures no surprises when the monthly bill arrives and provides all analytical users access to significant computing power.

While THD’s legacy data warehouse contained 450 terabytes of data, the BigQuery enterprise data warehouse has over 15 petabytes. That means better decision-making by utilizing new datasets like website clickstream data and by analyzing additional years of data.

As for performance, look at this chart:

With the cloud EDW migration complete, and the legacy on-premises data warehouse retired, analysts now execute more complex and demanding workloads that they would not have been able to complete before, such as utilizing Datalab for orchestrating analytics through Python Notebooks, utilizing BigQuery ML for machine learning directly against the BigQuery data (no movement of large datasets), and AutoML to help determine the best model for predictions.

Additionally, engineers at THD have adapted BigQuery to monitor, analyze, and act on application performance data across all its stores and warehouses in real time, something that was not practical in the on-premises system.

With over 600 projects that THD now has on Google Cloud, the BigQuery story is just one of the many ways that Google Cloud is working with THD to deliver meaningful business results, every day.