New to Cloud Firestore? Here are Some Basics You Need to Know

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New to Cloud Firestore? Here are Some Basics You Need to Know

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New to databases? Here are some basics to ease your journey before getting started with Cloud Firestore. In case you are already familiar with relational and non-relational databases, you can jump to questions about key terms for using Firestore.

Learning to use a new database can be daunting, even more so if you don’t already have technical knowledge about databases. In this article, I will break down some database basics, terms you should know, what Firestore is, how it works, how it stores data, and how to get started using it with the assumption that you don’t have any existing database knowledge.

Before we dive into what Cloud Firestore is, let’s discuss some key database terms you should know. Feel free to skip this section if you are already familiar with the basics of Relational and non-relational databases.

What is a database?

A database is software that allows you to easily access, manage, modify, update, control and organize data. The way you want to store information can impact what type of database you choose. There are two major categories of databases, Relational and non-relational.

Relational Database

A relational database can be thought of like a spreadsheet. You can store information in your spreadsheet like this:

Now, what happens if I want to store information about where Sparrow1 lives, in my spreadsheet, but I don’t care about where the other birds live? I would have to add another column to my spreadsheet, called home, that would only contain data for the sparrow. That would look like this:

Even though I only want to know information about where the sparrow lives, I am required to have blank spaces in the column for all of the other animals. This is because in a relational database, you have a specific structure of your data called a schema. Just like in a spreadsheet, every item you are storing information on must have a place to put information about the bird’s home, even if you only want that information for one bird. This is enforced by the schema, which is essentially the column headers you put in the sheet and dictates a strict structure for the data, which has pros and cons.

The strict structure of a relational database allows your application to know what kind of data exists, to know what the data type is, and to enforce rules such as requiring data to be unique, or enforcing type of data stored etc. A schema, by design, forces the data in each row to have the same characteristics, which means it is not very flexible, unless you change the schema for the database. That means if you want to add different data that doesn’t fit your existing schema, you have to change the schema. As we discussed above, if you want to change the schema we are using to store information, such as Home, there is some information that will be stored for all rows, even if you don’t want to store anything. The amount of wasted storage is different between database engines, data types etc. Another thing to consider about Relational databases is that at scale, some traditional Relational databases will require more advanced deployments to handle the scale.

Changing the schema of a relational database can be highly disruptive, especially for busy workloads because it requires running scripts to change the schema and coordinating it carefully with the code changes in the app. Due to locking, you might even experience downtime in some cases. Now contrast that with a non-relational document database like Firestore, where you don’t have to worry about schema changes in the databases or downtime as a result of it.

Also, when you have a lot of data that you want to collect and it only applies to a few things in your database, having extra space with no information in it can become wasteful because it uses up storage space in many cases. A non-relational database can help get around this problem.

Non-relational databases

Generally speaking, a non-relational database stores information in a different format than a Relational database. There are 4 major categories of non-relational databases that you will hear most frequently.

Column-Family
Document (Firestore)
Key-Value
Graph

Since this post is focusing on Firestore, in this section we will dive into what a document database is, how it is used, and when to use it.

Document database (Firestore)

A document database can be thought of as a multi layered collection of entities, such as this:

As you can see, when the list is all collapsed, you can only see the information at the top; in this case, that is the BirdID (Cardinal1, Bluejay1, Sparrow1, Cardinal2, Crow1 etc. When I open the list I see “word: word”. For example, the document ID Sparrow1, points to a document with “Type: Sparrow”. I also see “Color: grey”, “Age: 2”, “Gender: f” and “Home: Birdhouse #3”

This is known as a key value pair. For “Type: Sparrow”, Type is the key and Sparrow is the value. All of the keys in the Sparrow1 document are: Type, Color, Age, Gender, House. All of the values in the Sparrow1 document are: Sparrow, grey, 2, f, Birdhouse #3.

Similarly to how the key gives you context, it allows you to ask the computer for a specific piece of information, such as the age of the bird. It is important to decide on a specific key term you will use for each piece of data you collect so your data can be easily read programmatically. This is called an implicit schema, an implied understanding of how data is stored that is not enforced by the database. Let’s go over what happens when we use an implicit schema.

Under Cardinal1, you see Type, Color, Age, and Gender; however, under Sparrow1 you also see House. This is possible because in a non-relational database you don’t have a schema that requires you to store the same information about every bird in your database; instead, you can store the specific information that you need for each bird, regardless of what is stored for other birds. This is a great benefit in terms of flexibility, but because of this flexibility, maintaining standard naming conventions is very important.

Now, let’s discuss why using standard naming conventions is so important. In the example above, if I ask a human: “What is the age of Cardinal1?”, they would probably tell me 2. If I asked them: “What is the Age of Bluejay1?”, they would probably tell me 4. These are both correct answers, but they are only correct because a human is able to assume what Age means. A computer, on the other hand, can’t make assumptions. If I ask a computer: “What is the Age of Cardinal1?” it would say 2, but if I ask it: “What is the Age of Bluejay1?” it would not know. This is because the computer is looking for the keyword Age and it isn’t able to use any context clues to determine what other words might mean Age. However, if I asked the computer: “What is the BirdAge of Bluejay1?”, the computer would tell me 4. Why do I care that I need to tell the computer to look for BirdAge to get the age of blujay one, but to look for Age to get the age of Cardinal one? I care because it means I would have to write two entirely different sets of instructions (i.e software code) to get the age of Cardinal1 and the age of Bluejay1 if I am not careful in how I structure my data. But when I structure my data well, this is not an issue and is infact a benefit by adding added flexibility.

What we see from this example, is that even without a strict schema, we can (and should) define conventions for document formats. If conventions aren’t defined, things can get unwieldy quickly.

How information is accessed

Now, let’s discuss how the information is accessed. If I wanted to know information about which birds are blue in our drop down list example, I would need to expand every section of the list to check if the bird is blue or not. As you can imagine, once you start to get a lot of birds in your database, it becomes cumbersome to open every drop down and see if the bird is blue. Luckily, Firestore lets you run these types of queries against the data (See more here) and receive all the documents that satisfy your conditions. On the other hand, if I wanted to know all of the information about Cardinal1, I could just open the drop down for Cardinal1 and I would have all of the information about that bird.

Now let’s start using some Firestore specific terminology. For the example we just discussed:Collections

In Firestore, your data lives in collections. You can think of collections as tabs in a spreadsheet.
Collections can be used to organize data. For example, if I decide that I want to collect data about birds and fish, the data about birds could be put in a birds collection, and the data about fish could be put in a Fish collection. ex:

Documents

This is the unit of storage that Firestore uses. In our example, each bird is its own document. Documents reside in collections. This is what one document would contain:

Each Document corresponds to a row in the sheet. The following diagram demonstrates that each column header maps to a property name in the document and that each value in a row maps to a value in the document.
Each document must be identified by a unique identifier. In our example, that is BirdID. Notice that the value for BirdID is stored at the top level of the list, so when the document is closed, you can only see Cardinal1 and Cardinal1 is not also stored within the document.

References

All documents can be uniquely identified by their location. Let’s think through this in words first before we move to code. If I want to tell someone to get data about the sparrow from the drop down lists, I would need to tell them:
In the bird drop down list, can you please get all the information under Sparrow1 and put it on a piece of paper called sparrow1Info?
Now let’s try that again using Firestore terms.
From the birds collection, can you please get the document for sparrow1 from the Firestore database (db) and save it as sparrow1Info?
Now let’s try it in code.
var sparrow1Info = db.collection(‘birds’).doc(‘sparrow1’);

Subcollections

A subcollection is a collection associated with a document. Using our example of the drop down list, we can add a collection called sightings that stores documents about each sighting of the specific bird. This is what that would look like:

It is important to note that you don’t need to have the same subcollections on all documents. For example, Cardinal1 can be the only document that has a subcollection of Sightings.

How to search on Google about Firestore

The hardest part of learning a new technology can often be knowing the right terms to put into Google search to get the answers you are looking for. Here are some key terms that can help you get started

Your question:

How should I arrange my data to store it in Firestore?

Search:

Document database implicit schema design

Your Question:

What other databases are similar to firestore?

Search:

What are some document databases

Your Question:

How do I get all documents in the Birds collection?

Search:

How to use wildcards in Firestore

What next?

Try this guide to get started building your first application that uses firestore: https://firebase.google.com/docs/firestore/quickstart

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Can Your Data Warehouse Handle a 100-Trillion Row Query?

Today’s enterprise demands from data go far beyond the capabilities of traditional data warehousing and for many leaders, the need to digitally transform their businesses is a key driver for data analytics spending.

Businesses want to make real-time decisions from fresh information as well as make future predictions from their data in order to remain competitive.

In this video, Jordan Tigani, Director of Product Management, Google BigQuery reveals the power of Google Cloud’s modern data warehouse, BigQuery, that helps businesses make informed decisions quickly.

In addition, he talks about how big Google BigQuery can get. He shares examples of how one customer ran a query against a giant table of 100 trillion rows. “I think it was something like 19 petabytes of data scanned. It took about took about 20 minutes. It used 39,000 slots, which is about 20,000 cores,” says Tigani.

He also shares examples of how businesses, such as online retailer, Zulily generate real business benefits from being able to query large datasets faster, and more easily than ever–without having to invest time managing infrastructure.

Finally, Amir Aryanpour, Technical Architect, Channel 4, talks abouut how connecting connecting Google BigQuery to other solutions with the Google Cloud Platform, including storage, data visualisation, and a sentiment analysis engine, among others, helped the company.

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GCP Launches Datastream, A Serverless Change Data Capture and Replication Service

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Explore GCP's Datastream, a serverless change data capture (CDC) and replication service that allows enterprises to synchronize data across databases, storage systems, and applications reliably and with minimal latency. The brand new service helps enterprises ease database replication and take advantage of the serverless architecture to create visibility into the shift in the data volume in real-time, allowing teams to focus on delivering timely insights instead of managing infrastructure. Read on further before you get started.

Today, we’re announcing Datastream, a serverless change data capture (CDC) and replication service, available now in preview. Datastream allows enterprises to synchronize data across heterogeneous databases, storage systems, and applications reliably and with minimal latency to support real-time analytics, database replication, and event-driven architectures. You can now easily and seamlessly deliver change streams from Oracle and MySQL databases into Google Cloud services such as BigQuery, Cloud SQL, Google Cloud Storage, and Cloud Spanner, saving time and resources and ensuring your data is accurate and up-to-date.

*Datastream provides an integrated solution for CDC replication use cases with custom sources and destinations**Check the documentation page for all supported sources and destinations.

“Global companies are demanding change data capture to provide replication capabilities across disparate data sources, and provide a real-time source of streaming data for real-time analytics and business operations,” says Stewart Bond, Director, Data Integration and Intelligence Software Research at IDC.

However, companies are finding it difficult to realize these capabilities because commonly used data replication offerings are costly, cumbersome to set up, and require significant management and monitoring overhead to run flexibly or at scale. This leaves customers with a difficult-to-maintain and fragmented architecture.

Datastream’s differentiated approach

Datastream is taking on these challenges with a differentiated approach. Its serverless architecture seamlessly and transparently scales up or down as data volumes shift in real time, freeing teams to focus on delivering up-to-date insights instead of managing infrastructure. It also provides the streamlined customer experience, ease of use, and security that our customers have come to expect from Google Cloud, with private connectivity options built into the guided setup experience.

Datastream integrates with purpose-built and extensible Dataflow templates to pull the change streams written to Cloud Storage, and create up-to-date replicated tables in BigQuery for analytics. It also leverages Dataflow templates to replicate and synchronize databases into Cloud SQL or Cloud Spanner for database migrations and hybrid cloud configurations.

Datastream also powers a Google-native Oracle connector in Cloud Data Fusion’s new replication feature for easy ETL/ELT pipelining. And by delivering change streams directly into Cloud Storage, customers can leverage Datastream to implement modern, event-driven architectures.

Customers tell us about the benefits they’ve found using Datastream. That includes Schnuck Markets, Inc., “Leveraging Datastream, we’ve been able to replicate data from our on-premises databases to BigQuery reliably and with little impact to our production workloads. This new method replaced our batch processing and allowed for insights to be leveraged from BigQuery quicker,” says Caleb Carr, principal technologist from Schnuck Markets. “Furthermore, implementing Datastream removed the need for our analytics group to reference on-premises databases to do their work and support our business users.”

Cogeco Communications, Inc. used Datastream to also realize the value of low-latency data access. “Datastream unlocked new customer interaction opportunities not previously possible by enabling low-latency access in BigQuery to our operational Oracle data.” says Jean-Lou Dupont, Senior Director, Enterprise Architecture, Cogeco Communications, Inc. “This streamlined integration process brings data from hundreds of disparate Oracle tables into a unified data hub. Datastream enabled us to achieve this with 10X time and effort efficiency.”

In addition, Major League Baseball (MLB) used Datastream’s replication capabilities to migrate their data from Oracle to Cloud SQL for PostgreSQL. “As we’re modernizing our applications, replicating the database data reliably out of Oracle and into Cloud SQL for PostgreSQL is a critical component of that process,” says Shawn O’Rourke, manager of technology at MLB. “Using Datastream’s CDC capabilities, we were able to replicate our database securely and with low latency, resulting in minimal downtime to our application. We can now standardize on this process and repeat it for our next databases, regardless of scale.”

Our partner HCL has worked with many organizations looking to get more out of their data and plan for the future. “HCL customers across every industry are looking for ways to extract more value out of their vast amounts of data,” says Siva G. Subramanian, Global Head for Data & Analytics at HCL Google Business Unit. “CDC plays a big part in the solutions we offer to our customers using Google Cloud. Datastream enables us to deliver a secure and reliable solution to our customers that’s easy to set up and maintain. CDC is a key and integrated part of Google Cloud Data Solutions.”

“Google Cloud’s new CDC offering, Datastream, is a differentiator for Google among hyperscale cloud service providers, by supporting replication of data from Oracle and MySQL databases into the Google Cloud environment using a serverless cloud-native architecture, which removes the burden of infrastructure management for organizations, and provides elastic scalability to handle real-time workloads,” says Stewart Bond, Director, Data Integration and Intelligence Software Research at IDC.

Datastream under the hood

Datastream reads CDC events (inserts, updates, and deletes) from source databases, and writes those events with minimal latency to a data destination. It leverages the fact that each database source has its own CDC log—for MySQL it’s the binlog, for Oracle it’s LogMiner—which it uses for its own internal replication and consistency purposes. Using Google-native, agentless, high-scale log reader technology, Datastream can quickly and efficiently generate change streams populated by events based on the database’s CDC log while minimizing performance impact on the source database.

Each generated event includes the entire row of data from the database, with the data type and value of each column. The original source data types, whether it’s, for example, an Oracle NUMBER type or a MySQL NUMERIC type, are normalized into Datastream unified types. The unified types represent a lossless superset of all possible source types, and the normalization means data from different sources can easily be processed and queried downstream in a source-agnostic way. Should a downstream system need to know the original source data type, it can perform a quick API call to Datastream’s Schema Registry, which stores up-to-date, versioned schemas for every data source. This also allows for in-flight downstream schema drift resolution as source database schemas change.

The generated streams of events, referred to as “change streams,” are then written as files, either in JSON or Avro format during preview or in other formats like Parquet in the future, into a Cloud Storage bucket organized by source table and event times. Files are rotated as table schemas change, so events in a single file always have the same schema, as well as on a configurable file size or rotation frequency setting. This way customers can find the best balance between the speed of data availability and the file size that makes the most sense for their business use case.

Through its integration with Dataflow, Datastream powers up-to-date, replicated tables for analytics over BigQuery, and for data replication and synchronization to Cloud SQL and Spanner. Datastream refers to these constantly updated tables as “materialized views.” They are kept up-to-date via Dataflow template-based upserts into Cloud SQL or Spanner, or through consolidations into BigQuery. The consolidations, performed as part of the Dataflow template, take the change streams that are written into a log table in BigQuery, and push those changes into a final table, which mirrors the table from the source.

gcp datastream.jpg — *Datastream normalizes change streams into Cloud Storage, utilizing Dataflow for up to date materialized views.*

Datastream offers a variety of secure connectivity methods to sources, so your data is always safe in transit. And with its serverless architecture, Datastream can scale up and down readers and processing power to seamlessly keep up with the speed of data and ensure minimal latency end to end. As data volumes decrease, Datastream automatically scales back down—the result is a “pay for what you use” pricing model, where you never have to pay for idle machines or worry about bottlenecks and delays during data peaks.

Get started with Datastream

Datastream, now available in preview, supports streaming change data from Oracle and MySQL sources, hosted either on-premises or in the cloud, into Cloud Storage. You can start streaming your data today for $2 per GB of data processed by Datastream.

To get started, head over to the Datastream area of your Google Cloud console, under Big Data, and click Create Stream. There you can:

Initiate stream creation, and see what actions you need to take to set up your source and destination for successful streaming.
Define your source and destination, whose connectivity information is saved as connection profiles you can re-use for other streams. Sources support multiple connectivity options, with both private and public connectivity options to suit your business needs.
Select the source data you’d like to stream, and which you’d like to exclude.
Test your stream to ensure it will be successful when you’re ready to go.

Start your stream and your database’s CDC data will start to flow to your Cloud Storage bucket! From there you can integrate with Dataflow templates to load data into BigQuery, Spanner, or Cloud SQL. Datastream’s preview is supported in us-central1, europe-west1, and asia-east1, with additional regions coming soon.https://www.youtube.com/embed/FZG4w4Vbj38?enablejsapi=1&

Datastream will become generally available later this year, and will soon expand its support to also include PostgreSQL and SQL Server as sources, as well as out-of-the-box integration with BigQuery for easy delivery of up-to-date replicated tables for analytics, and message queues like Pub/Sub for real-time change stream access.

For more resources to help get you started with change streaming, check out the Datastream documentation.

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Start-up Paves Way for More Inclusive Clinical Research: Honoring Black Founders of Acclinate with Google Cloud

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February is Black history month, honoring the contributions by Black Americans. Acclinate, a research start-up uses Google Cloud to grow business and drive inclusion in clinical research. Read their journey of scaling platform on Google Cloud.

Editor’s note: February is Black History Month—a time for us to come together to celebrate the diverse set of experiences, perspectives and identities that make up the Black experience. Over the next few weeks, we will highlight Black-led startups and how they use Google Cloud to grow their businesses. Today’s feature highlights Acclinate and its founders, Del and Tiffany.

As patients, as caregivers, and as parents taking our own children to the doctor, we want recommended medications to be safe and effective. It’s a right everyone deserves.

It’s known that certain medications don’t work in the same way in all populations. For example, Albuterol, a medication often prescribed for asthma, is less effective in 67% of all Puerto Ricans and 47% of Black Americans. These problems—which can have deadly consequences—result from historically limited diversity in pharmaceutical clinical trials.

We founded our startup Acclinate to integrate culture and technology to achieve more inclusive clinical research. Help pharmaceutical companies and healthcare organizations access and engage communities of color so research is more inclusive.

Bridging the health equity gap by building trust

It’s important that health research organizations access and engage communities of color so their efforts reflect all the people they serve. Take a disease like diabetes, which affects a significantly higher proportion of Black Americans. When you look at even recent clinical trials for diabetic drugs, the representation of Black Americans among participants is only in the low single digits, despite comprising 13 percent of the U.S. population and more than 40 percent of diabetes patients in this country. Industry leaders have been aware of the lack of diversity issue, but some have chosen to ignore it or brush it aside. The biggest problem, in our opinion, is that there have been no penalties for not achieving higher diversity figures in clinical trials, and only minor financial repercussions to pharma/biotech companies when their treatments either do not work across all groups once approved, or there is a lack of uptake by all groups due to the lack of testing in those groups. The lack of clinical trial diversity has adversely impacted the reputation of the industry and the ability to recruit diverse populations in the future.

Acclinate integrates culture and technology to promote diverse patient representation in medical research. Our approach is not transactional. We build trust through our #NOWINCLUDED community, which is an ongoing, ever-expanding digital platform that educates and engages with communities of color on health issues.

#NOWINCLUDED includes a website app, and social media presence where members can learn information about diseases, particularly those with greater negative impacts on people of color, such as cancer, diabetes, and cardiovascular diseases. Members can share stories and ask questions. By providing access to trusted resources about these health issues and the latest clinical research, we empower Black people to take control of their health and consider participating in research that is shaping the future of healthcare.

For healthcare-related organizations, we offer the opportunity to better understand the attitudes, aspirations, and unmet needs of underrepresented minority communities. Data from #NOWINCLUDED feeds our HIPAA-compliant SaaS platform, e-DICT™ (Enhanced Diversity in Clinical Trials), which uses predictive analytics and machine learning to identify individuals matching the requirements and most likely to be receptive to participation in a particular clinical trial.

Acclinate scales its platform with Google Cloud

We rely on Google Cloud services, including Vertex AI, to know whom to ask, when to ask, and how to ask for clinical trial participation. With Vertex AI, we enjoy a unified platform for developing our artificial intelligence models, including tools for preparing and storing our datasets. We can easily train and compare models using AutoML, which requires minimal ML expertise or effort with its intuitive graphical interface. This allows us to leverage more than ten ordinal and categorical data points to determine in real time a community member’s likelihood to enroll, which we call our Participation Probability Index (PPI). Our models evolve in an iterative process the more we interact with, and learn about, our community members.

We follow the pay-per-use Google Cloud Platform architecture model using serverless technology, which helps reduce infrastructure management costs and lets us focus on product development and engaging with communities across the U.S.

CloudSQL, a fully managed relational database service, integrates easily with BigQuery so we can glean insights for our clients in real time, all with Google Cloud’s robust security, governance, and reliability controls. Virtual Private Cloud (VPC) gives us scalable and flexible networking for our cloud-based resources and services. We also use Identity and Access Management (IAM) to simplify oversight of Google Cloud resource permissions for different user groups and roles, with appropriate security protections.

API Gateway manages our APIs using Cloud Functions, which both use consumption-based pricing, plus give our developers consistent and highly secure access to our services through a well-defined REST API. We use Memorystore for Redis to reduce platform latency. This is done with a fully managed service powered by the Redis in-memory data store, which builds application caches for fast data access. All of this comes together to provide an outstanding experience for our platform’s users and contributors.

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Expanding influence with the Google for Startups Accelerator for Black Founders

Three months of one-on-one Google support and mentorship as part of the most recent Google for Startups Accelerator : Black Founders cohort not only helped us build our product, but also helped us earn external credibility. People use Google every day, so whether we’re trying to engage in conversations with industry experts or with somebody in a rural community, it is helpful to have the buy-in of a globally-recognized brand as we take on a historically difficult, systemic issue with challenges around trust. Getting access to the products, best practices, and people we need to build and grow through the Accelerator program has been priceless. For example, working with the Google AdWords team helped us generate important traffic from people interested in learning more about #NOWINCLUDED or sharing their story with us. Jason Scott, who leads the Google for Startups Accelerator: Black Founders program, is still connecting us to people in his network and identifying key opportunities for us months after the program wrapped. He continues to demonstrate that he is invested in seeing us succeed.

Our company has made great progress against our goals, in part thanks to receiving capital from the Google for Startups Black Founders Fund. We received $100K in non-dilutive funding along with Google Cloud credits, Google.org Ads grants, and hands-on support. We used the funds to pay for the transition and development costs associated with moving to Google Cloud. The Google support and accountability has been incredible. After receiving the Google for Startups Black Founders Fund award, we’ve gone on to raise another $1M and moved our cloud from Salesforce to Google Cloud.

We also had the amazing opportunity to be selected as one of six companies to take part in a face-to-face web conference with Sundar Pichai, Google’s CEO. We were thrilled to hear him explain his vision around health equity and the role Google plays. Ultimately, for us, it’s not just about the funding we get, but we are also gratified to receive support from an entity that truly believes in addressing this issue. We know Google is aligned with our mission of health and racial equity.

Championing diversity in clinical trials

Our 2022 looks bright. We expanded our presence to Washington, D.C. as part of the Johnson & Johnson Innovation JLABS ecosystem. We were also selected to take part in the BLUE KNIGHT initiative created between Johnson & Johnson Innovation and the Biomedical Advanced Research and Development Authority (BARDA) under the U.S. Department of Health and Human Services.

Acclinate is also on track to have contracts with five of the top 25 largest biopharmaceutical companies in the U.S this year. They’ve taken note, as has the Food and Drug Administration (FDA), that the lack of diversity in clinical trials represents a significant health concern—to the extent that the FDA has provided strong guidance for pharmaceutical companies to diversify their clinical trials. At the same time, the industry is also responding to pressure from communities of people of color to make equitable representation a priority.

Today, we are in the fortunate but challenging position to have significant inbound opportunities coming our way. In response, we continue to recruit and hire talented people to join our team. On the technology side, we are happy to be aligned with Google Cloud to have powerful cloud infrastructure that will scale with us, as well as high-caliber champions united in partnership. With people’s lives at stake, we are passionate in our commitment to helping ensure medications do what they are supposed to do: heal and improve the quality of life for everyone who takes them.

Hear Acclinate cofounders Del Smith and Tiffany Whitlow chat with Google’s Head of Startup Developer Ecosystem Jason Scott and fellow Black Founders Fund recipient Bobby Bryant about building on Google Cloud in a recent Google for Startups Instagram Live.

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

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Why Data Cloud Matters for Business Transformation

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Google's data cloud is a combo of data expertise and software solutions to help harness the value of data at the right time. Read to learn how data cloud enables secure data unification to break silos and increase agility for business transformation.

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

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

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

Enter the data cloud

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

Why a data cloud is essential

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

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

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

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

First, Google delivers insights at planet scale

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

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

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

Third, Google is the open data platform

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

Fourth, Google offers a trusted platform for your data needs

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

Learn more at Data Cloud Summit

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

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

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