The ecommerce playing field has been hard to navigate for most retailers, and Cart.com is on a mission to change that. Traditionally, retailers needing to run their online store, order fulfillment, customer service, marketing, and other essential activities have had to cobble together systems to get the capabilities they need – much less having access to analytics across these functions. The result is costly, siloed ecommerce operations that are difficult to manage and scale.

It’s clearly not a formula for success, yet that’s the reality facing most retailers. Cart.com, in contrast, has set out to democratize ecommerce by giving brands of all sizes the full capabilities they need to take on the world’s largest online retailers. Our end-to-end environment empowers retailers to keep more of their revenue, set up proven strategies for managing all aspects of their business, and act on valuable insights from customer data every step of the way.

Together with our talented team, we’re building a unified ecommerce platform that already provides value to many leading or up and coming brands including Whataburger, GUESS, Dr. Scholl’s, Rowing Blazers, and Howler Bros. 

We’re excited about the opportunity ahead as we reimagine traditional approaches to online sales, fulfillment, marketing, accessing growth capital, providing a unified view of all ecommerce and marketing analytics, and other activities. Expectations for Cart.com are high, and we are building a company that can scale to $100B in revenue and beyond. Supported by the Startup Program by Google Cloud and Google Cloud solutions, we’re establishing a technology platform to transform all aspects of ecommerce for brands worldwide. 

Partner in disruption

At Cart.com, we’re currently targeting an underserved market. Our ideal customer is beyond demonstrating product-market-fit and is now at an inflection point seeking a growth opportunity. Typically, those companies are generating between $1M and $100M in annual revenue. We’ve seen an enthusiastic response from brands and retailers as well as investors, with backing from investors in just over a year totaling $143 million in three funding rounds.

Our strategy is to build an integrated ecommerce model that combines best-of-breed solutions, many of which we gain through acquisitions and then build upon to provide a streamlined and fully integrated experience for our brands. We’ve made seven acquisitions so far to round out our online store, order fulfillment, marketing services, customer service, and we have launched some integral partnerships including easy access to growth capital through our relationship with Clearco and product protection for customers on every purchase with Extend. Instead of acquiring a data company, we’re building our data platform on Google Cloud, across each operating function for a single-view for brands to harness actionable data. We see Google Cloud as the leader for data management, analytics, machine learning (ML) and artificial intelligence (AI).

Other reasons why we’re building our business on Google Cloud include scalability, excellence, security, reach, and data analytics that are far superior to other environments.

We also feel a cultural and mission alignment with Google Cloud and envision leaning into a long-term partnership of marketing, selling, and disrupting the disruptors together. Equally important to us are the investments Google Cloud is willing to make in early-stage companies like ours. The support through the Google Cloud for Startups program has been outstanding.

Built on Google Cloud

A wide range of Google Cloud solutions provide the foundation for our platform. For instance, Cloud Pub/Sub keeps our services communicating with one another. We rely on fully managed relational databases, like Cloud SQL and Cloud Spanner, to securely handle the huge volume of brand and shopper data generated every day.

Cloud Run allowed us to develop inside of containers before our Kubernetes infrastructure was ready to go. Now, we are taking advantage of all the capabilities in Google Kubernetes Engine. BigQuery integrates with all Google Cloud solutions and offers true data streaming natively out of the box, along with Dataflow for advanced analytics. We also use Container Registry to store and manage our Docker container images. Right now, we’re testing Cloud Composer to evaluate using it for data workflow orchestration instead of Apache Airflow.

The openness of the Google Cloud environment is further enabled by Anthos, which we may deploy soon to perform data integrations quickly as we acquire more companies over the next year. For example, if we acquire a company using Azure, we can easily align it with our Google Cloud ecosystem.

Enabling ecommerce 2.0

Recently, our team has been experimenting with Google Cloud Vertex AI and the fully managed services of AI deployment and ML operations. The capabilities would save us substantial time in the management of the ML lifecycle which allows us to focus more on developing proprietary AI that will transform commerce at scale.

Because Google Cloud is so far ahead in data science, our teams benefit from deep Google Cloud expertise as we look to provide brands with unmatched insights into customers to improve services and revenue. We’re also planning to test Recommendations AI among other tools to deploy customer product recommendations and personalization as turnkey productized offerings. Moving forward, we will likely use Bigtable to aid in serving machine learning to hundreds of thousands of brands due to its low latency and scalability.

Fanatical about brand success

We know that our work with Google Cloud for Startups and use of Google Cloud solutions for best-in-class data management, analytics, ML, and AI will enable us to offer even more transformative services to brands.

We also see the opportunity to use our platform and customer insights to break down barriers between brands, enabling retailers to share information and work better together when it’s in their best interests. What we’re building today on Google Cloud is fundamentally changing what’s possible for retailers of any size everywhere. 

As a startup, when recruiting talent or working with prospective customers, it helps to share our success with Google Cloud. We view them as an extension of the Cart.com team. It also validates our business as we continue building a more integrated, holistic approach to commerce that opens new opportunities and drives growth for brands worldwide.

For more details about Cart.com’s vision for unified ecommerce, check out our video.

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.

At Google Cloud, we develop our database services to meet the needs of enterprise teams wherever they are in their cloud journey. That is why we announce launched Cloud SQL for SQL Server and make it available to all of our customers.

This addition to our database lineup means you can migrate your enterprise SQL Server workloads to Google Cloud easily, and take advantage of fully managed services.  

Cloud SQL for SQL Server is a key component when onboarding your existing applications and infrastructure to get the benefits of a fully managed and compatible database that will reduce your operational costs and overhead.

Highlights of Cloud SQL for SQL Server

Cloud SQL for SQL Server brings some key benefits that can help you run workloads easily. 

• Compatibility: Cloud SQL for SQL Server offers multiple editions of the current version of SQL Server and works with popular clients such as SQL Server Management Studio.
• Flexible backups: Schedule automatic daily backups or run them on-demand.
• Scalability: Enable the automatic storage increase configuration and Cloud SQL will add storage capacity whenever you approach your limit. Easily scale up your customized machines’ memory and processor cores as necessary.
• Built-in high availability: Cloud SQL for SQL Server has built-in high availability enabled for all editions that synchronously replicates data to each zone’s regional persistent disk.

Cloud SQL for SQL Server is currently available regions and will integrate with our existing Cloud SQL functionality, such as connectivity via the Cloud SQL Proxy.   Here’s a look at creating a new instance.

Early adopters of Cloud SQL for SQL Server have been using the service talk about the value for onboarding more workloads.

“Cloud SQL for SQL Server was very easy and fast to get up and going.” says Andrew P. Toi, database engineer lead at advertising management software company WideOrbit Inc. “With built-in high availability that can be used across editions, I can reduce the cost and overhead of managing databases dramatically, especially for smaller workloads” 

And desktop virtualization infrastructure company Itopia had this to say. “Cloud SQL Server dramatically reduces the complexity and cost involved in deploying cloud desktop infrastructure for our customers,” says Ubaldo Don, CTO of Itopia. “It turns a bulky pillar of infrastructure into a one-click service, reducing IT service management overhead.” 

As a product with a long history in the database ecosystem, SQL Server offers numerous native capabilities that help provide scalability and security to its users.  However, it can be time consuming and complex to take advantage of these features. Google Cloud SQL for SQL Server saves your team time by eliminating much of the unnecessary toil (OS patching, version upgrades, replica setup etc.) while still allowing you to leverage the functionality you’re used to. Three new features for Cloud SQL for SQL Server take its functionality even further. 

A few months ago, we announced Active Directory (AD) integration had entered preview; now, it is generally available. Equally exciting, we are releasing Cross-Region Replicas (based on SQL Server’s Always On Availability Groups) in preview.  Finally, you can try out this great new functionality in our managed database service with the latest release of SQL Server 2019, which is now generally available.  

Simple and Secure Windows Authentication with Active Directory

As one of the most requested and critical security capabilities for Cloud SQL for SQL Server, we are pleased to now provide Windows Authentication via Managed Service for Microsoft Active Directory as generally available. Customers should feel confident onboarding their business critical production workloads to the managed service while still maintaining the authentication best practices they rely on today.  While identities can be created and managed directly within the managed AD service, many customers choose to establish a trust relationship with their existing on-prem AD footprint to leverage existing identity objects.

What is Cross-Region Replica for SQL Server?

Bringing parity in the Cloud SQL portfolio alongside MySQL and PostgreSQL, Cross-region replica makes it easy to create a fully managed read replica in a different region than that of the primary instance. You can create a replica in any Google Cloud region.  The difference for SQL Server is the Availability Group based architecture that paves the way for the service to continue to offer more core compatibility with the SQL Server features our customers depend on. Cloud SQL greatly simplifies the traditional process of provisioning Availability Groups and streamlines it into a few-step workflow.

Using read replicas will allow you to horizontally scale your read workloads. For example, you can configure a reporting dashboard to work against a read replica, and because it’s only reading, it will not affect the primary instance. You can also promote replicas to be Cloud SQL instances and that could help you reduce your recovery point objective (RPO) and recovery time objective (RTO). It can help you with the RPO because the data is constantly replicated and the replica is probably more up to date than your latest backup. It can help you with RTO because promoting the replica, especially in an automated way, is a relatively short process. To get started, check out the documentation for Cross-Region Replica

What’s new in SQL Server 2019?

Providing the most current major and minor versions is a key aspect of maintaining compatibility and security for your database workload. Cloud SQL provides an easy provisioning experience that will now allow you to select from four editions of SQL Server 2019 similar to our current SQL Server 2017 options of Enterprise, Standard, Web, and Express. A few key considerations as you are evaluating the new version should be:

• Compatibility level – A newly created database on a Cloud SQL for SQL Server 2019 Databases instance has a compatibility level of 150 by default.  
• Accelerated Database Recovery – Allows instances to reduce the availability impact of restarts and shutdowns.
• TempDB changes – While we recently provided you more control to manage your tempdb files, 2019 also brings optimization to improve performance as well.
• Intelligent query processing – SQL Server 2019 provides direct improvements to the query engine itself which may improve overall query processing and performance.
• Many other performance improvements – capabilities such as verbose truncation warnings, resumable index build, and others.  Learn more about supported features here.

To get started, check out documentation for  SQL Server 2019. 

In conclusion

These three features have been the most common requests from our enterprise customers. Finally, you can bring your own Active Directory domain for SQL Server authentication and authorization, use the latest features from SQL Server 2019 and scale your read workloads as well as leveraging the cross regional replicas for faster disaster-recovery.

To get started, check out the documentation for Cross-Region Replica, Active Directory, and SQL Server 2019. All are available with any new instance created via the console or API, simply follow the instructions in the documentation.

Daffodil Software is an India-based information technology services company with 180 employees and satellite offices in the US, Singapore, and the United Arab Emirates.

Its product: A suite of web-based applications, including a business customer relationship management (CRM) program and an enterprise resource planning (ERP) app for schools.

Daffodil’s Challenge

Daffodil needed a cloud-based hosting platform for its applications that would save money and time over in-house hosting. The company tried Amazon Elastic Cloud Compute (EC2) with MySQL to manage the databases that its applications depended on. The potential was there, but building, testing and deploying the applications took too long, and scalability was also an issue.

“The old system provided the option to scale up if many users accessed our applications at the same time, but we had to do this manually,” says Gaurav Sharma, assistant head of Daffodil’s product team.

Soon, Daffodil switched to Google App Engine, which allows businesses to build and host web apps on the same infrastructure that powers Google applications.

Integration with App Engine lets Sharma and his team quickly build, test and deploy applications and allows them to automatically accommodate fluctuations in the number of users so they don’t have to manually manage the system when demand increases.

Although App Engine’s built-in Datastore was useful, they found that they wanted more SQL-like functionality for their data-driven programs.

Google’s Solution

At Google’s suggestion, Daffodil migrated to Cloud SQL three months later to take advantage of the system’s database indexing and dynamic filtering abilities, which speed up data sorting for Daffodil’s end users.

Besides indexing and dynamic filtering, Cloud SQL has simplified database management for the Daffodil team. Sharma can easily import their databases and administer them through an intuitive interface, while Google handles backend maintenance and administration.

Google Cloud SQL replicates data among multiple geographic regions, giving Daffodil CEO Yogesh Agarwal peace of mind that users’ data held by the applications is protected.

“The safety of our customers’ data is a major concern, so we appreciate that we don’t need to worry about data loss,” he says.

The Results

With App Engine and Cloud SQL, the Daffodil team is saving about 80 hours of development time every month because of easier deployment, no active management for scaling, and simplified database management. They have also slashed the time it takes to bring new products to market.

“Using Google App Engine and Google Cloud SQL make our applications go live in half the time and have provided us with hassle-free control over all processes, such as development, deployment and monitoring,” Agarwal says. “We’re able to deploy our applications much faster and spend less time managing them.”

Going forward, the Daffodil team plans to add other Google services to their applications, including Prediction API, which allows CRM application users to better predict the probability of a lead becoming a customer, and App Engine’s Full Text Search API, which helps users retrieve customer data more easily.

Agarwal is happy with the move to App Engine and Cloud SQL. “The shift has allowed us to focus on making our applications even better,” he says.

Enterprises are struggling to make high quality data easily discoverable and accessible for analytics, across multiple silos, to a growing number of people and tools within their organization. They are often forced to make tradeoffs—to move and duplicate data across silos to enable diverse analytics use cases or leave their data distributed but limit the agility of decisions.  

Today we are excited to announce Dataplex, an intelligent data fabric that provides a way to centrally manage, monitor, and govern your data across data lakes, data warehouses and data marts, and make this data securely accessible to a variety of analytics and data science tools.  

Dataplex provides an integrated analytics experience, bringing together the best of Google Cloud and open source tools, so you can rapidly curate, secure, integrate, and analyze data at scale. With built-in data intelligence using Google Artificial Intelligence (AI) and machine learning (ML) capabilities and a flexible consumption model, you can now spend less time wrestling with infrastructure and more time focused on driving business outcomes.

Dataplex enables you to:

• Achieve freedom of choice to store data wherever you want for the right price/performance and choose the best analytics tools for the job, including Google Cloud and open source analytics technologies such as Apache Spark and Presto.
• Enforce consistent controls across your data to ensure unified security and governance
• Take advantage of the built-in data intelligence using Google’s best in class AI/ML capabilities to automate much of the manual toil around data management and get access to higher quality data.

Early customers like Equifax, Loblaw, and ANZ are excited about using Dataplex to address data management complexity.

“Dataplex will greatly simplify the existing analytics workflows within Equifax with its unified data fabric and single interface for policy management and governance across all our analytics data. Its built-in data discovery and data quality features will ensure that our data scientists and analysts always have access to high quality data that they can trust. Dataplex aligns well with our enterprise data strategy and we are excited to partner with Google Cloud on this.”
—Kumar Menon, SVP, Data Fabric & Decision Science Technology, Equifax.

“Loblaw is Canada’s food and pharmacy leader, and we are excited to be an early adopter of Dataplex. We could significantly benefit from Dataplex as it provides a single pane of glass for end-to-end data management and governance. We are particularly interested in improving platform resilience and data quality by detecting anomalies as early as possible in the data pipeline with the help of Dataplex.”
—Elton Martins, Senior Director of Data Insights & Analytics, Loblaw

“We are undergoing a major data transformation at ANZ, bringing together our various data assets and building a cohesive data ecosystem for customer benefit. Dataplex’s vision and capabilities align well with our current data strategy to build a unified data fabric for all our analytics and AI/ML use cases. We are excited to partner with GCP on Dataplex and test the product in private preview.”
—Ashish Shekhar, Head of Technology – Enterprise Analytics & Applied AI, ANZ 

Dataplex is built for distributed data. We are starting with data stored in Google Cloud Storage and BigQuery, with support for other data sources coming soon. It provides a workflow-driven experience helping you build an open data platform and make data easily accessible to your end users while ensuring your policies and best practices are consistently enforced.

Organizing and curating your data

One of the core tenets of Dataplex is letting you organize and manage your data in a way that makes sense for your business, without data movement or duplication. For that, we are providing logical constructs like lakes, data zones and assets. Those constructs enable you to abstract away the underlying storage systems and become the foundation for setting policies around data access, security, lifecycle management, and so on. 

For example, you can create a lake per department within your organization (Retail, Sales, Finance, etc.) and create data zones that map to data readiness and usage (landing, raw, curated_data_analytics, curated_data_science, etc.). 

Once you have your lakes and zones setup, you can now attach data to these zones as assets. You can add data from different types of storage (e.g. GCS Bucket and BigQuery dataset) under the same zone. You can also attach data across multiple projects under the same zone.

You can ingest data into your lakes and zones using the tools of your choice including services such as Dataflow, Data Fusion, Dataproc, Pub/Sub or choose from one of our partner products. Dataplex provides built-in 1-click templates for common data management tasks. 

Securing your data 

Dataplex enables you to define and enforce consistent policies across your data, irrespective of where it physically resides. Data owners can easily set up policies for specific data domains based on business needs, without thinking about where the data is stored while data stewards get global visibility into governance policies and permissions across their data. 

You can apply security and governance policies for your entire lake, a specific zone, or an asset. Dataplex maps the policies to the underlying storage and pushes down permissions to the storage layer to provide end-to-end secure data access. Additionally, you can secure not just data but also related artifacts like notebooks, scripts, and models using the same set of access policies.

Making high quality data available for analytics and data science

One of the biggest differentiators for Dataplex is our data intelligence capabilities using Google’s best in class AI/ML technologies.  As you bring the data under management, Dataplex will automatically harvest the metadata for both structured and unstructured data, with built-in data quality checks. All of the metadata is automatically registered in a unified metastore, and made available for search and discovery. It is also published to Bigquery, Dataproc Metastore, and Data Catalog – ensuring that you have the same consistent data context and access across your tools.

For example, when you write parquet files to a Google Cloud Storage bucket – Dataplex will automatically extract metadata of these files, detect a tabular schema, including hive-style partitions, run data quality checks, and make this data queryable in BigQuery as an external table and from any open source or partner application – with the same consistent security and access policies you defined at the logical data layer. 

Your data scientists and analysts now have secured access to this data that meets your quality bar and governance rules via the tools of their choice, without needing any additional processing.

One-click access to collaborative analytics

Dataplex provides fully managed, one-click analytics environments enabling you to use the power of Apache Spark and BigQuery with support for other engines coming in the future. 

As data administrators, you now have the flexibility to pre-configure these environments with the right cost and financial governance measures without taking on the overhead of managing and maintaining the infrastructure required to power these environments. You can easily configure different environments for different types of workloads and share it with multiple users using their IAM credentials. Dataplex manages the provisioning, monitoring, scaling, and shutdown of these environments. 

As data scientists, analysts, and engineers, you now have a turn-key experience to run your analysis using notebooks and a SQL workbench. You can search for notebooks and scripts alongside data, save and share your work with other users, and schedule your notebooks or scripts for recurring workloads – all using the same integrated experience within Dataplex.

Building an open platform with industry leaders

We are partnering with industry leaders such as Accenture, Collibra, Confluent, Informatica, HCL, Starburst, NVIDIA, Trifacta, and others to build an open platform to power analytics at scale. Our partners are excited about the capabilities that Dataplex will provide:

“Collibra is excited to partner with Dataplex to provide data governance and data quality for consistent controls across distributed data. Pairing Collibra’s multi-cloud and hybrid solution with Dataplex allows enterprises to securely open up access to more, higher quality data for users and analytics using a single unified view.”
—Jim Cushman, Chief Product Officer, Collibra

“Dataplex builds on Google Cloud’s commitment to open source by integrating with Apache Kafka®, a leading open source platform for event streaming. We at Confluent, the platform for data in motion that completes Apache Kafka® to be enterprise-ready, are excited to partner with Dataplex to enable customers to bring together distributed, real-time data and build a unified data fabric for end to end analytics.”
—Paul Mac Farland, VP, Head of Customer Solutions and Innovation, Confluent

“We are excited to partner with Google Cloud’s Dataplex team as we look to provide our joint customers an integrated and open data fabric for analytics at scale. Extending Dataplex’s data management and data quality capabilities with Starburst Enterprise will accelerate time to value for enterprises looking to connect distributed data without having to move data.”
—Justin Borgman, CEO and Co-founder, Starburst

Next Steps

Dataplex is now available in preview for a select number of customers. For more information, visit our website or watch the recording. If you would like to sign up, please click here.