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.

Over the last year, COVID-19 presented unforeseen challenges for practically every type of business and organization—including schools, colleges, and universities. For educational institutions, the pandemic was an unapologetic agent of acceleration, shifting one billion learners from in-person to online learning within two months. 

The rapid transition to online learning exposed many schools’ lack of readiness for the new online learning environment. It also widened the learning equity gap for students, with fewer than 40% of students from low-income families having access to the tools required for remote learning.

For those who do have online access, today’s students expect everything from engaging and collaborative digital learning experiences to skills-based training for their roles in the future workforce. Expectations are also high for 24×7 multi-channel tech support across all learning devices, applications, and platforms.  

In these remarkable times, education technology companies have an important role to play in supporting academic institutions and students. Indeed, this is already happening, as the EdTech (Educational Technology) market is nearly tripling, with total global expenditures expected to reach $404 billion by 2025. However, the success of these EdTech companies depends on their performance in a number of areas, including:

• Content and products: How quickly can they generate new content and react to learner needs with new products to additional markets for broader adoption?
• Personalization: How effectively can they leverage artificial intelligence (AI) to provide a personalized experience to all types of learners?
• Trust and security: How trusted and secure are their services when educational organizations are suffering the highest number of data breaches since 2005?

Here are a few examples of how EdTech companies are successfully using AI and analytics to capture this opportunity and transform their businesses:

• Build better products: iSchoolConnect is an online platform that lets students explore schools, courses, and countries where they might study, and makes higher education admissions accessible to students around the globe. The company leverages AI services to help educational institutions optimize their academic operations by accelerating admission processing by greater than 90%, while saving significant costs.
• Launch in new markets faster: Classroom creativity tools provider Book Creator uses AI APIs to enhance accessibility and improve the learner experience. “The broad suite of intelligent APIs enables us to deliver richer experiences, faster and more easily, without having to be experts in machine learning, drawing recognition, map embeds, or other areas,” says VP of engineering Thom Leggett.
• Scale businesses securely: Using DevOps and CDN [content delivery network] services, Chrome browser recording extension creator Screencastify was able to support eight times growth in users overnight amid the COVID-19 pandemic, while maintaining consistent total cost of ownership. These technologies helped the company rapidly scale operations in response to the overnight increase in demand from consumers and assure student data privacy and security on a budget. “We know this is just the beginning, as more educators rely on technology to deliver richer, more interactive curricula to students,” says CEO James Francis. 
• Provide personalized learning and support: Smart analytics and AI can provide personalized support and recommendations for students, forecast demand, and predict shifts in learners’ preferences. Online learning platform Mindvalley uses cloud-based tools to understand and make decisions based on user activity and leverage machine learning to predict behavior. 

Google Cloud is partnering with many of these leading EdTech companies, as well as industry-leading consortiums like Ed-Fi and Unizin, to standardize educational common data models and best practices for more agile and cost-effective integration of EdTech into existing environments.

The education landscape is changing rapidly, and EdTech has a major role to play as institutions adapt to the massive shift in learners’ preferences and expectations. We’re committed to empowering EdTech companies with the tools and services they need to help expand learning for everyone, anywhere. 

Watch our Spotlight session with EdTechX to learn more.

BigQuery offers strong query performance, but it is also a complex distributed system with many internal and external factors that can affect query speed. When your queries are running slower than expected or are slower than prior runs, understanding what happened can be a challenge.

The query execution graph provides an intuitive interface for inspecting query execution details. By using it, you can review the query plan information in graphical format for any query, whether running or completed.

You can also use the query execution graph to get performance insights for queries. Performance insights provide best-effort suggestions to help you improve query performance. Since query performance is multi-faceted, performance insights might only provide a partial picture of the overall query performance.

Execution graph

When BigQuery executes a query job, it converts the declarative SQL statement into a graph of execution, broken up into a series of query stages, which themselves are composed of more granular sets of execution steps. The query execution graph provides a visual representation of the execution stages and shows the corresponding metrics. Not all stages are made equal. Some are more expensive and time consuming than others. The execution graph provides toggles for highlighting critical stages, which makes it easier to spot the potential performance bottlenecks in the query.

Query performance insights

In addition to the detailed execution graph BigQuery also provides specific insights on possible factors that might be slowing query performance.

Slot contention

When you run a query, BigQuery attempts to break up the work needed by your query into tasks. A task is a single slice of data that is input into and output from a stage. A single slot picks up a task and executes that slice of data for the stage. Ideally, BigQuery slots execute tasks in parallel to achieve high performance. Slot contention occurs when your query has many tasks ready for slots to start executing, but BigQuery can’t get enough available slots to execute them.

Insufficient shuffle quota

Before running your query, BigQuery breaks up your query’s logic into stages. BigQuery slots execute the tasks for each stage. When a slot completes the execution of a stage’s tasks, it stores the intermediate results in shuffle. Subsequent stages in your query read data from shuffle to continue your query’s execution. Insufficient shuffle quota occurs when you have more data that needs to get written to shuffle than you have shuffle capacity.

Data input scale change

Getting this performance insight indicates that your query is reading at least 50% more data for a given input table than the last time you ran the query and hence experiencing query slowness. You can use table change history to see if the size of any of the tables used in the query has recently increased.

What’s next?

We continue to work on improving the visualization of the graph. We are working on adding additional metrics to each step and adding more performance insights that will make query diagnosis significantly easier. We are just getting started.

Investment management is a heavily data-driven industry—portfolio managers and investment researchers require a large number of data sources to guide them in shaping their investment strategies. 

New cloud capabilities and technologies enable investment managers to process data faster than ever before and iterate on ideas quickly to fuel innovation in the signal generation process and gain a competitive edge. 

Using the cloud for investment research workflows makes it easier to onboard data from data providers, spin up large compute workloads in the midst of market volatility or during heavy research cycles, and manage complex machine learning or natural language workflows to gain market insights.

We hear from industry leaders that they’re exploring new ways to run investment research. “Differentiated investment strategies require new types of information sources, and new ways to process that information,” David Easthope, senior analyst, Market Structure and Technology, Greenwich Associates. “And that, of course, relies heavily on having access to reliable and scalable storage, computational, and AI / ML resources. More specifically, quantitative strategies can benefit from the computational platforms and embedded AI/ML capabilities the cloud can offer.” 

Google Cloud gives investment managers essential components to work and operate faster as they bring their investment research workflows to the cloud. Here are the key highlights:

1. Simplify, speed up your data acquisition, discovery, and analytics

The foundation of any investment strategy starts with data—acquiring it, detecting patterns, and analyzing it for insights. Enabling data providers to easily share large datasets such as tick history within a high-performance analytics engine can greatly reduce the data engineering overhead when possible.

Once data is onboarded, you can tag business and technical metadata related to your datasets and provide portfolio managers the ability to discover these datasets via a search interface.

We further review analytics options for various scenarios, including aggregating massive datasets, creating dashboards, and incorporating streaming analytics workloads.

2. Take advantage of burst compute workloads

Data engineers and researchers require ready access to burst compute capabilities to perform backtesting, portfolio simulations and run risk calculations. Cloud works well for these workloads due to its elasticity, consumption-based models, and hardware evolution.

Many investment managers are shifting to a container-based strategy along with a Kubernetes-based scheduler for greater consistency, scaling and efficiency in environments with a large number of researchers. Cloud managed services and a rich suite of CI/CD tools can make this vision a reality while improving security and developer productivity.

3. Tackle machine learning (ML) and model deployment with the help from cloud

Quantitative researchers scour vast amounts of market and alternative data sources searching for signals and correlations, while ML engineers have the challenge of taking these signals and moving them to production.

Google Cloud empowers users to create and operationalize their models without wasting valuable time with a comprehensive set of MLOps tools. 

In this paper, we explore multiple solutions for ML and model deployment. Those capabilities reduce the amount of time operationalizing ML models, so quants and data scientists have more time to devote to differentiating activities. 

4. Get the data you need in less time with Natural Language and Document AI

Thousands of financial filings, news articles, and sell-side research reports are generated every day, and it’s difficult for humans alone to process this volume of information. These documents are often generated in many languages and the ability to do entity recognition, sentiment or syntactical analysis in those languages, or perhaps translate them into the language of the portfolio manager is of critical importance. Google Cloud provides these capabilities through pre-trained models, or allows you to train high-quality models with your own datasets.

Getting started

There are plenty of emerging technologies, tools, and approaches available to help investment managers today. At Google Cloud, we can help you access, organize, and utilize these essential components to make your research faster, reliable, and more valuable.

To learn more about these four keys to better investment research, check out our whitepaper for more.