They run containers on Google Cloud. Why is that important and why should you care?

Containers are at the heart of modern applications. According to the 2022 State of DevOps report, containers have been the number one deployment target for primary services and applications for the past two years, and adoption continues to accelerate rapidly. 

Nowhere is this more evident than in the retail industry. In a world where improving inventory margins and operational efficiency is a top priority, containers allow retail organizations’ IT teams to do just that. 

Google pioneered container technologies and Google Cloud is optimized for containers. The flexibility and velocity gained by running containers in Google Cloud have been essential for retailers in the application modernization journey. Here are three retailers who run containerized workloads on Google Cloud, and how it helps them stay ahead of the competition: 

Carrefour
The Taiwan operation of global retail giant Carrefour chose a combination of Cloud Run and GKE to build on a more flexible and scalable infrastructure, accelerate growth and reduce its operational costs. The retailer now deploys its enterprise resource planning, human resource and supply chain management system on SAP on Google Cloud, while running its ecommerce platform with containers on Cloud Run and its mobile application with servers on Google Kubernetes Engine (GKE).

L’Oréal
A legendary leader in beauty, L’Oréal chose to manage the ingestion of diverse datasets into BigQuery with Cloud Run’s managed containers, to meet its system’s scalability, flexibility and portability demands. For the retailer, being able to measure and understand the environmental footprint of its public cloud usage is also an important step to support its sustainability efforts. With Google Cloud Carbon Footprint, L’Oréal can easily see the impact of its sustainable infrastructure approach and architecture principles.

Rent the Runway
Online fashion retailer Rent the Runway chose GKE to improve its reliability and minimize latency for its customers. After a decade of running its operations on hosted data centers and virtual machines, Rent the Runway migrated to GKE to simplify its underlying infrastructure, reduce the amount of time it took IT teams to manage it, and enhance its ability to quickly scale compute and storage up or down depending on the needs of its customers.

These retailers all have something in common: They chose containers because of the flexibility and portability it unlocks. But containers are just a packaging format. These retailers arrived at Google Cloud specifically because we run containers so well — on a variety of platforms.

First, we designed Cloud Run and GKE to make running containers in the cloud as simple and as fast as possible. Developers want to write code using their preferred language and safely deploy code into production without worrying if their favorite open-source libraries are supported. Second, Google Cloud is optimized for containers. For decades we’ve oriented our infrastructure design patterns and operations practices around high-performance container orchestration. At Google Cloud, developers reap the benefits of this optimization while also being able to easily integrate with the extensive Google Cloud ecosystem (including cloud-native databases, AI/ML APIs and toolkits, and data analytics platform offerings), and with the rich open-source and third-party ecosystem built around containers. Finally, because priorities change, flexibility matters: the same container image can run on GKE or Cloud Run without much effort.

For the retail industry, change happens fast. The industry first experienced the initial shock of e-commerce, and continues to adapt as it navigates new challenges such as COVID-19 and disrupted supply chains. Improving flexibility  across all business areas, including how they design and run their applications, is key for thriving retail going forward. 

Get started using Cloud Run and GKE on Google Cloud today.

Today’s leading organizations want to ensure their business users get fast access to data with real-time governed metrics, so they can make better business decisions. Last April, we announced our unified BI experience, bringing together both self-serve and governed BI. Now, we are making our Looker connector to Looker Studio generally available, enabling you to access your Looker modeled data in your preferred environment.

Connecting people to answers quickly and accurately to empower informed decisions is a primary goal for any successful business, and more than ten million users turn to Looker each month to easily explore and visualize their data from hundreds of different data sources. Now you can join the many Google Cloud customers who have benefited from early access to this connector by connecting your data in a few steps.*

How do I turn on the integration between Looker and Looker Studio?

You can connect to any Google Cloud-hosted Looker instance immediately after your Looker admin turns on its Looker Studio integration.

Once the integration is turned on, you create a new Data Source, select the Looker connector, choose an Explore in your connected Looker instance, and start analyzing your modeled data.

You can explore your company’s modeled data in the Looker Studio report editor and share results with other users in your organization.

When can I access the Looker connector?

The Looker connector is now available for Looker Studio, and Looker Studio Pro, which includes expanded enterprise support and compliance.

Learn more about the connector here.

* A Google Cloud-hosted Looker instance with Looker 23.0 or higher is required to use the Looker connector. A Looker admin must enable the Looker Studio BI connector before users can access modeled data in Looker Studio.

“Are we making it easier for customers to buy hammers or are we stuck on toil?” At The Home Depot, part of SRE’s responsibility is to keep our developers focused on building the technologies that make it easier for our customers to buy home improvement goods and services. So we like to use the hammer question as a barometer of whether a process or technology needs to be automated/outsourced, or whether it is something that deserves our attention as engineers.

We run a highly distributed, hybrid- and multi-cloud IT environment at The Home Depot which connects our stores to our cloud and datacenters. You can read about the transformation that occurred when we switched to BigQuery, making sales forecasting, inventory management, and performance scorecards more effective. However, to collect that data for advanced analytics, our systems need to be up and running. Monitoring the infrastructure and applications that run across all of our environments used to be a complex process. Google Cloud Managed Service for Prometheus helped us pull together metrics, a key component of our observability stack, so we now have a single pane of glass view for our developers, operators, SRE, and security teams.

Monitoring more than 2,200 stores running bare metal Kubernetes 

We run our applications in on-prem data centers, the cloud, and at the edge in our stores with a mix of managed and self-managed Kubernetes. In fact, we have bare-metal Kubernetes running at each of our store locations — over 2,200 of them. You could imagine the large number of metrics that we’re dealing with; to give you some sense, if we don’t compress data, egress from each of our stores can run in the 20-30 MBPS range. Managing these metrics quickly became a huge operational burden. In particular, we struggled with:

Storage federation: Open-source Prometheus is not built for scale. By default it runs on one machine and stores metrics locally on that machine. As your applications grow, you will quickly exceed the ability for that single machine to scrape and store the metrics. To deal with this you can start federating your Prometheus metrics, which means aggregating from multiple machines and storing them. We initially tried using Thanos, which is an open source solution to aggregate and store metrics, but it took a lot of engineering resources to maintain.

Uptime: As your federation becomes more complex, you need to maintain an ever-increasing infrastructure footprint and be ready to deal with changes to metrics that break the federation structure. Eventually, you have a team that is really good at running a metrics scraping, storage, and querying service. Going back to the question above: as an SRE manager, is running this metrics operation making it easier for customers to buy hammers, or is this operational toil that we need to consider outsourcing?

For us, the right answer was to simply use a service for all of this and we chose Google Cloud Managed Service for Prometheus. It allows us to keep everything we love about Prometheus including the ecosystem and the flexibility — we can monitor applications, infrastructure, and literally anything else that emits Prometheus-format metrics — while offloading the heavy operational burden of scaling it.  

Creating a “single pane of glass” for observability at The Home Depot

Part of what I do as an SRE director is make the developers and operators on my team more effective by providing them processes and tools they can use to make better applications. Our observability stack provides a comprehensive view of logs, metrics, and traces that are connected in such a way that gives us visibility across our IT footprint and the data we need for root cause analysis.  

A view of the aggregated metrics dashboard for over 2,200 stores used at The Home Depot  

Logs: We generate a huge amount of logs across our applications and infrastructure and we use BigQuery to store and query them. The powerful search capability of BigQuery makes it easy to pull up stack traces whenever we encounter an exception in our code workflows. 

Metrics: We can keep an eye on what is happening in real time across our applications and infrastructure. In addition to the metrics we all are used to, I want to call out log-based metrics and exemplars as particularly useful elements of our observability strategy. 

We create log-based metrics which are captured and stored. Then we can query those log-based metrics using PromQL, allowing us to tie together logs and metrics in the same dashboards.

Exemplars add data, such as a traceID, to metrics that an application is producing. Without exemplars you have to investigate issues such as latency through guesswork across different UIs. Review a particular timeframe in the metrics, then review the same timeframe in your traces and draw the conclusion that some event happened.

Traces: We use OpenTelemetry and OpenTracing to provide visibility into traces and spans so we can create service and application dependency graphs.

What’s next for The Home Depot

We are working closely with the Google Cloud team to get even more features incorporated into Managed Service for Prometheus to help us round out our single-pane-of-glass goals. Support for exemplars in the managed collector is about to be added by the Google Cloud team and we will incorporate that as soon as it’s ready. Further, they are working to expand PromQL support throughout their Cloud operations suite so that their built-in alerting can use PromQL.

I am always looking for people who are passionate about Site Reliability Engineering and DevOps, so please take a look at our Home Depot jobs board. And if you want to get more in-depth on this topic, check out the podcast I did with the Google Cloud team.

When it comes to pricing strategy, balancing profit and conversion has long been a challenge for retailers, and one compounded by today’s rapidly changing commerce landscape. 

Macroeconomic factors such as rising costs of goods, fluctuating costs of shipping, changing consumer demand, and inflation make keeping up with pricing decisions nearly impossible. These factors, combined with the phasing out of cookies and third-party data tools, leave retailers in a lurch and seeking solutions to combat their real business problems, of which pricing and profitability remain paramount. 

Introducing Spresso Insights: 
Introducing Spresso, modular SaaS solutions that leverage machine learning, advanced analytics, and AI to deliver actionable data-driven insights and better business outcomes. 

The solutions were developed with retailers at the forefront of the design. In proud partnership with Google Cloud, and available in the GCP Marketplace, Spresso aims to empower retailers to take meaningful action on their most powerful asset, their data. 

Price optimization, built by retailers for real-world problems
Spresso’s price optimization solution was created in direct response to retailers’ challenges with pricing.

The solution offers retailers compelling benefits including:

Increased SKU-level profitability without compromising conversion.

Use of first-party data, empowering retailers to make proactive pricing decisions based on their consumers’ behavior, not competitors’ pricing.

Real-time dynamic pricing, automatically routing site traffic to the top-performing price point, effectively balancing profit with consumers’ willingness to pay, meaning you’re always hedged. 

In addition to Price Optimization, Spresso’s inaugural marketplace offering includes Customer Quality and Retention module, designed to help businesses predict LTV and combat customer churn. Using data and advanced analytics support the shift into a proactive retention strategy. 

Whether it be pricing or retention, empowering businesses to adopt proactive strategies is a centralized theme for Spresso. With a stated mission of democratizing advanced analytics through actionable insights, Spresso’s solutions are designed to empower proactive strategies for all facets of business – from marketing to finance, merchandising to data science.

It is also precisely why Google is excited about the partnership. 

Insights, ready for action in GCP Marketplace
The decision to use Google Cloud for Spresso was a simple one. The product suite, security, performance, and scale Google Cloud brings to Spresso are unmatched. 

For Spresso, being powered by GCP means increased developer velocity, scalability, and unparalleled security. With Google Kubernetes Engine, the Spresso team has been able to focus on the solution instead of cloud management. This has allowed the product to move at the speed of innovation, iterating quickly and launching new products and features as needed. 

Plus, since Google Kubernetes Engine has automatic cluster management, autoscaling, and built-in security best practices, Spresso is able to provide a robust scalable solution. 

In addition to Google Kubernetes Engine, the suite of other products that GCP offers including Pub/Sub, Memorystore, Cloud SQL, Cloud DNS, and Cloud Storage, provides Spresso a central platform from which to build and scale the product.

For retailers, Spresso being powered by GCP means lighting fast optimization and price calls without slowing down their website. Spresso can scale with a retailer’s business so they can focus on acquisition and profitability. Lastly, it means peace of mind as retailers know their customers’ data is secure.

As a trusted partner of Google Cloud, Spresso is able to bring advanced analytics to a wide array of retailers who stand to benefit from the solutions. Through Spresso’s listing in the GCP Marketplace, retailers have easy access to the price optimization module, a proven solution to combat pricing woes and drive revenue growth, at their fingertips. 

Multi-armed bandit algorithm
The foundation of the price optimization module is a multi-armed bandit algorithm (MAB). A favorite of innovative, data-driven cultures, multi-armed bandits use machine learning algorithms to dynamically allocate traffic to best-performing variations and away from those underperforming. 

One of the primary reasons MABs are a favorite of data-driven cultures is their ability to optimize for multiple objectives. In the case of Spresso, the Price Optimization solution balances for conversion and profit, according to the goals set within the console.

How it works
While MABs are beloved by data scientists, one needn’t be a data scientist to reap the benefits of Spresso’s price optimization solution. The Spresso app is designed for all functions of a retail business to navigate with ease and is an especially valuable tool for the merchandising team. They simply set a minimum and maximum price for the product based on their knowledge of the business and specify the desired balance, or goal, between conversion and profit.

The bandit determines five price points, equally distributed within the specified range, and immediately starts to present those price points equally to consumers on the retailer’s website.

Then, in real-time, the bandit identifies which price point is performing the best, either converting at the highest rate or generating the most profit, according to the goals set within the app, and begins to drive more site traffic to that price point.

The bandit is always on, meaning the exploration never stops, keeping a small percentage of traffic at other price points. 

So, if something happens and consumers are suddenly willing to pay more, the bandit picks up on that change and dynamically shifts, adjusting to the new best-performing price point. 

Furthermore, implementation is straightforward with only three inputs needed – event data, catalog data, and a call to Spresso for pricing.

Early Spresso customer success stories
Despite a turbulent macroeconomic climate and decreased site traffic, e-commerce retailer Boxed was able to leverage Spresso insights to net an additional $3,000,000+ annualized incremental profit by improving margin percentage while maintaining conversion.

In a recent case study, Boxed used Spresso’s price optimization module for their 550 lowest-margin SKUs. Based on seasonality, they knew traffic would be decreasing on these items and wanted to make up the forecasted impact to their bottom line. The retailer set a goal of increasing profit while holding conversion in the Spresso console. 

In under 2 months, the price optimization solution was able to increase the margin on the 550 SKUs from 0.8% to 5.1% while maintaining the same conversion, resulting in millions of additional annualized revenue for Boxed. 

“Last quarter, we were able to report an increase of 88% in gross profit, and the Spresso Price Optimization bandit was integral part of that. Better yet, that profit came without sacrificing conversion or customer experience. With what feels like a simple flip of a switch we were able to find literally millions of dollars hiding in the business. The ROI of this solution was realized in just a few days – something I haven’t seen with any other solution we’ve invested in.” said Chieh Huang, CEO of Boxed. 

Alison Weick, President of eCommerce at Boxed agreed, “Spresso’s price optimization solution is my merchandising team’s secret weapon. We’ve been able to break free of manual scraping and use our data to make pricing decisions that have proven to resonate with our customers.”

Striking the right price
As the retail industry continues to evolve and grapple with the complexities of pricing strategy, it’s clear that retailers need innovative solutions to help them stay competitive. 

With Spresso’s price optimization module, retailers can take control of their pricing strategy and drive profitability at the SKU level. Leveraging first-party data and real-time optimization allows retailers to strike the right balance between profit and conversion. 

And with the added benefits of being a trusted partner of Google Cloud, retailers can trust in the security, performance, and scalability of the Spresso platform. 

Retailers are invited to try the solution for themselves, to learn more check out the Spresso GCP Marketplace listing or visit the Spresso website here.

Data is one of the single most valuable assets for organizations today. It can empower businesses to do incredible things like create better views of health for hospitals, enable people to share timely insights with their colleagues, and — increasingly — be a foundational building block for startups who build their products and businesses in a data cloud. 

Last year, we shared that more than 800 software companies are building their products and businesses with Google’s data cloud. Many of these are fast-growing startups. These companies are creating entirely new products with technologies like AI, ML and data analytics that help their customers turn data into real-time value. In turn, Google’s data cloud and services like Google BigQuery, Cloud Storage, and Vertex AI are helping startups build their own thriving businesses. 

We’re committed to supporting these innovative, fast-growing startups and helping them grow within our open data cloud ecosystem. That’s why today, I’m excited to share how three innovative data companies – Ocient, SingleStore, and Glean – are now building on Google’s data cloud as they grow in the market and deliver scalable data solutions to more customers around the world.

Founded in 2016, Ocient is a hyperscale data warehousing and analytics startup that is helping enterprises analyze and gain real-time value from trillions of data records by enabling massively parallelized processing in a matter of seconds. By designing its data warehouse architecture with compute adjacent to storage on NVMe solid state drives, continuous ingest on high-volume data sets, and intra-database ELT and machine learning, Ocient’s technology enables users to transform, load, and analyze otherwise infeasible data queries at 10x-100x the price performance of other cloud data warehouse providers. To help more enterprises scale their data intelligence to drive business growth, Ocient chose to bring its platform to Google Cloud’s flexible and scalable infrastructure earlier this year via Google Cloud Marketplace. In addition to bringing its solution to Google Cloud Marketplace, Ocient is using Google Cloud technologies including Google Cloud Storage for file loading, Google Compute Engine (GCE) for running its managed hyperscale data analytics solutions, and Google Cloud networking tools for scalability, increased security, and for analyzing hyperscale sets data with greater speed. In just three months, Ocient more than doubled its Google Cloud usage in order to support the transformation workloads of enterprises on Google Cloud.

Another fast-growing company that recently brought its solution to Google Cloud Marketplace to reach more customers on Google Cloud’s scalable, secure, and global infrastructure is SingleStore. Built with developers and database architects in mind, SingleStore helps companies provide low-latency access to large datasets and simplify the development of enterprise applications by bringing transactions and analytics in a single, unified data engine (SingleStoreDB). Singlestore integrates with Google Cloud services to enable a scalable and highly available implementation. In addition to growing its business by reaching more customers on Google Cloud Marketplace, SingleStore is today announcing the establishment of its go-to-market strategy with Google Cloud, which will further enable them to deliver their  database solution to customers around the world.

I’m also excited to share how Glean is leveraging our solutions to scale its business and support more customers. Founded in 2019, Glean is a powerful, unified search tool built to search across all deployed applications at an organization. Glean’s platform understands context, language, behavior, and relationships, which in turn enables users to find personalized answers to questions, instantly. To achieve this, the Glean team built its enterprise search and knowledge discovery product with Google managed services, including Cloud SQL and Kubernetes, along with solutions from Google Cloud like our Vertex AI, Dataflow, Google BigQuery. By creating its product with technologies from Google Cloud, Glean has the capabilities needed to be agile and iterate quickly. This also gives Glean’s developer team more time to focus on developing the core application aspects of its product, like relevance, performance, ease of use, and delivering a magical search experience to users. To support the growing needs of enterprises and bring its product to more customers at scale, Glean is today announcing its formal partnership with Google Cloud and the availability of its product on Google Cloud Marketplace. 

We’re proud to support innovative startups with the data cloud capabilities they need to help their customers thrive and to build and grow their own businesses, and we’re committed to providing them with an open and extensible data ecosystem so they can continue helping their customers realize the full value of their data.

As a Platform Administrator or Operator, you’ve already been using Config Sync to sync your configurations — deployments, policy definitions, Helm charts, ConfigMaps, and more — consistently across many Kubernetes clusters. But with the excitement of solving one problem comes a new one: real time visibility on configuration syncs and failures across clusters. When operating at scale, your list of concerns grows longer: Has my configuration synced? Are my resources reconciling? Which of my configuration changes in the cluster is impacting end-user behavior? Today, we’re introducing the Config Management Dashboard which can help you more easily find the answers to these questions and more.

The new Config Management Dashboard not only helps you quickly identify the configurations and differences between the desired and the actual state, but also keeps track of the clusters that run Config Sync. In this article, you’ll learn more about the key components for this new dashboard and how it can help you solve your operational problems. 

Key components 

Dashboard: focuses on the overall status of all configurations and resources across one or multiple clusters. The dashboard also provides a quick view of the top concerns within a particular cluster or package.

Packages: are Git repositories, Helm charts, and OCI registries that contain cluster configurations and resources that are synced across clusters. You can view synced resources and configurations, either by package or cluster. You can also filter your view by sync status, reconcile status, location, and clusters.

Common operations 

The new Config Management Dashboard has been designed with common operations in mind that previously could only be done via CLI. Now you can: 

Easily install Config Sync on one or multiple clusters from the dashboard and track install status on the Settings tab.

Quickly check the sync status and reconcile status of a particular configuration in a package across one or many clusters from the Packages tab with the help of quick filters. Sync status is the status of the latest sync from the packages such as Git repositories, Helm charts, or OCI registries. Reconcile status is the status of the configuration when deployed to the Kubernetes API by Config Sync.

Filter issues and identify errors on any resource across clusters by viewing the error messages directly from the Packages tab. Now you can quickly see common errors such as configuration errors and synchronization errors right from the dashboard.

Glance over a real-time snapshot of the sync status and reconcile status of all the packages, along with overall Config Sync health across clusters right from the dashboard.

Summary

Config Management Dashboard will help you and your application teams with some of their critical daily tasks:

Review progress of configurations and resources across clusters confidently and ensure consistent cluster behavior. 

Quickly identify issues and act accordingly so that value to end-users and service-level objectives (SLOs) are maintained.

Making a Config Management Dashboard that is easy to use and serves your needs is important for us and we plan to add important features to the Config Management Dashboard including package deployment, rollout management, and notifications in the future. So stay tuned and we welcome your feedback on the new Config Management Dashboard. You can reach us by clicking on the question icon on the top right corner in Cloud Console and choosing Send feedback.

In 2022 we saw several major elections across the globe. And as many countries prepare for elections in 2023 and 2024, we’d like to re-introduce a set of transparency tools that Google provides in the election advertising space. 

We know that election advertising is an important component of the democratic process – candidates use ads to raise awareness about their campaigns, share information and engage potential voters. And we want people to have confidence in election ads they see on our platforms. That’s why, in 2018, Google launched its Election Advertising Verification Policy, requiring all advertisers who wish to run election ads to complete a verification process. This includes checking and confirming an advertiser’s identity, and ensuring they meet certain eligibility requirements to run elections ads. We currently support election advertising verification in over 35 countries. 

Using the information collected via this verification process, Google also released itsPolitical Ads Transparency Report, which provides important insights and data about the ads published under our elections ads policy through Google Ads, YouTube and Google Display & Video 360.

In this blog post, we will explore the data available in the dataset, how to access it, and provide some examples of how it can be used. By the end of this post, you will have a better understanding of the valuable insights this information can provide.  We encourage all readers to partner with us by using this data to drive greater accountability in the election advertising ecosystem.

Deeper dive into the dataset

Let’s dive deeper into what qualifies ads to be included in the Political Ads Transparency Report, and the data that is made available in the dataset. 

The dataset contains information on election ads run by verified advertisers across Google Ads, YouTube and Google Display & Video 360. The definition of an election ad on Google varies based on the country or region in question. For example, in the United States, advertisers who wish to run ads that mention a US state-level or federal candidate or office holder, political party, or ballot measure, must complete our verification process verification process and the ads would subsequently appear in the Transparency Report. 

The dataset– which begins in 2018 and retains ads for 7 years – includes the ads creatives themselves, as well as meaningful data on each ad, such as:

the audience that was targeted (advertisers can only target election ads on four broad categories: age, gender, contextual and general location)

how many users saw the ad, and 

the money spent on the ad.

These features are a prime example of Google’s commitment to transparency and accountability in the industry. Not only have journalists and research groups found the Transparency Report’s dataset incredibly useful for analyzing trends in election advertising spending, but this data and the disclosures that accompany it also help empower voters with reliable information about the election ads they may see on Google. 

Accessing the election ads dataset

You can access this dataset in two ways: through a user-friendly interface and with SQL using the public dataset available in BigQuery. 

The interface, which was updated in 2022, includes an interactive drop-down tool that allows users to select and filter specific advertisers, candidates, locations, ad formats, and timeframes. It then displays the resulting ads and automatically produces insights in the form of easy-to-understand data visualizations, such as tables and charts. You can also create reports on an advertiser’s  spend over time and spend by location. This can provide valuable insights into who is spending money on election ads and where investments are happening. 

The BigQuery public dataset allows you to analyze the data using SQL, including the ability to join the data with other public datasets, or their own private data loaded in BigQuery.  It also opens up the possibility to access the data via the BigQuery API, visualize the data in Looker Studio or Connected Sheets, or integrate the data for any number of purposes across the services of Google Cloud. 

We’ll delve into a few example ways you can search using the UI and access and analyze the data in BigQuery.

Use cases for the election ads dataset

For research looking at ad dollars spent by location 

With the user interface, you can search and analyze ad spending over time in a specific location as broad as an entire country or as granular as a specific state or congressional district. For instance, the UI allows for search for spending inAlabama from 2018 to 2022. You can see top advertisers, and district by district spending amounts in Alabama.

For research looking into targeted political advertisement

The dataset can also be used to identify political advertisers for a targeted demographic. For example, we can use BigQuery to write a query to identify political advertisers that targeted women in Florida over the 6 weeks leading up to the November 8, 2022 US midterm elections:

After running this query in the BigQuery console, the results show 149 ads, with details including the advertiser’s name, advertiser page, gender and general location targeting of the ad , and the total number of election ads that advertiser ran for this demographic. 

For researching election ads with high impressions

Another use case is analyzing election ads with large numbers of impressions, for example those that have reached over 1 million impressions. To do this, we can use the following SQL query in BigQuery:

The query returned over 24,790 ads and each row contains additional information like the election ad id (which can be useful to find the ad in the UI), impressions, advertiser name and a link to the election ad details. If you click on the link for the election ad details, you’ll see even more information on that specific ad. 

Next steps

These are just a few of the many ways to use the Political Ads Transparency Report dataset.

A great way to get familiar with the dataset is through its visual UI; for example, you can check out all the election ads run by a specific advertiser. Then you can start running SQL queries on the BigQuery dataset at no cost by creating a BigQuery sandbox. The BigQuery sandbox enables you to query data within the limits of the free tier without needing a credit card. If you decide to enable billing and go above the free tier threshold, you are subject to regular BigQuery pricing. 

To learn more about the Political Ads Transparency Report dataset, head to the Marketplace listing. 

We would like to thank Juan Uribe (Software Engineer) and Michael Yu (Software Engineer) for their help in creating this blog post.