Demand for spoken audio content such as podcasts remains robust despite the proliferation of video services and other entertainment options for consumers. Shibin Li, Co-founder of Castbox, credits growth of the global podcast platform to the following: speed and availability, market-leading features, the proliferation of smart devices to deliver audio content, and activities — such as driving and working around the house — that make consuming video difficult.

Founded in 2016 and headquartered in Beijing, China, Castbox enables users to locate, access, and create spoken audio content. Available on iOS and Android, Castbox supports 50 million podcasts, on-demand radio programs, and audiobooks in 70 languages from 175 countries. The platform hosts about 2 million users per day and is the largest podcast platform on Android.

Castbox includes a range of features that build on its core service to provide a high-quality user experience. These features include curated podcast recommendations and in-audio search.

A combination of services

At its inception, Castbox relied on a combination of a multinational cloud services, as well as Google Cloud Platform (GCP) services, including the Google BigQuery analytics data warehouse and Cloud APIs to provide programmatic interfaces with Google Cloud services, and a range of services from the Google mobile development platform Firebase.

However, as Castbox matured and its user base expanded, the business increased its reliance on Google Cloud Platform and Firebase.

“We needed to access stable cloud services as we could not tolerate long periods of downtime that would compromise the user experience,” says Li. “Furthermore, we had to support up to 50,000 concurrent connections, and potentially more in future, without disruption.”

“Based on our analysis of the data in Google BigQuery, we can determine what type of content users are listening to, how long they like to listen to it, and when they like to listen to it. This allows us to recommend similar podcasts to each user based on the preferences he or she expressed, encouraging activity on and return visits to our platform.”

—Shibin Li, Co-Founder, Castbox

Competitive differentiation

Castbox also found machine learning-powered Google Cloud Platform APIs could help deliver features, such as in-audio search, that differentiate the podcast platform from its competitors. In addition, Firebase SDKs and Firebase A/B Testing would enable Castbox to create and analyze new applications, as well as make adjustments based on user feedback. Firebase Realtime Database would allow the business to support tens of thousands of concurrent user connections.

The diligence of the Google Cloud team in advising Li and her team about forthcoming products and services also swayed Castbox towards Google technologies. The business gained the opportunity with Google to join several programs that offered early access to Google innovations.

Signature in-audio search service

Castbox now uses Google Cloud Platform services in the Tokyo, Japan, and U.S. East regions. Cloud Speech-to-Text API plays a key role in delivering Castbox’s signature in-audio search service. This service enables users to search transcriptions of audio content on the platform for words or phrases. The search results incorporate the title of the podcast and the search term in context (for example, within the sentence or sentence excerpt in which it appears). Each use of the word or phrase is time-stamped so it can easily be found. The API enables Castbox developers to apply neural network algorithms to achieve audio-to-text conversion accuracy rates of greater than 96%, while search queries typically experience latency of just 50 milliseconds.

In addition, the latency of comparison data, converting audio to text, is only about 250 milliseconds, contributing to the processing of about 12 minutes worth of audio to text in just 10 minutes. “We can process about 20 hours of audio files in one day,” Li says. “This enables us to transcribe and index all the new episodes of a podcast in that period.”

50,000 concurrent connections

With Firebase Realtime Database, Castbox now supports up to 50,000 concurrent connections to its platform with an average latency per connection of just 10 milliseconds. “Firebase Realtime Database also allows us to continue operating in offline mode, which is extremely helpful if we experience any network disruptions,” Li explains. “When we come back online again, any data is simply synchronized with the database.”

Google BigQuery and the analytics capabilities of Firebase SDKs also enable Castbox to monitor and analyze user behaviors. “Based on our analysis of the data in Google BigQuery, we can determine what type of content users are listening to, how long they like to listen to it, and when they like to listen to it,” Li explains. “This allows us to recommend similar podcasts to each user based on the preferences he or she expressed, encouraging activity on and return visits to our platform.”

“Given our queries may span up to 40 days of data, we may be analyzing up to 1,200 GB at one time. We have no problem doing this with Google BigQuery.”

—Shibin Li, Co-Founder, Castbox

Castbox also uses its analyses of Google BigQuery data to amend banners and summaries on its platform to encourage users to listen to additional content. Furthermore, the service is prepared to make surprise recommendations of content to users based on the preferences and reactions of users with similar tastes.

“We analyze a pool of data growing at up to 30 GB per day,” Li explains. “Given our queries may span up to 40 days of data, we may be analyzing up to 1,200 GB at one time. We have no problem doing this with Google BigQuery.” These analyses also support Castbox’s decision to start creating original content, such as finance and economic news, for its platform.

Checking weekly changes

Castbox does not rely only on analyzing user data to deliver a high-quality experience. The business aggregates user feedback from emails and Google Play reviews to make weekly changes to its platform. It then uses Firebase A/B Testing to check whether these changes are met with a positive user response.

“We are extremely pleased with Google Cloud Platform and Firebase. We have been able to differentiate ourselves from our competitors and provide an attractive option for users at a time when content and entertainment options are exploding. We have a great opportunity with Google to continue to improve the value of our offering to users and build engagement and loyalty.”

—Shibin Li, Co-Founder, Castbox

Castbox’s positive experiences with Google Cloud Platform are encouraging the business to grow its use of the product. “We are trying to move some more services to Google Cloud Platform because it is very stable and scalable,” Li says. The business is keen to explore the capabilities of Cloud Pub/Sub to provide low latency messaging between applications, Cloud Spanner to deliver a distributed relational database service, and Cloud Dataflow to transform and enrich data in stream and batch modes.

“We are extremely pleased with Google Cloud Platform and Firebase,” Li concludes. “We have been able to differentiate ourselves from our competitors and provide an attractive option for users at a time when content and entertainment options are exploding. We have a great opportunity with Google to continue to improve the value of our offering to users and build engagement and loyalty.”

It’s simply amazing to witness how some of our systems integrators employ Google Cloud solutions to drive innovation in ways we at Google may never have considered—especially in healthcare.  According to analyst firm MarketsandMarkets, the market for the Cloud in healthcare is projected to grow 43% between 2020 and 2025 to nearly $65 billion, fueled by the need for better technology infrastructures and faster digital transformation.  

Healthcare and life sciences communities are looking to Google Cloud and its Service engagement model partners to improve collaboration and activate the power of medical data.  These transformations deliver robust data analytics and bring a much deeper perspective into health epidemics like COVID-19 to help save lives.

In the healthcare and life sciences industry, our Google Cloud partners and customers provide constant energy and inspiration–and are the magic in some key healthcare innovations globally.  

Let me show you how they are solving real-world business challenges.

Improving collaboration in Healthcare

Cloudbakers and Comanche County Memorial Hospital transitioned 2,000 employees to Google Workspace to improve collaboration, reduce costs, and increase security. By implementing a system that requires less maintenance while enabling mobile access to data and true collaboration, Comanche County Memorial Hospital saves $175,000 annually on licenses and helps medical professionals spend more time with patients.

“We chose Google Workspace because it cost a quarter of what we were paying previously, offers the kind of modern features that healthcare facilities need, and gave us data security and peace of mind.” —James Wellman, CIO, Comanche County Memorial Hospital

Accessing previously locked down medical data

Google Cloud and Quantiphi supported advances in cloud-based machine learning services to reduce infrastructure costs, unlock new paths of treatment, and dramatically reduce the amount of time it takes to evaluate scanned imagery following a stroke.  John Hopkins University BIOS Division has been working on medical imaging to accelerate insights from scans on approximately 500 patients from 2,500 hours to 90 minutes, and lead to more accurate decision-making for brain injury patients that will ultimately improve medical outcomes.  

“We’ve aligned closely with the goal of showing that a cloud-based, AI-driven approach is robust and that it can be performed while protecting personal PHI. In terms of costs, the cloud has definitely reduced some of the traditional financial demands of our research.” —Daniel F. Hanley, Jr., M.D., Director, Johns Hopkins University BIOS Division

Improving data access with the flexibility of Google Cloud

With the help of MediaAgility, TRIARQ migrated to Google Cloud to modernize their platform, build new applications, and expand their global footprint.  With BigQuery, TRIARQ can now consolidate all transactional data, past and present, into a single location to report on specific insights and predict future data from e-prescriptions to complex revenue-cycle data and value-base analysis.

“The future of this industry will need to be a global one, and we can no longer be stuck in legacy systems if we want to survive. Having a team that is passionate about supporting us in this journey is definitely a plus point.”—Yaw Kwakye, Co-founder and Chief Architect, TRIARQ

Increasing visibility to COVID-19 outbreaks for actionable insights

As part of its response to the COVID-19 pandemic, HCA Healthcare chose to work with Google Cloud and SADA to create a national portal that increases visibility into outbreaks in 3,100 counties across the country in just 8 weeks.  Now, the portal generates 30,000 new analytical views each day that can help inform private and public sector decision making for reopenings, closures, hot spots, and many other population health management activities. 

“This project required deep knowledge of AI, and consumer-facing platforms, as well as healthcare. Google brought together the ideal combination of product, people, and partners. Google Cloud’s healthcare-specific products along with SADA’s expertise in the healthcare IT space made this partnership the perfect choice to move quickly and intelligently.”—Dr. Edmund Jackson, Chief Data Officer, HCA Healthcare

We’re committed to building the technology, resources, and services through Partner Advantage to help our partners address this opportunity.  Looking for a solution focused partner in your region who has achieved Expertise and/or Specialization in your industry?  Search our Global Partner Directory.  Not yet a Google Cloud partner? Visit Partner Advantage and learn how to become one today!

In our conversations with technology leaders about data-driven transformation using Google Data Cloud –  industry’s leading unified data and AI solution – , one important topic is incorporating continuous intelligence to move from answering questions such as “What has happened? to questions like “What is happening?” and “What might happen?”. The core to this evolution is the need for an underlying data processing that not only provides powerful real-time capabilities for events happening close to origination, but also brings together existing data sources under one unified data platform to enable organizations to draw insights and take actions holistically. Dataflow, Google’s cloud-native data processing and streaming analytics platform, is a key component of any modern data and AI architecture and data transformation journey, along with BigQuery, Google’s internet-scale warehouse with built-in streaming, BI engine and ML; Pub/Sub, a global no-ops event delivery service; and Looker, a modern BI and embedded analytics platform. One of the key evaluation factors is potential economic value of Dataflow to their organization, particularly in the context of engaging other stakeholders is key for many of the leaders that we engage with. So we commissioned Forrester Consulting to conduct a comprehensive study on the impact that Dataflow had on their organization by interviewing actual customers . 

Today we’re excited to share our commissioned study conducted by Forrester Consulting, the Total Economic Impact™ of Google Cloud Dataflow, which allows data leaders to understand and quantify the benefits of Dataflow, and use cases it enables. Forrester conducted interviews with Dataflow customers to evaluate the benefits, costs, and risks of investing in Dataflow across an organization. Based on their interviews, Forrester identified major financial benefits across four different areas: business growth, infrastructure cost savings, data engineer productivity, and administration efficiency. In fact, Forrester found that customers adopting Dataflow can achieve a 55% boost in developer productivity and a 50% reduction in infrastructure costs. In fact, Forrester projects that customers adopting Dataflow can achieve a range of up to 171% Return on Investment (ROI) and a less than six months payback period. Customers can now use figures in the report to compute their own Return on Investment (ROI) and payback period.

“Dataflow is integral to accelerating time-to-market, decreasing time-to-production, reducing time to figure out how to use data for use cases, focusing time on value-add tasks, streamlining ingestion, and reducing total cost of ownership.” – Lead technical architect, CPG

Let’s take a deeper look at the ways that Forrester found that Dataflow can help you achieve your goals and unlock your business potential. 

Benefit #1: Increase data engineer productivity by 55%

Developers can choose among a variety of programming languages to define and execute data workflows. Dataflow also seamlessly integrates with other Google Cloud Platform and open source technologies to maximize value and applicability to a wide variety of use cases. Dataflow streamlined workflows with code reusability,dynamic templates, and the simplicity of a managed service. Engineers trusted pipelines to run correctly and adhere to governance. Data engineers avoided laborious issue-monitoring and remediation tasks that were common in the legacy environments such as poor performance, lack of availability, and failed jobs. Teams valued the language flexibility and open source base.

“Dataflow provided us with ETL replacement that opened limitless potential use cases and enabled us to do smarter data enhancement while data remains in motion.” — Director of data projects, financial services

Benefit #2: Reduce infrastructure costs by up-to 50% for batch and streaming workloads 

Dataflow’s serverless autoscaling and discrete control of job needs, scheduling, and regions eliminated overhead and optimized technology spending. Consolidating global data processing solutions to Dataflow further eliminated excess costs while ensuring performance, resilience, and governance across environments. Dataflow’s unified streaming and batch data platform gives organizations the flexibility to define either workload in the same programming model, run it on the same infrastructure, and manage it from a single operational management tool. 

“Our costs with our cloud data platform using Dataflow are just a fraction of the costs we faced before. Now we only pay for cloud infrastructure consumption because the open source base helps us avoid licensing costs. We spend about $120,000 per year with Dataflow, but we’d be spending millions with our old technologies.” – Lead technical architect, CPG

Benefit #3: Increase top-line revenue by improving customer experience and retention with payback time of < 6 months

Streaming analytics is an essential capability in today’s digital world to gain real-time actionable insights. Likewise, organizations must also have flexible, high- performance batch environments to analyze historical data for building machine learning models, business intelligence, and advanced analytics. Dataflow enabled real-time streaming use cases, improved data enrichment, encouraged data exploration,improved performance and resiliency, reduced errors, increased trust, and eliminated barriers to scale. As a result, organizations provided customers with more accurate, relevant, and in-the-moment data-backed services and insights — boosting customer experience, creating new revenue streams, and improving acquisition, retention, and enrichment.

“It’s already been proven that we are getting more business [with Dataflow] because we can turn around results faster for customers.” – VP of technology, financial services technology

“When we provide data to our customers and partners with Dataflow, we are much more confident in those numbers and can provide accurate data within a minute. Our customers and partners have taken note and commented on this. It’s reduced complaints and prevented churn.” – Senior software engineer, media

Other benefits 

Eliminated administrative overhead and toil

As a cloud-native managed service, all administration tasks such as provisioning, scaling, and updates are automatically handled by Google Cloud. Teams no longer need to manage servers and related software for legacy data processing solutions. Admins also streamlined processes for setting up data sources, adding pipelines, and enforcing governance.

Saved business operations costs for support teams and data end users

Dataflow improved the speed, quality, reliability, and ease of access to data for insights for general business users, saving time and empowering users to drive better data-backed outcomes. It also reduced support inquiry volume while automating manual job creation.

What’s next?

Download the Forrester Total Economic Impact study today to dive deep into the economic impact Dataflow can deliver your organization. We would love to partner with you to explore the potential Dataflow can unlock in your teams. Please reach out to our sales team to start a conversation about your data transformation with Google Cloud.

TL;DR – Regular file export for Google Cloud billing is going away on May 15th! Try out this interactive tutorial to set up exporting and explore your billing data with BigQuery

You may have seen an email from Google Cloud reminding you to set up billing data export to BigQuery, and letting you know that file export options are going away. More specifically:

Starting May 15, 2023, your daily usage and cost estimates won’t be automatically exported to a Cloud Storage bucket. Your existing Cloud Storage buckets will continue to exist, but no new files will be added on an ongoing basis.

In fact, I even received one of these notifications since I had an old billing account (or two) that still had file export enabled. Even though the file export is going away, exporting your billing data to BigQuery gives you more detail and more capabilities to analyze, so it’s a big upgrade.

Start from the start

While Google Cloud does offer a free trial and free tier of services, it shouldn’t come as a surprise that most cloud services cost money to use. With the unique properties of the cloud, it’s much easier to bill for exact usage of various services, like pay-per-second for virtual machines. However, this can also become an overwhelming amount of information when a large number of services are being used across multiple teams. In order to make sure you understand your billing data, Google Cloud offers that data through direct reporting in the Cloud console, file exports, and exporting to BigQuery.

Most importantly, the file export for Cloud Billing data is going away on May 15th (the file exports for specific reports, like the cost table, aren’t going away). This was an option for billing accounts to generate daily CSV or JSON files into a Cloud Storage bucket so you could download the files and analyze them as needed. While these exports were definitely helpful for looking at your billing data, over time they couldn’t keep up with the complexity and volume of data that comes out of a billing account.

The most important thing to note here is simple: this export option is being deprecated entirely, so if you’re currently using it (and even if you’re not), you should make sure exporting to BigQuery is enabled.

Large data, large queries

Exporting from your billing account into BigQuery has been an option for the past few years, and now more than ever it’s the central option for managing your billing data. Of course, BigQuery is a natural home for large datasets with the scale and granularity of your billing data. In addition to that, exporting to BigQuery also happens more frequently than a daily file export, so you can do cost anomaly detection much faster.

As teams and organizations become larger, it can be increasingly difficult to figure out which groups are responsible for different costs. If your organization is using a chargeback model (where one group pays for all resources and then breaks down cost details for individual teams/groups to be responsible for their usage), then using BigQuery is the best way for you to accurately collect and analyze costs. On top of being able to query directly against your data, BigQuery also makes it much easier to integrate with other tools, like Looker or Looker Studio for visualization.

The actual data being exported to BigQuery are hourly records of every service your billing account is responsible for, along with detailed information like the SKU, what type of unit is being measured, and how many of that unit you’re paying for.

I suspect most folks using the file export (and probably most folks overall) will be fine with the standard export for BigQuery, and can use the detailed cost and pricing options if they need even more details. You can find some more details on all the exports in this blog post.

With the file export going away, it’s also important to mention: exporting your Cloud Billing data to BigQuery starts when you enable it, and doesn’t include older data. So make sure you set yourself up for future success by enabling billing export as soon as possible. Try using our built-in walkthrough to help you enable the export and to get started analyzing the data! You can also read more in the documentation.