Predictive Model Built on Google Cloud Helps You Get a 7-day Mosquito Forecast Report!
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Mosquitoes aren’t just the peskiest creatures on Earth; they infect more than 700 million people a year with dangerous diseases like Zika, Malaria, Dengue Fever, and Yellow Fever. Prevention is the best protection, and stopping mosquito bites before they happen is a critical step.
SC Johnson—a leading developer and manufacturer of pest control products, consumer packaged goods, and other professional products—has an outsized impact in reducing the transmission of mosquito-borne diseases. That’s why Google Cloud was honored to team up with one of the company’s leading pest control brands, OFF!®, to develop a new publicly available, predictive model of when and where mosquito populations are emerging nationwide.
As the planet warms and weather changes, OFF! noticed month-to-month and year-to-year fluctuations in consumer habits at a regional level, due to changes in mosquito populations. Because of these rapid changes, it’s difficult for people to know when to protect themselves. The OFF!Cast Mosquito Forecast™, built on Google Cloud and available today, will predict mosquito outbreaks across the United States, helping communities protect themselves from both the nuisance of mosquitoes and the dangers of mosquito-borne diseases—with the goal of expanding to other markets, like Brazil and Mexico, in the near future.
With the OFF!Cast Mosquito Forecast™, anyone can get their local mosquito prediction as easily as a daily weather update. Powered by Google Cloud’s geospatial and data analytics technologies, OFF!Cast Mosquito Forecast is the world’s first public technology platform that predicts and shares mosquito abundance information. By applying data that is informed by the science of mosquito biology, OFF!Cast accurately predicts mosquito behavior and mosquito populations in specific geographical locations.
Starting today, anyone can easily explore OFF!Cast on a desktop or mobile device and get their local seven-day mosquito forecast for any zip code in the continental United States. People can also sign up to receive a weekly forecast. To make this forecasting tool as helpful as possible, OFF! modeled its user interface after popular weather apps, a familiar frame of reference for consumers.
The technology behind the OFF!Cast Mosquito Forecast
To create this first-of-its-kind forecast, OFF! stood up a secure and production-scale Google Cloud Platform environment and tapped into Google Earth Engine, our cloud-based geospatial analysis platform that combines satellite imagery and geospatial data with powerful computing to help people and organizations understand how the planet is changing.
The OFF!Cast Mosquito Forecast is the result of multiple data sources coming together to provide consumers with an accurate view of mosquito activity. First, Google Earth Engine extracts billions of individual weather data points. Then, a scientific algorithm co-developed by the SC Johnson Center for Insect Science and Family Health and Climate Engine experts translates that weather data into relevant mosquito information. Finally, the collected information is put into the model and distilled into a color-coded, seven-day forecast of mosquito populations. The model is applied to the lifecycle of a mosquito, starting from when it lays eggs to when it could bite a human.
The SC Johnson Center for Insect Science and Family Health is one the world’s leading entomology research centers, studying advanced science of insect biology, insect-borne disease prevention and effective product solutions for consumer use. The science behind brands like OFF! is grounded in knowledge from world-class entomologists who have devoted their careers to SC Johnson’s mission of eradicating diseases like Malaria and Zika.
“We are putting the power in consumers’ hands in providing them with a tool to help predict their exposure and prevent mosquito bites,” said Maude Meier, SC Johnson entomologist. “It’s an exciting time to be working in the field of insect science as we find new opportunities to combine science and technology, like Google Earth Engine, to be a force for good in our mission to prevent the spread of insect-borne diseases.”
It takes an ecosystem to battle mosquitos
Over the past decade, academics, scientists and NGOs have used Google Earth Engine and its earth observation data to make meaningful progress on climate research, natural resource protection, carbon emissions reduction and other sustainability goals. It has made it possible for organizations to monitor global forest loss in near real-time and has helped more than 160 countries map and protect freshwater ecosystems. Google Earth Engine is now available in preview with Google Cloud for commercial use.
Our partner, Climate Engine, was a key player in helping make the OFF!Cast Mosquito Forecast a reality. Climate Engine is a scientist-led company that works with Google Cloud and our customers to accelerate and scale the use of Google Earth Engine, in addition to those of Google Cloud Storage and BigQuery, among other tools. With Climate Engine, OFF! integrated insect data from VectorBase, an organization that collects and counts mosquitoes and is funded by the U.S. National Institute of Allergy and Infectious Diseases.
The model powering the OFF!Cast Mosquito Forecast combines three inputs—knowledge of a mosquito’s lifecycle, detailed climate data inputs, and mosquito population counts from more than 5,000 locations provided by VectorBase. The model’s accuracy was validated against precise mosquito population data collected over six years from more than 33 million mosquitoes across 141 different species at more than 5,000 unique trapping locations.
A better understanding of entomology, especially things like degree days and how they affect mosquito populations, and helping communities take action is critically important to improving public health. Learn more about OFF!Cast Mosquito Forecast and see here to learn more about Google Earth Engine on Google Cloud.
STAC-M3 Tick History Analytics in Google Cloud Benchmark Results Reveals it is 18X Faster than Previous Version
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The Securities Technology Analysis Center (STAC®), an organization that improves technology discovery and assessment in the finance industry through dialog and research, recently audited the STAC-M3™ benchmark suite on Google Cloud (SUT ID KDB211210). These enterprise tick-analytics benchmarks assess the ability of a solution stack such as database software, servers, and storage, to perform a variety of I/O-intensive and compute-intensive operations on historical market data.
Following up on our previous STAC-M3 benchmark audit (SUT ID KDB181001), a redesigned Google Cloud architecture leveraged the most recent version of kdb+ 4.0, the time-series database from KX, and achieved significant improvements: 35 out of 41 benchmarks ran faster in the new cluster – by up to 18x faster than Google Cloud’s prior results. Key highlights include the following:
Compared to the previous STAC-M3 Antuco suite results on Google Cloud:
- Was faster in 13 of 17 mean response-time benchmarks
- Was 18x faster – a 94% reduction in run time – in the version of Year-High Bid that allows caching (STAC-M3.ß1.1T.YRHIBID-2.TIME), which also set an overall record for all published results
- Had 9x higher throughput in Year-High Bid (STAC-M3.ß1.1T.YRHIBID.MBPS)
Compared to the previous STAC-M3 Kanaga suite results on Google Cloud:
- Was faster in 22 of 24 mean response-time benchmarks
- Was over 10x faster in all four Market Snapshot workloads (STAC-M3.ß1.10T.YR[2,3,4,5]-MKTSNAP.TIME)
- Had 5x the throughput in Year-High Bid involving 2 years of data (STAC-M3.ß1.1T.2YRHIBID.MBPS)
“The STAC-M3 standard was designed by financial firms to reveal the performance of tick analytics stacks. Generational improvements like those exhibited by Google Cloud’s most recent STAC-M3 audit, are important data points for firms evaluating new architectures for performance and scale,” said Peter Nabicht, President of STAC.
These performance results may translate to real-world advantages that may be difficult for investment firms to achieve in static and costly on-premises environments: immediate answers in high data velocity markets, more thoroughly explored research theories by adding data or new quantitative approaches, and reduced costs by releasing cloud resources more quickly.
STAC-M3: High-speed tick analytics
Designing for record-breaking results
In our STAC-M3 audit, the stack under test (SUT) was designed to take advantage of horizontal scalability in the cloud by sharding data across independent compute nodes. The cluster of 12 Google Compute Engine N2 instances was powered by Intel Cascade Lake, with each node using 32 vCPUs, 160GiB of memory, and 9TiB of local NVMe SSDs. The full STAC-M3 Antuco and Kanaga data set was split across the cluster and kdb+ scripts distributed queries between nodes.
This configuration was the sweet spot for this particular workload, but this architecture does not need to be limited to 12 nodes for other workloads – the data sharding algorithm could scale to any number of nodes as required by workload demands. Since scaling out the cluster in this manner increases the total pool of available storage, this architecture can continue scaling out to petabytes of storage across hundreds of nodes.
The ability to spawn large clusters with hundreds of thousands of processors on demand at low cost, and to delete the resources when jobs complete, not only changes the economics of running computations on large financial data sets, it also opens up opportunities to explore solutions to new types of problems that were previously overlooked due to the constraints of fixed hardware on-premises. You can check the pricing of this VM configuration using the Google Cloud Pricing Calculator. The costs can be reduced even further by using preemptible VMs.
While the new cluster used a similar number of nodes, cores, and total memory as the previously-audited cluster, the redesigned architecture allowed us to harness the low latency and high throughput of Local NVMe SSDs.
Resources on demand
The cluster was created on demand using Terraform and Ansible during testing and auditing. The use of infrastructure as code (IaC) techniques ensured that the cluster, fully loaded with the STAC-M3 data set, could be created when needed and then removed when benchmarking was complete. It also meant that the cluster configuration was enforced by code on each deployment, eliminating configuration variance and drift. The full IaC definition to create the cluster can be retrieved from the report in the STAC Vault.
Each time the cluster was created, data was streamed to Local SSDs from Google Cloud Storage, our reliable and secure object storage, at up to the line rate of 32Gbps per node. The entire 57TiB STAC-M3 Antuco and Kanaga data was replicated from Cloud Storage to local storage in approximately 20 minutes.
Since each node was independent and responsible for its own shard of data, doubling the cluster size would cut the synchronization time in half, or copy twice as much data in the same amount of time. Using higher bandwidth options of up to 100Gbps would triple the possible throughput for a relatively small incremental cost, trading an approximately 11%-23% price increase at current list prices for a 200% data synchronization performance increase. Taking advantage of fast networking to cache sharded data in parallel to a large cluster makes storing bulk data in Cloud Storage viable for even the largest workloads.
For quants working on vast data sets in sprawling compute clusters, the ability to fully describe infrastructure as declarative code, create elastic resources on demand, cache data quickly from cheap bulk storage, and turn resources off when computations complete is a dramatic change compared to waiting months to grow on-premises clusters – and a compelling reason to use cloud infrastructure.
To see how we designed and optimized the cluster for API-driven cloud resources, read our new whitepaper.
STAC-A2™: Calculating derivatives risk
In 2018, we showed that cloud instances can outperform bare metal when analyzing large tick history data sets in the demanding suite of STAC-M3 benchmarks. Last year, Google Cloud’s partner Appsbroker showed that the same was true for calculating derivatives risk in STAC-A2 on Google Cloud. You can read about how Appsbroker built its record-breaking STAC-A2 compute cluster on Google Cloud in its blog post, or access the STAC Report directly. Here are the highlights:
Compared to all other publicly reported solutions, this solution, based on a cluster of 10 virtual machines, had:
- The highest throughput (STAC-A2.β2.HPORTFOLIO.SPEED)
- The fastest cold time in the large problem size (STAC-A2.β2.GREEKS.10-100k-1260.TIME.COLD)
Compared to a solution involving an 8-node, on-premises cluster (SUT ID INTC181012), this 10-node, cloud-based solution:
- Had 5 times the maximum paths (STAC-A2.β2.GREEKS.MAX_PATHS)
- Had 10% greater throughput (STAC-A2.β2.HPORTFOLIO.SPEED)
- Was 18% faster in cold runs of the large problem size (STAC-A2.β2.GREEKS.10-100k-1260.TIME)
- Was 9% faster in cold runs of the baseline problem size (STAC-A2.β2.GREEKS.TIME.COLD)
Finding market advantages with Google Cloud
Across the investment management industry, every firm is seeking many of the same competitive advantages. However, finding unique opportunities and managing larger and larger data sets is becoming a major strain. Cloud is fundamentally changing how quants tackle the problem while empowering them to manage risk and generate higher returns.
Building on-premises computing clusters with tens or hundreds of thousands of cores and petabytes of storage requires huge up-front investments and lead time measured in months or years. Google Cloud makes the same scale available to its customers, provisioned on demand and paid per use. More importantly, the elasticity of cloud resources enables agility that is simply not available in a fixed data center cluster – the agility to explore, experiment, iterate, and respond to markets faster than before.
Scaling out to tens of thousands of cores in minutes and then removing the resources immediately not only changes the speed at which questions can be answered; it encourages different and more frequent questions, asked simultaneously on many independent clusters, free from the constraints of fixed on-premises hardware.
It is this flexibility and power that enables financial services firms to leverage larger data sets and get results, backtest, research, and analyze large amounts of data, faster and whenever they need it.
Download our whitepaper to learn more about our latest STAC-M3 tick history analytics benchmark results and how to optimize cloud infrastructure for high-speed market data analysis.
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Indian Retailer Figures Optimizes Hyperlocal Delivery to Increase Customer Experience
Anyone who follows the Indian e-commerce scene knows that one of the largest challenges these companies face is hyperlocal delivery.
That was a problem facing Wellness Forever, a retail chain of pharmacies with 150-plus stores across India.
“Exactly a year ago, we started our journey of hyperlocal deliveries. This optimization was a big time challenge for us to understand how to optimize this,” Palani Subbiah, CTO, Wellness Forever.
The problem in front of Wellness Forever was to identify which customer could can be sold from which store, so that a delivery could be made within 90 minutes.
“We handle a large amount of customer data and we wanted to use insights to help and improve the customer satisfaction index,” says Subbiah.
To do that Wellness Forever leveraged Google Big Query to run massive amount of data to come up with the operational insights. They also used Firebase and Google Maps.
“By 2021, we are going to have about 450 stores. Those stores are going to be not only a physical store, which is a digital store.
Google Cloud’s Firebase Realtime Database and BigQuery AllowsCastbox to Ramp Up Customer Experience
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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.”
ESG Did the Math: It’s Cheaper and Smarter to Migrate Enterprise Data Warehouses to Google BigQuery. Way Smarter
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Enterprise data warehouses (EDWs) are often deemed the most valuable asset in the data center, serving as the backbone of the business. The ongoing insight gained from these solutions has justified the significant up-front capital investments and ongoing operational costs, but the rigidity of the traditional EDW is forcing organizations to reevaluate their approach to analytics and business intelligence.
While legacy EDW solutions were all about throwing as much computational power as possible at a relatively static data set, with the inflow of new and valuable sources of data and the emergence of all-encompassing analytics initiatives, the success of today’s EDW solutions depends more on operational and resource agility than raw horsepower.
Being able to dynamically adjust to the needs of the business, integrate into operational processes, and quickly react to emerging opportunities can place an organization at a distinct competitive advantage.
Today’s EDW solutions must act as a global repository of information, provide the agility to scale up or down on demand, and seamlessly integrate with other analytics tools and services used throughout a data-driven organization.
Over the last two years, ESG has conducted detailed studies quantifying the economic value of Google data analytics services. The first evaluated Google BigQuery compared to on-premises Hadoop and AWS redshift. The second focused on Google DataProc compared to DIY Spark and Hadoop approaches. Here’s the next iteration of our economic analysis, extending the BigQuery study to incorporate a comparison to legacy enterprise data warehouses, both on-premises and in the cloud.
Through publicly available pricing and in-depth qualitative customer interviews, ESG was able to assert a base set of assumptions that power a dynamic model, incorporating up-front capital investments, deployment and migration costs, expected monthly cloud costs, administrative costs, and operational costs associated with legacy on-premises EDWs, cloud-based EDWs, and Google BigQuery.
The crux of the results show organizations can save up to 52% by using BigQuery over on-premises EDWs and up to 41% over cloud-based EDWs. Unlike legacy on-premises EDWs, BigQuery provides organizations with the key abilities that are essential to delivering a modern EDW solution, most notably the ability to integrate across other Google Cloud Platform services, including its market leading AI-based solutions and services.
Although not called t out directly in the published report, ESG’s models indicate that the savings achieved by migrating an on-premises EDW solution to Google BigQuery may actually be more cost effective than simply continuing to operate an existing on-premises EDW solution.
Data-first Digitization Helps Leverage the Cloud for Your Mainframe Assets
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For many enterprises, the venerable mainframe is home to decades’ worth of data about the company’s customers, processes and operations. And it goes without saying that the business would like access to that mainframe data — to report on it, to analyze it with big data analysis tools, or to use it as the basis of new machine learning and artificial intelligence initiatives.
At Google Cloud, we are eager to work with organizations to help them transform their mainframe assets for the cloud era. Of course, we can help them modernize their mainframe applications by migrating them to the cloud. At the same time, working with partners and customers, we’ve developed another, more lightweight approach that can help them start to leverage the cloud for their mainframe assets much more quickly than performing a full-fledged migration. We call this approach data-first digitization.
In this rapidly evolving digital ecosystem, it’s imperative to understand the difference between ‘modernization’ and ‘digitization.’ With modernization you start with the current state and look forward, and rely on mainframe application migration approaches such as rehosting (emulation), refactoring (automated code transformation), reengineering — or simply replacing a custom application with a commercial package. With digitization, you start with the future state that you want to achieve, and work back to what is required to get there.
This data-first digitization approach includes a mainframe data-first integration framework comprising in-house and partner products and tools to migrate heterogeneous data sources from the mainframe to Google Cloud Storage. Once mainframe data has been copied to Cloud Storage, it can then be integrated and leveraged by Google Cloud tools such as BigQuery, AI and machine learning prodcuts and Smart and Stream analytics platforms. The integration framework covers both bulk batch data transfers and real-time data replication (change data capture).
Data-first digitization is based on the tenet that ‘applications are transient, data is permanent.’ By bringing data first to Google Cloud instead of traditional ways of modernizing applications (for example, with Gartner’s 7 options to Modernize), this allows organizations to leapfrog to new business models, use cases and innovative ways to serve end customers. For example:
- Making decisions with smart and stream analytics platforms and AI/ML engines. These tools need data to make decisions. Google is a pioneer in extracting information and value from the raw structured and unstructured data, and this approach opens up mainframe data for use by BigQuery and AI/ML models.
- Building new reporting applications. With access to mainframe data, you can use Google cloud products like Looker and Appsheet to build net-new reporting applications, expediting the process of retiring mainframe reporting applications, and accelerating your overall transformation.
In our experience, taking a data-first digitization approach to your mainframe offers a number of benefits:
- Faster time-to-business: Because data-first modernization is built on existing products, the implementation cycle is much shorter.
- Less capital investment: You spend your time integrating products, not developing applications.
- Minimized risk: Data-first integrates with existing, proven and reliable Google Cloud products.
- Faster overall mainframe transformation: When you shift your modernization center of gravity from the application to the data, you look at mainframe applications from a business perspective instead of just “keeping the lights on.” As a result, only the most business-critical applications are modernized and many support applications can be decommissioned, accelerating your transformation journey.
Taking a data-first approach to digitization is still relatively new, but we’re heartened by customers’ early successes. Watch this space for additional insights, reference architectures and technical white papers around data-first. And if you think this approach may be right for you, reach out to mainframe@google.com.
Learn more:
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