Thirteen years’ experience in e-commerce has given Teddy Chan, Chief Executive Officer and Chief Technology Officer, AfterShip, a deep understanding of the challenges of shipping and tracking packages to customers worldwide.

“The key problem many merchants face is customers asking ‘where is my order?’ and ‘when I will get the package?’” Chan says. “When I considered this issue, I came up with the idea of AfterShip.” Chan helped found AfterShip in 2011 to enable merchants to keep track of packages sent to customers via a web portal or an API. AfterShip also allows merchants to notify customers of anticipated delivery times.

“Using AfterShip, merchants can provide the same experience to buyers regardless of which couriers they use,” Chan says. “Merchants can also improve their customer engagement by including up-selling or marketing content with their delivery notifications.”

Hong Kong-headquartered AfterShip continues to grow quickly and now has a 40-person team. Thirty members of this team are based on the China special administrative region and 10 are based in India.

“Google Cloud Platform has a network of global datacentres with deep connectivity that enables us to put our infrastructure close to our customers. In addition, the ability to horizontally scale our global database using tools such as Google Cloud Spanner eliminates any limits on our geographic expansion.”

—Teddy Chan, Chief Executive Officer and Chief Technology Officer, AfterShip

By October 2017, the business was tracking about 30 million packages per month and had expanded its services to include label and rate calculation and self-service return. Revenue, package transaction numbers and team size have doubled every year for the past three years, while more than 300,000 merchants and 426 couriers are signed up to the service. Key customers include Wish, Etsy and Groupon.

Close to half AfterShip’s customers are based in the United States, about one third in Europe and the remainder are located in Asia. AfterShip had initially delivered its applications and services from an incumbent public cloud service. However, the company wanted to continue its growth trajectory while automating key infrastructure processes, implementing a continuous deployment model and controlling costs.

The business needed to achieve these objectives while maintaining a global presence and high-quality service. AfterShip started reviewing its options and decided to migrate to Google Cloud Platform (GCP). “Google Cloud Platform has a network of global datacentres with deep connectivity that enables us to place our infrastructure close to our customers,” Chan says.

“In addition, the ability to horizontally scale our global database using tools such as Google Cloud Spanner eliminates any limits to our geographic expansion. Furthermore, the managed services provided through GCP would allow us to focus on building better features for online merchants.” The reliability provided by GCP would also enable AfterShip to meet the stringent service level requirements of large digital marketplaces in the United States, Asia and elsewhere.

“Google Cloud Platform could manage the high volumes and enable us to deliver the service levels that would realise our ambition of becoming the number one tracking API platform in the world,” Chan says. “For example, with Google Cloud Platform, we can provide a 99.95% monthly uptime service level to our customers.” Finally, GCP provided managed solutions, including Google Kubernetes Engine powered by open source container orchestrator Kubernetes, that would enable AfterShip to automate processes such as scaling and enable its team to focus on developing applications.

AfterShip has moved its websites into GCP infrastructure in three datacentres around the world and anticipates completing the migration in Q4 2017. “Google provided a lot of assistance, particularly early in the project when we needed it,” Chan says. “They briefed us on several services we hadn’t known about that could replace the equivalents in the public cloud we were using previously.” The business then completed the migration using its own skilled team members. As well as Google Kubernetes Engine and Google Cloud Spanner, AfterShip is using Google BigQuery to store and analyse transaction information.

“Google Cloud Platform could manage the high volumes and enable us to deliver the service levels that would realise our ambition of becoming the number one tracking API platform in the world.”

—Teddy Chan, Chief Executive Officer and Chief Technology Officer, AfterShip

Deployment times down from one hour to two minutes

With deployment times falling from up to one hour in its previous cloud environment to about two minutes in GCP, AfterShip has been able to adopt a continuous deployment model. “This has improved our service levels,” Chan says. “If there are any issues we can fix them quickly, while we can iterate faster to create new features in response to customer requests or changes in the market. “This enables us to continue to lead our competitors.”

Targeting a 30 percent reduction in costs

AfterShip is now targeting a 30% reduction in costs by optimising its use of Docker containerisation technology on GCP.

“By using Docker with Kubernetes, we have been able to fine-tune our use of compute resources and better control our costs,” Chan says. “We’re extremely pleased with Google Cloud Platform as it really is built for engineers,” he adds. “In addition, its documentation is extremely clear, allowing us to troubleshoot or carry out activities on the platform ourselves. “We look forward to continuing to grow and extend our package tracking and associated services with Google Cloud Platform.”

Like countless other industries, farming is going digital and undergoing big changes—driven by access to more actionable information. The agriculture business can now gather and analyze georeferenced data from satellites, combined with data from IoT sensors in fields, crop rotation and yield histories, weather patterns, seed genotypes and soil composition to help increase the quantity and quality of crops.

This is essential for businesses in the agriculture industry, but it’s also critical to address growing food shortages around the world. 

At S4, we create technology to de-risk crop production. We provide customers seeking agricultural risk management solutions with the tools to make better, data-driven decisions for their crop planning, based on machine learning and proprietary algorithms.

We interpret plant evolution on a global scale with predictive modeling and analytics, and offer super-efficient risk-transferring solutions. Our multi-cloud platform includes a petabyte-scale database, an open source stack, and—after 50 proof-of-concept evaluations—BigQuery for our data warehouse and the Cloud SQL database service to handle OLTP queries to our PostgreSQL database.

These PoCs included, among others, Microsoft Azure Data Lake Analytics, IBM Netezza, Postgres/PostGIS running on IBM bare-metal servers with SATA SSDs and on Google’s Compute Engine with NVMe disks, and on-premises memSQL, CitusData and Yandex ClickHouse. 

Weeding out risk in an uncertain market

According to recent research, climate extreme events like drought, heat waves, and heavy precipitation are responsible for 18-43% of global variation in crop yields for maize, spring wheat, rice, and soybeans. This is a clear trend for other crops as well. Such variation poses risks of food shortages as well as large financial risks to farmers, insurers, and regions dependent on successful crop yields. Also, it creates vast humanitarian difficulties.

Our mission at S4 is to help de-risk crop production by matching the right data with analytics tools so farmers and other participants in the agricultural value chain can plan better, resulting in more reliable food supplies.

In a nutshell, we create indices out of biological assets. These indices measure yield losses on crops that are caused by the effects of weather and other factors, which are then used as underlying assets for products, such as swap/derivative contracts and parametric insurance policies, to transfer risk to the financial markets.

We enable insurers and lenders to buy and sell agricultural risks through the futures market. Also, our other products help farmers and seed and fertilizer companies provide customized genotype recommendations and fertilization requirements. This helps to optimize planting by geography, resources, and crop species, monitor phenological, pests and humidity evolution throughout the crop season, and estimate yields.

Local communities benefit from S4’s technology, as the ability to manage weather risks allows farmers to stabilize their cash flows, invest more to produce more with fewer risks, and develop in a more sustainable manner.

Growing data sources, reducing costs, accelerating performance

With the volume of diverse data sources and analytical complexity both growing at a very fast pace, we decided that using a major cloud services provider with a broad roadmap and global partnerships would be beneficial to S4’s future evolution.

At the same time, we wanted to bring our services to users faster and cut costs by consolidating our on-premises technology stack. When we started evaluating providers, our leading criteria included a powerful geospatial database and data analytics tools along with excellent support, all at a competitive price. GCP prevailed in nearly all criteria categories among the 50 companies we measured. 

Our previous platform architecture included a hybrid relational database that used Compute Engine for virtual machines and Cloud Storage for database backup. The RDBMS was slow. Maintaining our own data warehouse was complex and expensive.

We wanted to use machine learning and neural networks, but couldn’t do so easily and affordably. The complexity of that system meant that products or services requiring small changes or additions to the data model translated to expensive expansions of infrastructure or project time.

Also, agronomical or product teams couldn’t test these changes by themselves, always requiring the intervention on no small part of the IT team, which led to further delays.

We added GCP services like BigQuery as S4’s cloud data warehouse and use BigQuery GIS for geospatial analysis, Cloud Dataflow for simplified stream and batch data processing, and Cloud SQL for queries to the S4 database platform, which have all made a huge impact on our services and bottom line.

Database and analytics costs have decreased by 40% and customers are receiving our analytical results 25% faster. In addition, we’ve eliminated the time-consuming downloading of images, reducing storage and processing costs by 80%, because we no longer need expensive tools licenses, and have greatly reduced classification processing times.

Our customers working in the agriculture industry are also benefiting from this infrastructure change. They are now able to speed up their data analytics using our GCP-based platform.

“S4 products and technologies unlock the full potential of satellite imagery for crop prescriptions, monitoring and yield estimates,” says Nicolás Loria, Manager of Marketing Services, Southern Cone, Corteva Agriscience.

“We’ve worked with S4 for the last three (and starting year number four) crop seasons as its team capabilities, data integration capacities, and analytics insights have allowed Corteva to perform an entire new solution. Thanks to S4’s customized 360° approach, fast response and delivery times, we have safely outsourced our remote crop analytic technical needs.”

Also, this new architecture has allowed us to scale our models and databases with almost no limits, at a fraction of the cost vs. the previous models.

We’ve saved a lot of time on executing processes and reduced work needed by our internal teams to do certain tasks, like preparing images, converting them, validating results, and more. Using Google Earth Engine has decreased the execution time of daily tasks anywhere from 50% to 90% of the previous time, going from an average time of 30 minutes to between four and 15 minutes, depending on the task.

In addition to saving money and time, we are able to focus on innovation with the GCP performance and features we’re using. We’re able to seamlessly add satellite data to analytics using both public datasets and our own private data, and deliver GIS data management, analytics, crop classification and monitoring in real time.

We can do semi-automatic crop classification and classification using spectral signatures with Google Earth Engine. Later this year, we’ll be using neural networks for pattern recognition and machine learning in new applications to improve crop yields and fine-tune risk models. And using GCP and Google Earth Engine infrastructure means we can run models for customers in South America and around the world, since Google Earth Engine has global satellite imagery available. 

We’ve heard from our customer Indigo Argentina that they’re able to bring customers data insights faster.

“We are working with S4 in the development of two different applications for satellite crop monitoring and yield assessment,” says Carlos Becco, CEO, Indigo Argentina. “S4’s technology allowed us to manage and analyze multiple sources and layers of information in real time, letting us uncover valuable insights in Indigo’s own microbiome technologies, and at a very competitive cost.” 

Analytical products and app development thrive with GCP

With GCP, we are updating and improving algorithms that we built manually with machine learning processes to develop drought indices for upcoming crop seasons. Algorithms can recognize specific phases of crop phenology (e.g., bud burst, flowering, fruiting, leaf fall) and correlate them with photosynthetic activity, light, water, temperature, radiation, and plant genetics factors. Other analytical products like crop monitoring, pre-planting recommendations, financial scoring, and yield estimation can now do a lot more for users by offering multiple layers and datasets, faster image processing, and real-time access via APIs.

We also replaced our bare-metal S4 app deployment with the App Engine serverless application platform. It provides tighter integration between the S4 platform and our BigQuery data warehouse for integration with marketplaces and third-party solutions.

We get all of these Google Cloud features with all the benefits of managed cloud services, from multiversioning and security to automatic backups and high availability.

At S4, we trust technology to decode plant growth and help protect farmers and their communities from climate change. With growing food shortages due to increasing populations and intensifying weather, data and analytics can have a huge impact in lowering financial risks and improving agricultural yields. It’s one sector where cloud, database, analytics, and other technologies are combining to improve business outcomes and affect the lives of billions of people. Learn more about S4’s work and learn more about data analytics on Google Cloud.

As data environments become more complex, companies are turning to streaming analytics solutions that analyze data as it’s ingested and deliver immediate, high-value insights into what is happening now. These insights enable decision makers to act in real time to take advantage of opportunities or respond to issues as they occur.

While understanding what is happening now has great business value, forward-thinking companies are taking things a step further, using real-time analytics integrated with artificial intelligence (AI) and business intelligence (BI) to answer the question, “what might happen in the future?” Arkansas-based Tyson Foods has embraced AI/BI analytics to enable predictive insights that unlock new opportunities and drive future growth.

Creating a digital twin for connected intelligence company wide

Before using AI/BI, Tyson’s analytics capabilities consisted of traditional BI solutions focused on KPIs and simplifying data so that humans could understand it. Tyson wanted to leverage its data to uncover ways to improve current processes and grow its business. But with BI alone, Tyson struggled to use data to run the simulations and scenarios essential to make educated decisions. To keep growing, it had to embrace the complexity of its data, building ways to analyze it and use it to inform decision making. 

Tyson’s on-premises analytics solutions limited its ability to be aggressive and make intelligent, timely, prescriptive decisions. The solution was to create a digital twin to scale optimizations within business processes, moving from local optimizations to system-wide connected optimizations. Doing so meant shifting entirely to cloud computing, with an initial focus on building the ingestion component of the digital twin platform.

Investing in a digital twin enabled Tyson to accelerate new capabilities like supply chain simulation “what-if” scenarios, prescriptive price elasticity recommendations, and improvement of customer intimacy. 

Solving the ingestion problem for faster time to insights

Before its migration to Google Cloud, analytics projects that Tyson suffered from uncertainty over how to obtain the data. This problem was prolific and caused project times to be extended for weeks or even months due to the need to write and support one-off data ingestion processes at the front end. This problem also prevented the IT team from delivering analytics solutions fast enough for the business to take full advantage of them. 

To solve this analytics problem, the team created Data Ingestion Compute Engine (DICE). DICE is a Google Cloud-hosted, open-source, cloud-native ingestion platform developed to provide configuration-based, no-ops, code-free ingestion from disparate enterprise data systems, both internal and external. It is centered on three high-level goals:

1. Accelerate the speed of delivery of IT analytics solutions
2. Enable growth of IT capabilities to produce meaningful insight
3. Reduce long-term total cost of ownership for ingestion solutions

Creating DICE ingestion platform with Google Cloud services

Teams use DICE to set up secure data ingestion jobs in minutes without having to manage complex connections or write, deploy, and support their own code. DICE enables unbound scale, highly parallel processing, DevSecOps, open source, and the implementation of Lambda Data Architecture.

A DICE job is the logical unit of work in the DICE platform, consisting of immutable and mutable configurations persisted as JSON documents stored in Firestore. The job exists as an instruction set for the DICE data engine, which is Apache Beam running Dataflow to instruct which data to pull, how to pull it, how often to pull it, how to process it, when it changes, and where to direct it.

Two of DICE’s primary layers include the metadata engine and the data engine. The metadata engine is responsible for the creation and management of DICE job configuration and orchestration. It is made up of many microservices that interact with multiple Google Cloud services, including the job configuration creation API, job build configuration helper API, and job execution scheduler API.

The data engine is responsible for the physical ingestion of data, the change detection processing of that data, and the delivery of that data to specified targets. The data engine is Java code that uses the Apache Beam unified programming model and runs in Dataflow. It is comprised of streaming, jobs, and Dataflow flex template batch jobs. Logically, the data engine is segmented across three layers: the inbound processing layer, the DICE file system layer, and the target processing layer, which takes the data from the DICE file system and moves it to targets.

Rolling DICE for thousands of ingestion jobs each day

DICE was first deployed to a production environment in November 2019, and just two years later, it has more than 3,000 data ingestion jobs from more than a hundred disparate data systems, both internal and external to Tyson Foods. Most of these jobs run multiple times a day. On a daily basis the DICE environment sees more than 25,000 Dataflow jobs running and an average of 3.25 terabytes of new data being ingested.

DICE supports ingestion from many different types of technologies, including BigQuery, SQL Server, SAP HANA, Postgres, Oracle, MySQL, Db2, various types of file systems, and FTP servers. Additionally, DICE supports target platform technologies for ingestion jobs that include multiple JDBC targets, multiple file system targets, and BigQuery and queue-based store and forward technologies. 

The platform continues to see linear growth of DICE jobs, all while keeping platform costs relatively flat. With increasing demand for the platform, Tyson’s IT team is constantly enhancing DICE to support new sources and targets.

This intelligent platform keeps adding new value and makes it simple for Tyson to take advantage of its data. This innovation is a necessity in this fast-changing world of digital business in which companies must transform a high volume of complex data into actionable insight.