Google Introduces BigQuery Connector for SAP to Power Customers’ Data Analytics Strategy

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Google Cloud has a genuine passion for solving technology problems that make a difference for our customers. With the release of our BigQuery Connector for SAP, we’re taking a another big step towards solving a major challenge for SAP customers with a quick, easy, and inexpensive way to integrate SAP data with BigQuery, our serverless, highly scalable, and cost-effective multi cloud data warehouse designed for business agility.
Solving for simplified data integration
Like most businesses today, SAP customers are eager to unlock the immediate insights and opportunities within their ever-growing stores of business data. However, many are discovering just how hard it can be to take the first step in any modern, cloud-enabled data analytics strategy: combining SAP data with other cloud-native, and enterprise data sets in real-time and at scale. According to a 2020 SAPInsider study, more than half of SAP customers surveyed said data integration was their top analytics pain point. These companies urgently need a rapid, sustainable, cost-effective and scalable way to integrate SAP data with modern cloud data analytics solutions.
The BigQuery Connector for SAP gives our customers a solution: a fast, simple, cost-effective and massively scalable way to make SAP data fully accessible within BigQuery by leveraging customers’ existing SAP Landscape Transformation Replication Server (SLT) tooling and skill sets. It’s the first SAP SLT direct near real-time connector for BigQuery without the need to set up additional infrastructure or third-party middleware, and can be deployed using a variety of embedded or stand-alone deployment options. In fact, most customers can install the BigQuery Connector for SAP in less than an hour—a remarkably easy way to start working with our industry-leading analytics solution that delivers proven and quantifiable business advantages for customers. Additionally, the BigQuery Connector for SAP is not restricted to customers who have deployed their SAP applications on Google Cloud. Customer’s who are running their SAP applications on-premise, or on any cloud, can also deploy and realize the analytical benefits of the solution.
Designing a solution with customer requirements and investments in mind
When the Google Cloud team started work on an analytics data integration tool for our SAP customers, we began with a set of requirements designed to root out the usual sources of cost and complexity. These included:
- The need for real-time performance with deltas replicated in milliseconds
- The ability to integrate data from almost any SAP Netweaver based application running today, regardless of its deployment location (on premises, any cloud, Google Cloud)
- Automatic BigQuery data type mapping with minimal transformation required
- Generation of target tables in BigQuery directly from source, if required
- Application layer integration that avoids the issues of direct database access
- Leveraging customers’ existing SAP skillsets, change data capture, and infrastructure
An important step towards meeting these requirements came when Alphabet, Google’s parent company, decided to leverage SAP SLT as a foundation for developing direct data replication between SAP and BigQuery for its internal corporate landscape. SLT as part of SAP’s strategic Business Technology Platform, supports real-time replication of data from SAP or third-party systems to SAP HANA, however, one of its limitations was direct integration with targets like BigQuery.
SAP SLT was a logical foundation for developing the connector for several reasons:
- It’s widely adopted among SAP customers who likely already leverage SLT for SAP analytics data integration
- It works with almost every non-SaaS SAP application environment running today
- It supports real-time replication performance at massive scale
It was an obvious choice for the Alphabet engineering team who saw immediate value from integrating SAP with BigQuery.
“The BigQuery Connector for SAP has enabled fast, low latency data replication for billions of records from 500+ tables of our most critical financial and supply chain data. Now in one cost-effective BigQuery data lake, this ERP data can be combined with other data sources for previously impossible real-time analytics and ML use cases. This allows us to drive much deeper strategic insights that support business and operational excellence, management and P&L reporting and more.”—Anil Nagalla, Sr. Engineering Director, Financial Systems, Google
SAP data integration with BigQuery enables new value
By leveraging SAP SLT, the BigQuery Connector for SAP can integrate real-time data streams from any SAP system—while also taking advantage of customers’ existing SAP investments and skillsets.
At the same time, the BigQuery Connector for SAP does a lot of heavy lifting on its own. For example, it automatically handles the complex, multi-step process of transforming SAP data types for use in BigQuery—mapping data-type transitions between the SAP and BigQuery environments, creating a target table schema on BigQuery for the transformed data types, building the target BigQuery table, and even adapting as new data types appear in your SAP environment.
For teams that want to fine-tune the BigQuery Connector for SAP’s automated recommendations, the connector supports additional levels of customization and choice. But if you simply want to get the job done and give your data analytics team greater support for their high-value work, then you’ll love just how quickly and easily the BigQuery Connector for SAP turns the complicated work of data integration and performance to process large volumes of data into a done deal. By integrating enterprise data sets in real time, customers can drive differentiated value and unlock new insights and actions that drive a competitive advantage.
The BigQuery Connector for SAP really shines as an enabling tool that transports and transforms your SAP data to power analytics solutions enabled by accelerators like the Google Cloud Cortex Framework: a comprehensive set of reference architectures, deployment accelerators, and integration services designed to give SAP customers a fast and seamless path to value with their data analytics investments. Simply put, the more SAP data you make available within Google Cloud, the easier it is to get meaningful—and often game-changing—insights from these solutions.
Learn more about the BigQuery Connector for SAP
Ready to get started with your own SAP data analytics strategy on Google Cloud? Install the Google Cloud BigQuery Connector for SAP, and discover a faster, simpler, more sustainable way to power your company’s data analytics strategy.
Manhattan Associates’ Seamless Migration to Google Cloud SQL

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Editor’s note: Manhattan Associates provides transformative, modern supply chain and omnichannel commerce solutions. It enhanced the scalability, availability, and reliability of its software-as-a-service through a seamless migration to Google Cloud SQL for MySQL.
Geopolitical shifts and global pandemics have made the global supply chain increasingly unpredictable and complex.
At Manhattan Associates, we help many of the world’s leading organizations navigate that complexity through industry-leading supply chain commerce solutions like warehouse management, transportation management, order management, point of sale and much more, to continuously exceed increasing expectations.
The foundation for those solutions is Manhattan Active® Platform, a cloud-native, API-first microservices technology platform that’s been engineered to handle the most complex supply chain networks in the world and designed to never feel like it.
Manhattan Active solutions enable our clients to deliver exceptional shopping experiences in the store, online, and everywhere in between. They unify warehouse, automation, labor and transportation activities, bolster resilience, and seamlessly support growing sustainability requirements.
More Resiliency and Less Downtime
Manhattan Active solutions run 24×7 and need a database solution that can support this. Cloud SQL for MySQL helps us meet our availability goals with automatic failovers, automatic backups, point-in-time recovery, binary log management, and more. Cloud SQL also allows us to create in-region and cross-region replicas efficiently with near zero replication lags. We can create a new replica for a TB size DB in under 30 minutes, a process which used to take several days.
We provide a 99.9% overall up-time service level agreement (SLA) for Manhattan Active Platform, and Cloud SQL helps us keep that promise. Unplanned downtime is 83% less than it would have been with our previous database solutions.
Flexibility and Total Cost of Ownership
One of the fundamental requirements in a cloud-native platform like Manhattan Active is a robust, efficient, and cost-effective database. Our original database solutions struggled across different cloud platforms and created challenges in total cost of ownership and licensing.
We needed a more cost-efficient approach to managing a highly reliable and available database engine that could operate as a managed service, and Cloud SQL delivered.
We were able to move every Manhattan Active solution from our previous cloud vendor to Google Cloud, including the shift to Cloud SQL, with less than four hours of downtime.
Today, we run hundreds of Cloud SQL instances and operate most of them with just a few database administrators (DBA). By offloading the majority of our database management tasks to Cloud SQL, we significantly reduced the cost to maintain Manhattan Active Platform databases.
We also need a solution where we resize our database within minutes. This requirement is needed to manage database performance and infrastructure costs. The ease of resizing our database within minutes allows us to keep the optimal performance levels and saves significantly on overall infrastructure costs.
A Winning Innovation Combination
Cloud SQL provides highly scalable, available, and reliable database capabilities within Manhattan Active Platform, which helps us provide significantly better outcomes for our clients and better experiences for their customers.
Learn more about how you can use Cloud SQL at your organization. Get started today.
Carbon Health: Transforming outcomes with Connected Sheets for Looker

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Everyone wants affordable, quality healthcare but not everyone has it. A 2021 report by the Commonwealth Fund ranked the U.S. in last place among 11 high-income countries in healthcare access.1 Carbon Health is working to change that. We are doing so by combining the best of virtual care, in-person visits, and technology to support patients with their everyday physical and mental health needs.
Rethinking how data and analytics are accessed at Carbon Health
Delivering premium healthcare for the masses that’s accessible and affordable is an ambitious undertaking. It requires a commitment to operating the business in an efficient and disciplined way. To meet our goals, our teams across the company require detailed, daily insights into operating results.
In the last year, we realized our existing BI platform was inaccessible to most of our employees outside of R&D. Creating the analytics, dashboards, and reports needed by our clinic leaders and executives required direct help from our data scientists.
However, this has all changed since deploying Looker as our new BI platform. We initially used Looker to build tables, charts, and graphs that improved how people could access and analyze data about our operating efficiency. As we continued to evaluate how our data and analytics should be experienced by our in-clinic staff, we learned about Connected Sheets for Looker, which has unlocked an entirely new way of sharing insights across the company.
A new way to deliver performance reporting and drive results
Connected Sheets for Looker gives Carbon Health employees who work in Google Sheets—practically everyone—a familiar tool for working with Looker data. For instance, one of our first outputs using the Connected Sheets integration has been a daily and weekly performance push-report for the clinic’s operating leaders, including providers.
Essentially a scorecard, the report tracks the most important KPIs for measuring clinics’ successes, including appointment volume, patient satisfaction such as net promoter score (NPS), reviews, phone call answer rates, and even metrics about billing and collections. To provide easy access, we built a workflow through Google App Script that takes our daily performance report and automatically emails a PDF to key clinic leaders each morning.
Within the first 30 days of the report’s creation, clinic leaders were able to drive noticeable improvements in operating results. For instance, actively tracking clinic volume has enabled us to manage our schedules more effectively, which in turn drives more visits and enables us to better communicate expectations with our patients. Other clinics have dramatically improved their call answer rates by tracking inbound call volume, which has also led to better patient satisfaction.
Greater accountability, greater collaboration
As you can imagine, a report that holds people accountable for outcomes in such a visible way can create some anxiety. We’ve eased those concerns by using the information constructively, with the goal to use reporting as a positive feedback mechanism to bolster open collaboration and identify operational processes that need improvement. For example, data about our call answer rates initiated an investigation that led to an operational redesign of how phones are deployed and managed at more than 120 clinics across the U.S.
Looker as a scalable solution with endless applications
We’re now rolling out Connected Sheets for Looker to deliver performance push-reporting across all teams at Carbon Health. Additionally, we continue to find new ways to leverage Connected Sheets for Looker to meet other needs of the business.
For instance, we’ve recently been able to better understand our software costs by analyzing vendor spend from our accounting systems directly in Google Sheets. Going forward, this will allow us to build a basic workflow to monitor subscription spend and employee application usage, which will lead to us saving money on unnecessary licenses and underutilized software.
We’ve come a long way in the last year. Between Looker and its integration with Google Sheets, we can meet the data needs of all our stakeholders at Carbon Health. Connected Sheets for Looker has been an impactful solution that’s going to help us drive measurable results in how we deliver premium healthcare to the masses.
1. Mirror, Mirror 2021: Reflecting Poorly
2. HEALTHCARE EDITORS’ PICK Meet The Immigrant Entrepreneurs Who Raised $350 Million To Rethink U.S. Primary Care
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What is Google Cloud SQL?
Cloud SQL is a fully managed relational database for MySQL, PostgreSQL, and SQL Server.
It reduces maintenance cost and automates database provisioning, storage capacity management, replication, and backups. It offers quick setup, with standard connection drivers and built-in migration tools.
How Do You Set It Up?
Cloud SQL is easy to setup. You select the region and zone where you would like the instance to be, and it gets created there.
You also have a lot of configuration options, where you can select the machine type with the right number of CPU and amount of memory you need.
Choose storage each type between solid state and hard disk drives, depending on latency queries per second and cost requirements and set storage capacity.
Higher storage capacity leads to better performance.
What About Back Up?
Cloud SQL also offers automated backups and recovery options.
You can set time slots and locations for backups. For production applications, it is recommended to enable high availability, or HA.
By enabling this feature, the database instancewill automatically failover to another zone in your selected region in case of an outage.
You can also create cross-regional replicas to protect from regional failures.
In addition, you can enable automatic storage increase to add more storage when nearing capacity.
How Do You Migrate an Existing MySQL Database to Cloud SQL?
Cloud console makes it very easy by providing a migrate data button, which guides you through easy steps.
First, you provide your data source details, things like public IP address, port number,
and your replication credentials.
Second, you create a Cloud SQL read replica, just like we discussed in the creation process, using a SQL dump file.
Third, you sync the read replica with source. And, finally, you promote the read replica to primary instance with very low downtime.
Is Data Safe in Cloud SQL?
Like anything else in Google Cloud,the data in Cloud SQL is encrypted at rest and in transit.
External connections can be encrypted using SSL or Cloud SQL Proxy, which is a tool to help you connect to your Cloud SQL instance from your local machines.
You can use Cloud SQL as a relational database for your applications that are hosted within Google Cloud, like App Engine, Cloud Run, Compute Engine, Kubernetes Engine, or Cloud Functions.
You can also connect your Cloud SQL database with applications that are hosted outside of Google Cloud.
How Much Does It Cost and What Are Some Uses Cases?
Cloud SQL pricing varies depending on MySQL, PostgreSQL,or SQL Server.
Broadly speaking, though, it’s the combination of the type of instance, storage, or network you use. SQL Server also has some licensing costs.
Since Cloud SQL is a relational database, you can use it with any online transaction processing apps, such as order or payment processing apps, where you need to handle frequent queries with fast response times.
Fortress Vault Joins Forces with Google Cloud: Launches Private Data Storage for NFTs

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Over the past two years, the general population has become more acquainted with cryptocurrencies and the first iterations of NFTs, which were among the earliest use cases for blockchain technology. This public awareness and participation has led to a growing interest in, and demand for, Web3 technology at the enterprise level.
But building trust in a new wave of technology, especially in large organizations, doesn’t happen overnight. That is why it’s critical for Web3 technologists to bring the broader benefits, use cases, and core capabilities of blockchain to the forefront of the conversation. If businesses don’t understand how this new technology can help them, how can they prioritize it among competing tech plans and resources? And without baseline protocols that account for privacy, confidential data, and IP, how can they future-proof a business?
Answering these questions and delivering trustworthy infrastructure is exactly why Scott Purcell and I founded Fortress Web3 Technologies — to bring about the next wave of Web3 utility. The company’s goal is to provide infrastructure that eliminates barriers to Web3 adoption with RESTful APIs and widgetized services that enable businesses to quickly launch and scale their Web3 initiatives.
Our tools include embeddable wallets for NFTs and fungible rewards tokens; NFT minting engines; and core financial services. These include payments, compliance, and crypto liquidity via our wholly-owned financial institution, Fortress Trust. Being overseen by a chartered, regulated entity ensures privacy, compliance and business continuity.
Fortress chose Google Cloud to help usher in this new-wave technology because no other cloud provider is better suited to helping regulated industries get up to scale on our Web3 infrastructure and blockchain technology. I’ll get into more specifics below, but at the highest level: IPFS (the current standard distributed storage) is going to face major resistance when it comes to industries that are heavily regulated or deal in ownership rights. By leveraging Google Cloud, which has critical certifications such as HIPPA, Department of Defense, ISO, and Motion Picture, we’re striking the appropriate balance between decentralization and centralization, using the best of both technologies.
The Fortress Vault on Google Cloud is a huge and necessary step forward as the first ever NFT-database solution to protect intellectual property, confidential documents, and other electronic records. It represents the first technology that marries privately stored content with the accessibility, privacy, portability, and provenance that blockchain provides.
Understanding Non-Fungible Tokens (NFTs)
An NFT is not an expensive jpeg. From a technical point of view, an NFT is a unique key stored in a distributed and trustless ledger we call a blockchain. This blockchain token is uniquely identifiable from any other token and acts as a digital key to authenticate ownership and unlock data held in a database.
While different blockchains have adopted different standards, Ethereum standards are a good proxy to represent overall concepts. Going back to the primitives, if you read the EIP 721 proposal, metadata is explicitly optional. While today’s NFT hype has indeed leveraged that technology to monetize and distribute digital art, the potential of blockchain is in the ability to digitally represent ownership of a wide variety of different asset classes on a decentralized ledger.
Unique, non-fungible tokens are not a new concept. We use them every day in technical systems for things like authentication, database keys, idempotency, and much more. Now, thanks to blockchain technology, you can take those out of their walled gardens and into an open platform that can lead to transformational utility and applications.
Take real estate, for example. Instead of a paper-based title documenting you as the owner of your home, imagine that the title is tokenized with an NFT on a blockchain. Any platform could cryptographically verify the authenticity of that form of title along with its provenance in real time and confirm that you’re the rightful owner of that property.
But, perhaps you don’t want the title of your property visible to others, nor the associated permits, tax documents, architectural drawings, contractor lists, and other documents. Maybe you just want banks, insurance companies, and others to be able to confirm that you are indeed the owner without revealing the details of those records. The NFT metadata records immutable public-facing provenance, while the underlying data remains private and protected using Fortress Vault on Google Cloud.
Apply that same utility to other sensitive information such as medical records, intellectual property, estate documents, corporate contracts, and other confidential information and it’s easy to see how enterprises are just now exploring how to hold traditional assets as NFTs.
Fortress Vault: Intellectual Property, Confidential Documents, and Other Electronic Records
What NFTs and Web3 have been lacking is the ability to make the tokenized data accessible exclusively by the owner — and only the owner. NFTs are a digital key to unlock everything ranging from music and event tickets, to real estate deeds and healthcare records, to estate documents, and to everything in the world that’s digital.
This is why we created the Fortress Vault. When building it, we had to make a fundamental decision: Either go with a distributed and permissionless storage protocol like IPFS, filecoin, or other blockchain-based database offerings, or work with an industry-leading cloud platform that understands data integrity and is establishing itself as the leader in the space.
Ultimately, we chose Google Cloud for its industry-leading object storage, professional management, fault tolerance, and myriad of certifications for architecture and data integrity.
Some of the challenges faced when vaulting a vast variety and quantity of digital content at scale include:
- Balancing data availability versus cost of storage
- Data redundancy
- Long term archival needs
- Business continuity
- Flexibility to meet current and future needs of the rapidly evolving Web3 industry.
Google Cloud is the clear leader across all of these pain points. The object lifecycle management of Google Cloud Storage enables efficient transition between storage classes when either the data matures to a certain point or it’s updated with newer files. Content in the Fortress Vault can range from on-demand data to long-term uses, such as estate planning documents that won’t be accessed for 30 years.
When storing NFT data, robust disaster recovery is table stakes. We quickly gravitated to the automatic redundancy options and multi-region storage buckets that let us customize where we store our data without massive devops and management overhead. By leveraging Google Cloud, we can offer industry leading retention, redundancy, and integrity for our customers’ NFT content.
Working with a leader in data storage was key to making this a reality. Additionally, Google Cloud shares our vision of bringing every industry forward into the world of Web3. We are both focused on building the critical infrastructure that allows everyone from Web3 native companies to Fortune 500 brands navigate the strategic shift to blockchain technology.
Why Web3 Matters
“Web3” is shorthand for the “third wave” of the internet and the technological innovation that brought us here.
Web 1 — the earliest internet — democratized reading and access to information, opening the doors to mass communication. Web 2 expanded on that with the ability to read and “write.” It democratized publishing by letting people directly engage in producing information through blogs, social media, gaming, and contributions to collective knowledge.
Web 3 expands our technological capabilities even more with the ability to read, write, and “own.” With blockchain, we can now establish clear provenance with visibility into the origination of ownership of any tokenized asset, and we can see the chain of ownership. We can rely on this next-generation technology to track, authenticate, protect, and keep a ledger of our assets.
With the Fortress Vault on Google Cloud, we have the capability to ensure the integrity of non-public data while making it accessible via NFTs. This is a game changer for Web3 adoption, particularly in industries like music, event ticketing, gaming, finance, transportation, real estate, and healthcare. Every industry can benefit from the ability to tokenize assets on blockchain technology without leaving the trusted safety of Google Cloud data storage.
The market for NFTs is everyone. And the Fortress Vault on Google Cloud is the technology evolution that makes it possible for Web3 innovators to confidently build, launch, and scale their initiatives across every industry imaginable.
AgroStar: Small farms in India getting big help from the cloud

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AgroStar has launched a cloud-based mobile app that is helping to boost crop yields and encourage best practices for small farmers in India. Launched as an on-premises ecommerce platform selling farm tools in 2008, the firm turned to Google Cloud Platform (GCP) to expand its offering. It now uses cloud-based analytics and is deploying ML models to provide timely advice in five languages on everything from seed optimization, crop rotation, and soil nutrition to pest control.
A 2018 survey underscored the demand for agricultural planning for Indian farmers. While farming remains a dominant sector in India, employing half of its labor force, 70 percent of small farmers – those cultivating fewer than three acres – said their crops are damaged by unforeseen weather and pests. An even higher number – 74 percent – say they lack access to farming-related information.
Widening that gap is the relative lack of access to new, higher yield seeds and improved soil analyses for small farmers, who must otherwise rely on traditional methods. “It could take a few years for innovative information to trickle down from universities to small, grassroots farmers,” says Pritesh Gudge, AgroStar Software Engineer. “Today, just by clicking through our Android application, farmers learn about new, effective farming practices and receive advice customized to their crop and soil.”
Connecting a million farmers in the cloud
Operating in the Indian states of Gujarat, Maharashtra, Rajasthan, Orissa, Bihar, and Karnataka, AgroStar is closing the knowledge gap with a full-service, cloud-based SaaS solution – the only one of its kind in India. It combines agronomy, data science, and analytics to help farmers by providing a variety of resources.
AgroStar has reached over a million farmers through its Android app, the AgroStar Agri-Doctor. The mobile client is available as a web-based or full-featured native app. Both provide access to the firm’s knowledge base hosted on GCP, a Q&A forum that connects farmers to each other to help understand and better solve problems and to learn about innovative practices and products. Farmers can also click through to follow local and national market trends that help forecast crop prices.
In addition to the self-service knowledge base, AgroStar provides access to agronomy experts who use cloud-based analytics tools and historical data to provide season-and locale-specific advice to each farmer. “We are now tracking thousands of calls in 5 languages each day,” says Pritesh.
The AgroStar app also provides links to purchase and then track the delivery of farm tools and supplies such as cultivators and fertilizers. An in-house platform manages fulfillment centers and a doorstep delivery network simplifies the supply chain while giving farmers what they need, when they need it. By procuring directly from the manufacturers and primary distributors of farm supplies, Agrostar is achieving cost savings, which it passes on to farmers.
Build fast, pivot faster
From the start, the human and environmental variables of farming in India, not to mention the volume of AgroStar’s few hundred thousand monthly active users, made a highly scalable cloud-based solution inevitable. Farmers rely on the firm’s Agri-Doctor app to provide advice in multiple languages on topics that range widely throughout three growing seasons, each with distinct crop nutrition and rotation cycles and farm implementation requirements.
“For farmers, the focus keeps changing every month, and every season,” says Pritesh. “To serve our growing community, we needed a platform that could process images at high volume, fulfill tools and seed orders across thousands of miles, and respond to multilingual queries. We quickly moved away from spreadsheets and server-based solutions – we needed to build fast and pivot faster.”
Ending late-night deployments
The firm’s first cloud experience was with an AWS solution. At the time, AWS was the only cloud provider in India, but AgroStar wanted to find a solution that was easier to use and offered better integration with Android devices. “Deployment and processing costs were very high, and the developer tools and documentation were not as intuitive as we needed,” says Pritesh.
When GCP service arrived in India in October 2017, AgroStar embarked on a platform re-implementation that made possible dramatic changes in the way it developed and deployed its solution. Using Google Kubernetes Engine (GKE) for crop advice management and Compute Engine for its production application services, the firm built the backend for the Agri-Doctor discussion forum in only three weeks. The platform’s microservice architecture is implemented in Python and Golang and deployed on GCP.
AgroStar began to realize significant efficiencies in its build, deploy, and test cycles. “We previously needed to work overnight to deploy to production,” says Pritesh. “Now using Google for Kubernetes containers and a rolling update strategy, we can deploy during the day without any problems or interruptions to service.”
The move to GCP streamlined AgroStar’s stack. “We were running 12 independent instances on AWS,” says Pritesh. “With Google Kubernetes Engine, we are deployed on a single cluster at a cost savings of $1,300 per month and growing.”
Improving customer response times by 85 percent
With a managed deployment capability, AgroStar can devote more time and resources to executing on its platform and Agri-Doctor app development plan. A strategic goal was managing customer response times as the firm grew its base. GCP has helped the firm meet that goal, achieving an 85 percent improvement in customer response times even as traffic grew significantly.
“With our on-premises solution, we could handle around 100 customers daily, which took 30 to 50 minutes for each customer,” says Pritesh. “We now handle thousands of customers daily, taking only 4 to 5 minutes for each one.”
AgroStar used Firebase to implement its Agri-Doctor app. A real-time cloud database, Firebase provides an API that enables the Agri-Doctor advice forum to be synchronized across all its far-flung mobile clients, effectively sharing knowledge base updates with one million users in near real time.
Using cloud tools to manage and monitor
Cloud Pub/Sub, Kafka, and Cloud Dataflow manage data ingestion and queueing of event and transaction data to the analytics layer. BigQuery fetches and persists data to Cloud Storage. Cloud SQL and dashboards powered by Tableau deliver farmer crop and soil profiles within minutes.
Cloud IAM helps AgroStar control access to all its cloud resources. And Stackdriver, the integrated logging aggregation capability for GCP, helps monitor and speed debugging on every tier of the AgroStar solution.
Machine learning to enhance yields
AgroStar is developing a variety of ML components to improve responsiveness and extend its platform offerings.
To speed up the diagnosis of and treatment for crop blight, AgroStar is building a deep learning pipeline using TensorFlow. The pipeline relies on GoogLeNet models that use multi-layered convolutional visual pattern recognition. It will assess uploaded images to support a disease-detection capability on the mobile app. Based on the commercially successful AI algorithms that automated postal code processing, GoogLeNet offers improved performance and computational efficiencies by using a creative layering technique that distinguishes them from older, sequential recognition engines.
To improve its customer search experience, AgroStar is developing an ML pipeline that shrinks fetch times by suggesting tags mapped to stored data. Processed using TPUs, Cloud Natural Language and Video AI, the tags provide a metadata layer that supports queries in any of the ten natural languages that AgroStar farmers can use.
The AgroStar search pipeline consists of Long Short-Term Memory (LSTM) models of Recurrent Neural Networks. Recurrent networks exhibit “memory” through iterative processing and are distinguished from feedforward networks by a feedback loop connected to their past decisions, ingesting their own outputs moment after moment as input.
Implementing a recommendation engine
The firm is also adapting the Random Forests TensorFlow AI model to develop a crop and product recommendation engine. The model is trained by consuming numerical (rainfall, humidity, water availability per acre) and categorical (soil type, water sources) parameters to suggest appropriate products by season, region, and locale.
To simplify the product suggestion experience, AgroStar developers are testing Cloud Dialogflow, the Google Cloud conversational interface, to build a chatbot capability into its mobile app. The bot will track a farmer’s crop schedules and answer simple questions by linking to the recommendation engine.
AgroStar is also extending its analytics platform with AI-powered sales planning and forecasting. Using linear regression models implemented in TensorFlow and powered by Cloud ML Engine, the capability will enhance supply chain logistics as the company scales its operations across India.
To provide a credit on-demand offering for a range of seed-to-harvest cycle products, AgroStar is attempting to use Vision API to create an AI model that will convert uploaded photos of customer application records into standard data formats. The firm’s credit policy features a grace period in which farmers begin paying back loans after harvested crops go to market.
A versatile and friendly development ecosystem
AgroStar credits the convivial tools and documentation that GCP offers and its incremental, pay-as-you-go pricing model for both the firm’s success and its ability to manage growth.
“What Google Cloud offers is extremely good documentation and extremely simple-to-use tools and interfaces across all services,” says Pritesh. “It helped us initially deploy our platform and at every scale that we have required since then, and its cost effectiveness enabled us to staff up to meet new feature milestones.”
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