When our founders began Mambu in 2011, their goal was to bring the latest digital technologies to the banking and finance world. Banking, in particular, is an industry built on decades of deep legacy technology. So initially, Mambu was embraced by microfinance — 100 organizations in 26 countries in just the first two years. 

Since then, acceptance of modernizing core banking services by using composable, cloud technologies has grown across financial services institutions (FSIs). We now service top-tier banks, fintech startups, and other finance organizations across six continents, helping them deliver flexible, personalized, customer-centric banking products and services that their customers can depend upon.

One of the main reasons we’ve been able to scale the Mambu composable banking platform across the globe and at our current pace is our partnership with Google Cloud. The decision to move forward with Google Cloud happened for several reasons.

1. Flexibility and openness. Many FSIs are on hybrid and multicloud technology stacks, as they may still be transitioning from legacy systems, or have data residency requirements that have led them to use different clouds in different regions. Mambu meets customers wherever they are in their cloud journeys. We support interoperability without vendor lock-in. This need for openness, as well as the scalability benefits, led Mambu to evolve our platform on Google Kubernetes Engine (GKE). Many customers use open-source Kubernetes because, this common foundation can help streamline integration, speed up time to market, and reduce development. Just as important, the Google Cloud open cloud approach matches our company values. 

2. Security and data residency. For customers in highly regulated finance industries, security isn’t just top of mind,  it’s the No. 1 requirement. In addition to Google Cloud’s secure infrastructure, external audit certifications, and encryption, its wide array of regions has allowed us to expand into more countries, where we serve banks that must meet local data residency requirements. For example, Google Cloud’s Jakarta Cloud Region, has allowed us to support Bank Jago in Indonesia as it brings more financial inclusion to the unbanked in that country.

3. Availability. It’s critical for banks to maintain basic financial functionality, like accepting deposits and serving cash, even amid a service disruption. We needed a cloud partner with impeccable redundancy, failover, and disaster recovery capabilities.

In addition to GKE, the Mambu platform uses several other Google Cloud services for specific functions, including Cloud Armor, Cloud Load Balancing, Cloud VPN, Cloud Memorystore, and Google Cloud Operations.

Another important reason we chose to partner with Google Cloud was its expansive ecosystem and commitment to innovation. We are midway into a three-year journey to modernize our own technology stack to meet customer needs.

Building a roadmap with Google Cloud

Our customers need a core banking technology platform that will grow with them as they bring to market innovative services built on the latest technology advances. For Mambu to be that platform, we need a cloud partner that supports and scales with our growth. While we originally built our cloud architecture on GKE and Compute Engine (among a few other Google Cloud services), we’re now looking to a serverless future where we can scale more easily and leverage managed services within Google Cloud and its partner ecosystem to focus on our core offerings. 

These are just some of the modernization and customer-led innovations that we’re cooking up: 

• More workloads in GKE: Like many companies, our cloud transformation is a work in progress. While much of our codebase is in GKE, we’re continuing to break up some larger pieces of code into microservices to increase agility and velocity, enabling us to make consistent updates to discrete areas of our platform without affecting the whole. GKE is the leader for orchestrating microservices at scale and continues to be a natural fit.
• Native BigQuery integration: Mambu customers collect a tremendous amount of data within the platform that can be used for analytics, personalization, and other use cases. We’re planning to create a seamless integration for feeding core banking data from Mambu into BigQuery so that customers can better leverage their valuable data.
• CloudSQL vs. self-managed MySQL: We have almost completed migrating from our own MySQL instances to managed Cloud SQL databases, which will open up new opportunities to implement customer-centric solutions such as BigQuery integration.
• A serverless future with Cloud Run: Compute Engine is working well for Mambu, providing the flexibility to choose the virtual machines that best balance performance and cost needs. As we seek even more time and cost efficiencies, we believe the elastic scalability of a serverless architecture built on Cloud Run will get us there, and we’re considering going serverless in the future. Doing so would abstract infrastructure for simpler management, while allowing us to fire up containers to meet our customers’ high transactions-per-second needs, spin them down when not needed, and pay only when they run. It would also boost security: Without long-running compute, there are no patches or fixes, and each new instance is isolated and fresh by default.

These are just a handful of examples of the ways we want to best leverage Google Cloud services to simplify how we manage our tech stack, as well as continue to bolster security, scalability, and performance. There are many other ideas we’re exploring: using Dataproc and Datastream to support the specific data needs of Islamic banking, Cloud Functions so that customers can run their own queries against Mambu, and AI-enabled features. 

At Mambu, our mission is to empower our customers to deliver great modern financial experiences easily to everyone around the world. Every time Google Cloud opens a new data center, we can enter a new market. Every time we move to a new-to-us managed service via the Google Cloud Marketplace, we free up time to build new ways to deliver customer-centric banking solutions. And so, we look forward to continuing this partnership with Google Cloud well into the future.

In order to be successful, research needs to be replicable, so scientists can build on past work and insights. However, an article in Nature warned that as much as 50% of published drug development research could not be reproduced in subsequent trials. As a result, promising drug candidates sometimes led to disappointment, as well as wasted time and money, when key findings could not be replicated. 

The shift to cloud computing helps solve this problem because it allows researchers to use open-source tools that work across platforms. As demand for cloud computing rises, our customers have asked us for more ready-made solutions to assure reproducibility of results by their collaborators, regardless of the platform they are using. They asked for secure and effective collaboration tools as well as faster time-to-insight from any type of data.

We listened. Google’s new research and data insights solution includes three sets of functionalities to address these key challenges. Each can be activated on demand and may be eligible for subscription pricing. “HPC in a box” offers abstract complexity to run high performance computing (HPC) workloads by automatically managing your cluster in the most effective manner. It integrates seamlessly with some of the industry’s most-used schedulers like Slurm and PBS. It makes it easier than ever to answer bigger questions faster by accessing Google’s fast, powerful hardware like TPUs and GPUs, all for one predictable flat fee for eligible workloads. Healthcare Innovation Hub provides healthcare-specific functionality to help ingest, aggregate, and de-identify any type of healthcare data in its original format. It unlocks cross-modality analysis and collaboration and empowers researchers with harmonization tools to overcome healthcare interoperability issues. Google Cloud Real-World Insights (formerly FDA MyStudies) accelerates and streamlines drug development and clinical trials to address urgent medical challenges with reproducible results.

The solution enables researchers to ask new questions, get answers more quickly, and work more collaboratively–with no wait times or down times. Institutions can scale to more ambitious projects and generate actionable, real-time insights from any data source–all while staying within budget.

Many top research centers have already found it faster and more cost effective to shift from downloading and storing data on their own servers to storing and analyzing data on Google Cloud. Here are some of the real-world projects already yielding breakthroughs:

• Clemson analyzed 8,500 hours of traffic camera feeds with 2.1M vCPUs in three hours to improve evacuation routes for disaster planning.
• The Colorado Center for Personalized Medicine saved $2.9M by building their Compass data warehouse on Google Cloud rather than on on-prem.
• The Broad Institute slashed costs of genomic sequencing by 90% with Google Cloud.

Our partners, such as Atos, Burwood, Omnibond, Mavenwave, Quantiphi, and Deloitte, can help you first design and develop, then install and implement your own solution, including training. To assess your institution’s needs and develop a customized plan for your next-generation research solution with research and insights, contact our sales team.

As community and business concern over global warming, sustainability, and energy security continues to rise, companies such as Bharat Light & Power (BLP) are working on answers.

Founded in 2010 and located in Bangalore and Delhi, BLP is one of the leading clean energy generation companies in India. According to Tejpreet Singh Chopra, Chief Executive Officer of BLP, the business started with the aim of delivering clean energy from renewable sources such as wind, solar, biomass, and hydro to the 300 million residents of India that did not have reliable access to power at that time.

However, BLP soon began experiencing problems ensuring availability of the wind turbines that powered utility-scale wind power generation across the country.

“While we were investing large amounts building wind farms, the failure of the smallest component could shut down an entire wind turbine and compromise our efficient operation,” says Chopra. “A $5,000 battery could fail and shut down a $2.5 million piece of equipment!”

BLP transformed its legacy thinking about man, machine, technology, and process, and implemented an AI and Internet of Things (IoT) project. The business used hundreds of tags from its wind turbines to capture and send machine behavior and performance data to a control center. This data triggered an 18-month project to create AI and machine learning algorithms that would enable engineers to predict component failures in wind turbines, improve generation, and adjust maintenance and replacement schedules accordingly.

This activity provided the foundation of a technology business—one of the three companies that comprise BLP—that delivers enterprise AI for industrial uses over a platform, branded Orion.

“We provide an end-to-end solution that enables businesses in industries such as transportation, logistics, ports, infrastructure, oil and gas, supply chain manufacturing, steel, and automotive to improve productivity,” says Chopra.

As the business expanded, it began to extend beyond its core “AI for industry” mission. It recruited an IoT team to help factory owners and operators enable the programmable logic controllers (PLC) and supervisory control and data acquisition (SCADA) systems that run disparate equipment and machines—such as production line machines of different ages and countries of origin—to talk to each other and provide usable data and insights.

Agility and adaptability key

BLP’s management team realized early that the business needed to be agile and adaptable to keep pace with changes in technology.

“We knew we would have to effectively destroy and remake the business every 18–24 months to remain relevant, so we needed a service that could support our dynamic infrastructure, data, and AI needs,” says Chopra.

The business started operations on a cloud service but quickly ran into problems. “We found in the world of industry—the vast amounts of data, the variety of sources of data, and the complexity of insights required—created a very different set of challenges relative to the consumer technology environment,” says Chopra. “Our cloud provider could not provide an architecture that made sense for an industry-focused solution.”

Google Cloud to power AI and visual analytics

Within two years, BLP advanced its strategy and focused on using open source to reduce the cost of its architecture. As Orion matured and its take-up grew, the business began looking at multinational cloud services to run the forthcoming version 3.0 of the platform.

BLP found Google Cloud provided the best fit for its needs for a range of reasons, including the ease of use of Google Cloud services—likened by the BLP technology team as “the equivalent of a consumer app experience”—and its high quality database services, high availability, fast response times, and the power to run the platform’s AI and visual analytics.

BLP established as its key objective a 20% reduction in costs over its previous cloud service and availability levels exceeding 99.9%. The business completed the deployment in June 2019 following discussions and input from Google Cloud’s engineering and architecture experts prior to and during the first stage of the project.

Cloud IoT Core—a managed service that allows organizations to connect, manage, and ingest data from dispersed devices—is the cornerstone of the architecture supporting the latest latest version of the Orion platform, Orion 4.0. BLP has also created a data pipeline based on Cloud Pub/Sub event ingestion and delivery and Cloud Dataflow data processing.

Cloud Bigtable provides a high-performance NoSQL database service for the platform’s analytical workloads and BigQuery delivers a powerful analytics data warehouse. Cloud Functions enables the business’s developers to build event-driven serverless applications.

This architecture currently captures, processes, analyzes, and reports on 8 million data points in 578 turbines around the world per day and processes data from 200,000 data points in 4,000 sensors per day.

The Google Cloud architecture supports the data visualization and reporting and the AI and machine learning-powered products created by BLP. These reports and products enable users to monitor remote assets and use AI-based analytics to predict machine failures before they occur, sequence failure events, and maximize equipment uptime.

A range of benefits to customers

With Google Cloud, BLP is delivering projects with a range of benefits to customers, including increasing manufacturing productivity by at least 5% and reducing costs by about 10%.

“We’ve enabled one of the largest electrical companies in India to compare production line performance by capturing data from PLC and SCADA systems and extracting it to Google Cloud for processing, analysis, and reporting,” says Chopra. “For another customer, we’ve deployed an IoT system that has saved them about $200,000 in energy costs over six months.”

The business is also providing monitoring, reporting, and analysis to predict likely failures of gearboxes, bearings, generators, and blades in 2,000 wind turbines—that provide close over 2 GW of wind power—in countries such as France, Germany, Italy, India, Portugal, Spain, the United Kingdom, and the United States.

Furthermore, BLP is providing visual analytics to help one of the largest ports in the world detect when workers are not wearing helmets or safety equipment.

“We also do a lot of inventory track-and-trace work to help companies improve supply chains and help factories keep track of tooling through Bluetooth low-energy technologies,” says Chopra.

With Google Cloud, BLP is now ideally placed to execute its business strategy of helping customers improve productivity and increase growth, control and reduce costs, and enhance quality and safety.

“We are realizing this strategy by working to become the best company in the world at using AI to predict machine failure,” says Chopra. “Our underlying technology strategy entails enabling the most advanced IoT hardware used by industry to talk to software and the cloud, using the most powerful AI cloud around—which is why we chose to marry our AI algorithms with Google Cloud—and delivering insights through visualization.”

Testing Edge TPU

The business is now testing Edge TPU to run AI at the edge in high-performance, small-footprint, and low-power ASIC environments.

“We think that will be the next big revolution as TPU costs come down,” says Chopra. “It will be a considerable benefit performing AI at the edge rather than involving the full infrastructure of the cloud.”

BLP is keen to build on its existing relationship with and use of Google Cloud to further transform manufacturing worldwide.

“Manufacturers are pushing the boundaries of quality and cost through initiatives such as Lean Six Sigma, Zero Defects, poka-yoke, just in time, and other methodologies and approaches,” says Chopra.

“The next wave of manufacturing improvements are coming through industry 4.0, AI, IoT, and Google Cloud’s analytics, databases and other services. These are ideal to power this change in the industrial world.”