Founded in 1930 and headquartered in Jordan, Arab Bank is one of the oldest banks in the Middle East. Operating out of 28 countries, we’ve earned our customers’ trust with a prudent approach to operations and respect for the cultures and customs in the region. 

With a few exceptions where cloud providers have hosted their datacenter in a Middle Eastern or North African country, the banking sector, in general, in the region has been slow to adopt cloud technology for a number of reasons, including concern about data security, maturity and security controls of cloud services (PaaS and SaaS), and regulations in place. But on the other hand, we saw the opportunity to accelerate our development and testing using the cloud, as well as to partner with the fintech community and digital service providers to integrate their solution in the banking ecosystem. We needed more flexibility to connect with the outside world, and a more open architecture to help us drive our internal innovation at a faster rate with the help of the fintech industry. By collaborating with Google Cloud, we reached those goals and accelerated app development and testing through products like Apigee and Anthos. We’re now offering innovative apps and services to our customers and employees that leverage new technological capabilities to give more agility and flexibility, and to optimize our workloads.

Embracing the cloud in a regulated industry 

To get started with the cloud, we needed to create internal awareness about cloud technology, the API layer, containers and their benefits amongst our leaders and staff. Google helped us educate and get buy-in from key functions by organizing open technology demonstration sessions and discussion panels. When considering potential cloud providers we had four decision criteria: maturity of security controls, ease of use, cost, and scalability / agility for new deployments and continuous innovation. This last factor was critical, and we were impressed by Google Cloud’s innovation roadmap, both via direct conversations and at Google’s Next conference, where we met a lot of people passionate about technology, innovation and building something new.  

Going back to our journey, given the above-mentioned regional limitations, we started to develop a hybrid cloud approach. This helped us continue to operate on-premises for a number of services in production, particularly those that have personally identifiable information (PII) or other sensitive data attached, and to leverage the cloud for development, testing and production workloads that don’t contain customer data. 

In the short term, we didn’t anticipate that our many jurisdictions would allow data to be transported to other countries. But cloud tools will allow us to tokenize or anonymize customer data while maintaining customer data on-premises. This applies to many digital journeys such as customer onboarding, credit facility online applications, or marketplace navigation. In the coming years, we predict our API integration with partners will accelerate and enrich the overall digital value proposition of our business segments, namely consumer banking, small and medium-size businesses and large corporate and institutional clients. 

Building connections and cornerstones in the cloud

The first move in our digital transformation was to implement Apigee, Google Cloud’s API management platform, to connect to the world’s digital banking ecosystem. Apigee provides the security, sharing, mediation policies, and developer portal capabilities for us to successfully meet Open Banking standards while focusing on innovation.  

On the back of the Apigee implementation, we created an accelerator program to incubate Fintech ideas that can, in turn, integrate into our digital platforms and be offered to our customers. We also developed various banking APIs, all designed and documented in accordance with PSD2 and Open Banking regulations, and made them available to our partners. These APIs exposed on our API development portal offer the needed code structure for fintech companies to design creative solutions around them.

Next, we adopted Anthos, Google Cloud’s managed application platform. Anthos has become a cornerstone of our operations because it works across hybrid cloud, offering integration of microservice containers and fueling collaborative opportunities with external parties. Our current Anthos infrastructure includes several hundreds of  microservices now running on containers in Google Kubernetes Engine (GKE) and on-premises. We now use the cloud for collaboration, development and testing, but not for production, which is done on-premises. 

Along the way, Google Cloud’s Professional Services Organization (PSO) helped us through the entire cloud setup process, and with the adoption of Anthos. We originally built on the cloud tools through an iterative process, learning from our successes and errors along the way. Now that we have a better sense of how Anthos operates, we’re building a fresh infrastructure atop a sound, stable, and resilient foundation that will let us scale easily as we work to transform Arab Bank into a digital-first enterprise, that is our ambition. 

Currently products running on Anthos include customer acquisition and onboarding via mobile apps, and our Arabi-Pay app, which allows customers to instantly pay each other via WhatsApp or other messaging platforms. Leveraging Anthos, our instant loan service for Arab Bank salaried employees can grant and disburse loans up to $7,000 in less than seven minutes.

In addition, we’ve built a number of digital journeys for our Small and Medium Enterprise (SME) customers, such as our SME client digital onboarding process and paperless SME lending platform.

While some may think that digital adoption in this part of the world can be slow, as customer contact remains anchored in our customs, the recent COVID-19 pandemic has accelerated the adoption of digital banking services and electronic payments, inspiring more confidence to buy and pay online. Thanks to our rich and user-friendly banking app that relies on Apigee and Anthos for critical customer journeys, over 90% of new-to-bank customers are using our mobile apps. Within the next 18 months, we predict that number will be closer to 100%. 

Of course, with higher customer adoption comes the challenge of potential service interruptions. A single moment of downtime can be highly visible to many digital customers. But Google Cloud’s Anthos and Apigee give us the flexibility to resume processes at a fast rate, so any interruptions are almost invisible to our customers. In fact, when the COVID-19 pandemic hit, though our branches could be open only for limited hours each day, our consumer clients in particular were able to take advantage of our digital services in a very self-sufficient manner. Being well positioned with Google Cloud, we could also keep our internal teams and external partners connected and productive. Without Google Cloud, continuing the digital transformation of the bank at the pace we wanted would have been a big challenge. 

Collaborating across borders and time zones

With Google Cloud, our ability to collaborate and partner has transformed significantly. We operate 24/7 now because our developers are scattered across multiple geographies and different time zones. Because testing and deployment can run around the clock, including on weekends, we currently deploy a new digital journey in a few weeks, faster than ever before. This has given our organization a spirited mindset that prioritizes innovation, and raised the bar in terms of our operating model.  

Another consideration about Google Cloud tools is the elimination of inefficient processes typically seen in a software development lifecycle. We now build in a completely agile manner, from design squads until deployment in production. Compared to where we were two years ago, when we had an annual maximum of two systems releases in production, we now have close to monthly releases of our digital packages. In addition, we also  supplement those monthly releases with additional ad-hoc releases and fixes in between. As a result, we have removed internal silos and improved tremendously the collaboration between the product and sales teams, operations, IT Dev Factory and Infrastructure, as well as our supporting functions.

Through the agile process facilitated by APIs, we introduced Design Thinking workshops involving external customers and prospects early on to understand their true pain points better and emotions during the existing journeys and how to make new digital journeys frictionless. As a result, the relevance of our products for various customer personas has improved tremendously. 

Transcending banking 

With Google Cloud, we can offer our customers so much more than just banking. We’ve become more digitally relevant to their lives. For example, we recently launched a mortgage app that helps customers all the way from home selection through mortgage negotiations and closing, and even to getting the home decorated. It’s an end-to-end journey in which API integration with key regional players was a cornerstone to our success. 

For other digital products, we have an extensive roadmap of lifestyle-based solutions relevant to each segment and age group. We’ve only scratched the surface of the services we can provide, and we see the cloud as the future for everything we want to do.

Read more about Google Cloud’s Open Banking solution to learn how you can simplify and accelerate the process of delivering open banking as required by PSD2. You can also view our video on Open Banking, powered by Apigee API Management.

We are excited to announce that Google has been named a Leader in The Forrester Wave™: Streaming Analytics, Q2 2021 report. Thank you to our strong community of customers and partners for working with us to deliver a customer focused product. We believe Forrester’s recognition is an acknowledgement of our leadership across an integrated set of capabilities that rely on data to drive transformation. We were also honored to be named a leader in The Forrester Wave™: Cloud Data Warehouse, Q1 2021. 

Forrester gave Dataflow a score of 5 out of 5 across 12 different criteria and according to the report: “Google Cloud Dataflow has strengths in data sequencing, advanced analytics, performance, and high-availability. Google Dataflow’s sweet spot is for enterprises that have a preponderance of real-time data generated on Google Cloud Platform or wish to simplify all data processing by using a single platform that unifies both streaming and batch jobs.” 

Harnessing the power of real-time data

The speed with which businesses are able to respond to change is the difference between those that successfully navigate the future and those that get left behind. In order to accelerate their digital transformation, reimagine their business and leverage the power of real-time data,  today’s data leaders require a streaming analytics platform that provides both depth and breadth.  

Cloud Pub/Sub and Cloud Dataflow, based on more than a decade of experience in internet scale systems for Google’s own needs, provide customers with a reliable, scalable, performant platform. In addition, we’ve designed these products for ease of use to make streaming analytics accessible to more users, which is why customers such as Sky and others from across all industries use Dataflow to run streaming analytics workloads.

5 out of 5 across key streaming analytics criteria

While Forrester gave Dataflow a score of 5 out of 5 in 12 criteria, the product achieved the highest possible scores in areas that are top of mind for our customers.

We continue to be focused on solving problems that matter to you. For example, just in the last month we announced Dataflow Prime and  Auto Sharding for BigQuery – two new auto tuning capabilities that bring efficiency and simplicity to your streaming pipelines. 

Dataflow achieves highest score possible in strategy 

With Google, organizations gain an industry leading product and a partner that has the vision and strategy to help you tackle new business challenges and provide delightful experiences to your customers.

In summary, we are honored to be a Leader in The Forrester Wave™, Streaming Analytics, and look forward to continuing to innovate and partner with you on your digital transformation journey. 

Download the full report: The Forrester Wave™: Streaming Analytics, Q2 2021 and check out these smart analytics reference patterns. To learn more about Dataflow, visit our website and get to know the product by taking an interactive tutorial. You can also watch recordings from the Data Cloud Summit event (May 2021), where we provided an in-depth view of new product innovations in Dataflow and other data analytics products.

Organizations are currently modernizing their businesses in order to meet the increasing complexity of today’s business landscape. In effect, business leaders must evaluate the best way to mitigate the challenges which plague their cloud operations, all while meeting customers’ growing expectations around digital experience (DX) through agility, automation, and proactive incident avoidance. 

In this commissioned study, “Modernize With AIOps To Maximize Your Impact”, Forrester Consulting surveyed organizations worldwide to better understand how they’re approaching artificial intelligence for IT operations (AIOps) in their cloud environments, and what kind of benefits they’re seeing. 

Within this July 2021 study, you’ll see that AIOps systems and principles are here to help. It covers how AIOps increases efficiency and productivity across day-to-day operations, and how businesses are taking note. In fact, 91% of respondents have implemented AIOps to address at least one cloud operations issue, and expansion is set to skyrocket. Those that wait to act, risk losing out on the efficacy of their cloud investment and falling behind their more efficient competitors.

As you can see in the image above, there is a plethora of great information in this complimentary study. So, if you’re looking to enhance your cloud operations and/or adopt AIOps within your organization, be sure to download this free study today.

Whether with the cloud or within their own data centers, enterprises have undergone a period of remarkable consolidation and centralization of their compute resources. But with the rise of ever more powerful mobile devices, and increasingly capable cellular networks, application architects are starting to think beyond the confines of the data center, and looking out to the edge. 

What exactly do we mean by edge? Think of the edge as distributed compute happening on a wide variety of non-traditional devices — mobile phones of course, but also equipment sensors in factories, industrial equipment, or even temperature and reaction monitoring in a remote lab. Edge devices are also connected devices, and can communicate back to the mothership over wireless or cellular networks. 

Equipped with increasingly powerful processors, these edge devices are being called upon to perform tasks that have thus far been outside the scope of traditional IT. For enterprises, this could mean pre-processing incoming telemetry in a vehicle, collecting video in kiosks at a mall, gathering quality control data with cameras in a warehouse, or delivering interactive media to retail stores. Enterprises are also relying on edge to ingest data from outposts or devices that have even more intermittent connectivity, e.g., oil rigs or farm equipment, filtering that data to improve quality, reducing it to right-size information load, and processing it in the cloud. New data and models are then pushed back to the edge; in addition, we can also push configuration, software, and media updates and decentralize processing workload.

Edge isn’t all about enabling new use cases – it’s also about right-sizing environments and improving resource utilization. For example, adopting an edge model can also relieve load on existing data centers. 

But while edge computing is full of promise for enterprises, there are many pieces that are still works in progress. Further, developing edge workloads is very different from developing traditional applications, which enjoy the benefits of persistent data connections and run on well-resourced hardware platforms. As such, cloud architects are still in the early days of figuring out how to use and implement edge for their organizations. 

Fortunately, there are tools you can use to help ease the transition to edge computing — and that likely fit into your organization’s existing computing systems. Kubernetes, of course, but also higher level management tools like Anthos, which provides a consistent control plane across cloud, private data center and edge locations. Other parts of the Anthos family – Anthos Config Management and Anthos Service Mesh — go one step further and provide consistent, centralized management to your edge and cloud deployments. And there’s more to come.

For the remainder of this blog post, we’ll dive deeper into the past and current state of edge computing, and the benefits that architects and developers can expect to see from edge computing. In a next post, we’ll take a deeper look at some of the challenges that designing for edge introduces, and some of the advantages the average enterprise has in adopting the edge model. Finally, we’ll look at the Google Cloud tools that are available today to help you build out your edge environment, and look at some early customer examples that highlight what’s possible today — and that will spark your imagination for what to do tomorrow. 

The evolution of edge computing 

The edge is not a new concept. In fact, it’s been around for the last two decades, spanning many use cases that are prevalent today. One of the first applications for edge was to use content delivery networks (CDN) to cache and serve daily static website pages near clients, for example, web servers in California data centers serving financial data to European customers. 

As connectivity has improved and software evolved, the edge has evolved too, and the focus has shifted towards using edge to distribute services. First, simple services expanded from static HTML to javascript libraries or image repositories. Common functions like image transformation, credit and address validation support services followed. Soon, organizations were deploying more complex cloudlet and clustered microservices installations, as well as distributed and replicated datasets. The term “endpoint” became ubiquitous, and APIs profilerated. 

In parallel, there’s been an explosion of creativity in hardware, microcontrollers and dedicated edge devices. Fit-for-purpose products were deployed globally. Services like Google Cloud IoT Core extended our ability to manage and securely connect these dispersed devices, allowing platform managers to register tools and leverage managed services like Pub/Sub and Dataflow for data ingestion. And with Kubernetes, large remote clusters — mini private clouds in and of themselves — operate as self-healing, autoscaling services across the broader internet, opening the door to new models for applications and architectural patterns. In short, both distributed asynchronous systems and economies have blossomed.

What does this mean for enterprises? For the purposes of this series, edge means you can now go beyond the corporate network, beyond cloud VPCs, and beyond hybrid. The modern edge is not sitting at a major remote data center, nor is it a CDN, cloud provider, or in a corporate data center rack — it’s just as likely to look like 100 of these attached to a thousand sensors.

Edge, in short, is about having hardware and devices installed at remote locations that can process and communicate back the information they collect and generate. The edge management challenge, meanwhile, is being able to push configuration and software/model/media updates to these remote locations when they are connected.

Enable new use cases

Today, we have reached a new threshold for edge computing — one where micro-data-processing centers are deployed as the edge of a fractal arm, as it were. Together, they form a broad, geographically distributed, always-on framework for streaming, collecting, processing and serving asynchronous data. This big, loosely coupled application system lives, breathes and grows. Always changing, always learning from the data it collects — and always pushing out updated models when the tendrils are connected. 

Right now, the rise of 5G is pushing the limits of edge even further. Devices enabled with 5G can transmit using a mobile network — no ISP required — enabling connectivity anywhere within reach of a cell tower. Granted, these networks have lower bandwidth, but they are often more than adequate for certain types of data, for example fire sensors in forests bordering remote towns that emit temperature or carbon monoxide data periodically. Recently, Google Cloud partnered with AT&T to enhance business use of 5G edge technology but there is so much more that can be done. 

Reduce data center investments

In addition to enabling the digitization of a broad range of new use cases, adopting edge can also benefit your existing data center.

Let’s face it: data centers are expensive to maintain. Moving some data center load to edge locations can reduce your data center infrastructure investment, as well as compute time spent there. Edge services tend to have much lower service level objectives (SLOs) than data center services, driving lower levels of hardware investment. Edge installations also tend to tolerate disconnectedness, and thus function perfectly well with lower SLOs — and lower costs. 

Let’s look at an example of where edge can really reduce costs: big data. Back in the day, we used to build monolithic serial processors — state machines — that had to keep track of where they were in processing in case of failure. But time and again, we’ve seen that smaller, more distributed processing can break down big, expensive problems into smaller, more cost-effective chunks. 

Starting with the explosion of MapReduce almost 20 years ago, big-data workloads were parallelized across clusters on a network, and state management was simplified with intermediate output to share, wait for, or restart processing from checkpoints. Those monolithic systems were replaced by cheaper, smarter, networked clusters and data repositories where parallel work could be executed and rendered into workable datasets. 

Flash forward to today, and we are seeing those same concepts applied and distributed to edge data-collection points. In this evolution of big data processing, we are scaling up and out to the point where observation data is so massive that it must first be prefiltered, and then preprocessed down to a manageable size and still be actionable. Only then should it be written back to the main data repositories for more resource-intensive processing and model building.

In short, data collection, cleanup, and potentially initial aggregation happens at the edge location, which reduces the amount of junk data sitting in costly data stores. This increases performance of the core data warehouse, and reduces the size and cost of network transfers and storage! 

The edge is a huge opportunity for today’s enterprises. But designing environments that can make effective use of the edge isn’t without its challenges. Stick around for part two of this series, where we look at some of the architectural challenges typically encountered while designing for the edge and how we begin to address them.