Over the last decade, financial service organizations have been adopting a cloud-first mindset. According to InformationWeek, lower costs and enhanced scalability were the biggest drivers for cloud adoption in financial services, and cloud-native applications allow access to the latest technology and talent, enabling adopters to rebuild transaction processing systems capable of supporting very high volumes and low latency.

Both Neo4j and Google Cloud have been using relationship-based data representations since the beginning, and we’re dedicated to using this technology to help financial services customers drive business transformation. We are excited about the prospects of financial services (FinServ) cloud systems and believe that graph data in the cloud can help solve significant challenges in the industry.

Data Challenge #1: Risk Management and Compliance

First among the top concerns for any CIO moving to the cloud is risk management and compliance. Disconnected, uncontextualized, or stale data create opportunities for fraud and financial crimes to occur. The fact is when it comes to FinServ, the question is not “if” but rather how often an attack will occur.  Unfortunately, incidents have been trending upward over the last decade, and COVID has only exacerbated this reality. Financial crimes affect the bottom line both in the remediation of these crimes and in intangibles like brand value.  

Add to this the complexity of international banking, which makes “compliance” a moving target. Penalties due to noncompliance are a constant concern to any FinServ organization.

The tabular representation of information with a fixed number of columns that never change prevents a description of an ever changing world with changing characteristics. Relational databases are great if the world you describe does not move fast but have limitations when data structures are highly interlinked and not homogeneous.

Neo4j Aura on Google Cloud provides a foundation for creating dynamic, futureproof, scalable applications that adhere to the security standards and protocols today’s financial services organizations require to meet the challenges of finding and preventing bad actors. This also includes enterprise scalability; reaching over 1 Billion nodes and relationships to streamline queries and provide solutions that meet regulatory and privacy compliance across geographies. Neo4j has helped some organizations save billions of USD in fraud in the first year of deployment alone.  

What makes graph technology the best choice for fraud detection use cases is that the relationships between the data-points are as important as the data-points themselves. Let’s take as an example, one John Smith approaches a multi-national banking institution to manage the primary account for his new holding corporation.  

While no one has any record of John R Smith Holdings LLC, the bank’s application built on graph technology understands that there are several well-known entities owned by John Smith Holdings. The application also identifies several well-known board members who bank with this institution. Due to this relationship-driven approach, the bank now understands John R Smith is not “John Smith,” who previously attempted to open an account for his holding corporation, which had no information associated with it prior to two months ago.

Data Challenge #2 Manual Processes and Inefficiencies 

The ubiquity of the cloud offers an opportunity to deploy automation at unprecedented levels to tackle the errors and inefficiencies that manual processing allows to creep into processes. When data comes from disparate, perhaps legacy systems – which may have become siloed and “untouchable” over the years – further complexity arises. As an example, if someone in sales types “John Smith” into a CRM system not knowing that John R Smith is the spelling in the customer data master, it may result in two separate and potentially conflicting records. Being able to join those records together in a mastered view helps to solve this problem. In addition, low data quality equates to an increase in risk, costs, and implementation times for new systems. 

Neo4j Aura on Google Cloud provides automation and artificial intelligence (AI) that reduces manual processes and the errors that accompany them. In this graph architecture each node, which can represent a person, will have labels, relationships, and properties associated with it. This allows for the use of AI which can easily understand that John Smith in the CRM is the same John R Smith in the customer master. The information contained in Neo4j can be connected bi-directionally to ensure consistency across applications and data sources. 

One of the benefits of this approach is that linking information allows organizations to keep the full value of the data, rather than forcing the data into predetermined tabular representations, with the risk of losing valuable information and insights.

Data Challenge #3: Customer Engagement and Insight

Another significant concern is the high expectations today’s customers have for every interaction. End users are accustomed to predictable experiences on their digital devices, and FinServ apps are no exception. Added to this, the “Covid economy” has driven digital adoption significantly across demographics; even among customers who might traditionally have used in-person services. This also equates to increased expectations for personalized, predictable experiences with every digital interaction. We know that latency has always been a key consideration for financial trading, but a recent ComputerWeekly study showed that every financial organization should ensure their visible latency is at 10 milliseconds or less. Customers no longer accept their broadband is at fault.

Finally, blind spots in the customer journey often result in dissatisfaction, which ultimately leads to increased churn. Without gaining actionable insights from your customers, there is no room to innovate and iterate on what they are looking for in your products and services. And this translates to losing market share and competitive advantage.

The NoSQL architecture, specifically the dynamic schema and structure of Neo4j Aura gives you the ability to take charge of your data and make changes according to your development cycles or newer data models. This equates to faster builds, more comprehensive releases and a wider, richer data-set that can be contextualized and understood instantly. Graph technology is the logical choice for building a Customer 360 application. Under this approach organizations not only get valuable insight into the individual client’s behavior and patterns, but also those of their family, friends and colleagues. This allows for stronger personalization, targeted campaigns and successful execution, resulting in increased customer satisfaction and retention levels.

Graph Technology on Google Cloud

Neo4j is a recognized leader in graph database technology and the only fully integrated graph solution on Google Cloud, helping to fill a common need for Google Cloud customers. Both Neo4j and Google Cloud are invested in continuing to grow our partnership and mutual product direction.  

You can find and deploy the Neo4j graph database straight from the Google Cloud marketplace, whether you want to download the software for an on-premises deployment, use the virtual machine image, or use the hosted solution, Aura on Google Cloud, the graph database-as-a-service. In any deployment, you get the same enterprise-grade scalability, reliability, and connectivity along with successful, repeatable use cases you can rely on to resolve your particular challenges and integrated billing. 

For a real-world example of how graph technology can optimize financial services, you can read our Case Study with fintech Current. Current, a leading U.S. financial technology platform with over three million members, used Neo4j Aura on Google Cloud to create a personalization engine based on client relationships. 

To learn more about Neo4j Aura on Google Cloud for FinServ organizations, register for our webinar on Thursday, December 16 with Jim Webber, Chief Scientist, CTO Field Ops at Neo4j and Antoine Larmanjat, Technical Director, Office of the CTO, Google Cloud. 

Click here to Register

For a business to succeed in the long term, it needs to learn not just to adapt to inevitable change, but to harness it. South Africa-based PPS has been an insurance company since 1941 and today is the biggest mutual insurance provider in the country.

As a mutual company, PPS is owned by more than 200,000 members, making them shareholders. In recent years, PPS and other companies like it have been affected by a number of external factors.

“For one thing, technology platforms have brought in a new gig economy that has all kinds of implications for insurance,” says Avsharn Bachoo, CTO at PPS. “What we’ve been seeing is basically a disruption of the South African insurance industry. We chose to see that as an opportunity.”

“Our servers were at the end of their life cycle and we had to decide whether to refresh them or switch completely. To embrace the world of AI and machine learning effectively, we knew we needed a cloud-based infrastructure. We’ve found the answer in Google Cloud Platform.”

—Avsharn Bachoo, CTO, PPS

In early 2018, faced with an uncertain economic environment that was squeezing growth and profitability, PPS decided to transform itself from a traditional broker-based business into a digital insurance provider. A key pillar of this new strategy was to overhaul the company’s technology infrastructure. To turn the strategy into reality, Avsharn and his team chose Google Cloud Platform (GCP).

“Our servers were at the end of their life cycle and we had to decide whether to refresh them or switch completely,” says Avsharn. “To embrace the world of AI and machine learning (ML) effectively, we knew we needed a cloud-based infrastructure. We’ve found the answer in Google Cloud Platform.”

Power, speed, flexibility with Google Cloud Platform

Previously, PPS maintained an on-premises IT infrastructure, which worked for its traditional business but was unsuited for its new way of working. In early 2018, the company started working on new products for its members but this required large amounts of compute power that proved prohibitively expensive with on-premises servers. Even existing products were starting to require more than the infrastructure could deliver. Aging equipment meant that it’s testing and quality assurance environments bore little resemblance to the actual production environment.

“We had no pre-production environments at all,” says Avsharn, resulting in more work for developers after products had been released. Meanwhile, the capital required to buy and configure more servers for new projects meant fewer resources available for innovation, and left the company less able to react to changes in the market. PPS knew it had to find a cloud-based alternative.

Shortly after devising a new digital strategy, PPS engineers attended a training session on cloud infrastructure given by leading South African Google Cloud Partner Siatik. Impressed with the presentation, PPS engaged Siatik to help run a proof of concept for a cloud-based infrastructure, running on GCP. With on-site engineers and constant communication, Siatik formed a very close working relationship with PPS. “The team at Siatik was exemplary,” recalls Avsharn. “They were well-organized, with cutting-edge technical acumen and very creative solutions to our problems. They were real game-changers.”

“We wanted the platform to retrain its models in response to new data and improve its recommendations with more information. Normally this would be a manual process but Google Cloud ML Engine lets the models do this automatically.”

—Kimoon Kim, Lead Solution Architect and Data Engineer, Siatik

The proof of concept was successful, with GCP outperforming the existing infrastructure in terms of how it handled compute demands, databases, and storage.

“It’s the speed of GCP that really impresses us,” says Avsharn. PPS saw that GCP wasn’t just an opportunity to migrate its existing infrastructure to the cloud. With Siatik’s help, it redesigned its monolithic core architecture to one based around microservices using Google Kubernetes Engine (GKE). For data processing and storage, Cloud Dataflow and Cloud Datastore proved invaluable, while Stackdriver helped the IT team stay on top of logging and monitoring the system.

“Google Cloud makes migrations very easy,” says Brett St. Clair, CEO at Siatik. “It takes care of all the hard work with configurations and replications, so when we switch the machines on, everything is ready and working.”

The ease with which PPS migrated to GCP means that it can now tackle strategic goals much more quickly than before. The most ambitious of these is an AI-powered product recommendation platform. Information is collected from customers who opt in at a defined point in their journey, this database is queried using BigQuery, and the information is fed into the platform. The AI model then calculates the most appropriate products for each member, according to their personal history.

“Most of the product recommendation engines out there are based on clustering, where you’re offered products based on your peer groups,” explains Avsharn. “For the first time, we can make recommendations to members based on their individual preferences and historical behavior. That’s really powerful for us.”

Siatik helped PPS use TensorFlow and Cloud Machine Learning Engine to build the AI platform. For the engineers, these easy-to-use tools helped speed up the process considerably, allowing them to host the models locally without any fuss. Previously, it took one to three months to manually build the model and match an offer to a customer. With the AI platform, a match takes just a few minutes. Cloud ML Engine, in particular, helped the platform adapt to new information on the fly and easily make adjustments to its hyperparameters, that is, preset variables which define the model-training process.

“We wanted the platform to retrain its models in response to new data and improve its recommendations with more information,” says Kimoon Kim, Lead Solution Architect and Data Engineer at Siatik. “Normally this would be a manual process but Google Cloud ML Engine lets the models do this automatically.”

“Google Cloud helped us cancel out a lot of the noise around machine learning and AI. We don’t have to build new complicated algorithms or hire huge teams of data scientists to benefit. We just bring our data and use the right tools to focus on what’s really important.”

—Avsharn Bachoo, CTO, PPS

Harnessing artificial intelligence for real-world results

PPS deployed its new AI recommendation platform in December, 2018. Just a couple of months later, its impact was clear. “In around eight weeks, we saw a 5 percent growth in sales,” says Avsharn. “It’s been a direct result of building our recommendation platform with Google Cloud. We can offer the right products to the right members.”

For developers and engineers at PPS, working with Google Cloud gives them access to high performance technology and automation options with GKE. As a result, the infrastructure runs 70 percent faster than before with fewer cores and less memory. Developers can also work in mature testing environments, and for the first time, are able to build pre-production environments, leading to better quality products. More strategically, moving to a serverless, cloud-based infrastructure has helped PPS take control of its budget, moving away from intermittent, large capital spends to more manageable, project-to-project flows of operational expenditure. The company expects to see savings of around 50 percent, or $695,000.

“We have a lot more flexibility with our resources thanks to Google Cloud,” says Avsharn. “When we have a new idea, we don’t have to outlay new capital such as servers before we can even start working on it. We just spin up instances when we want and spin them back down when we’re done.”

With the AI platform deployed and working well, PPS is already looking at ways to improve it, including real-time updates and further automation. Soon, the company will integrate the platform with more sales campaigns for more effective targeting to boost sales even further. Meanwhile, it’s also experimenting with machine learning to spot patterns in data at scale for fraud analytics and risk assessment.

For PPS, working with Google Cloud has helped it transform quickly and effectively from disrupted to disruptor. The company is now looking to gain the same transformative effects by implementing G Suite for increased productivity and collaboration.

“Google Cloud helped us cancel out a lot of the noise around machine learning and AI,” says Avsharn. “We don’t have to build new complicated algorithms or hire huge teams of data scientists to benefit. We just bring our data and use the right tools to focus on what’s really important.”

In the United Kingdom, the popularity of online grocery shopping is expected to surge from about 6% of the market today to 9% by 2021, according to market research firm Mintel. One of the pioneers of online-only grocery retailing is Ocado, based in Hatfield, Hertfordshire in the U.K. Since starting commercial deliveries in 2002, the company has grown to 600,000 active customers, 260,000 weekly orders, and £1.39 billion in annual revenue.

Ocado takes supermarket trips out of the equation by enabling shoppers to purchase items online through its convenient web and mobile applications. Items are then picked and packed in automated warehouses and shipped directly to customers in a one-hour time slot of their choosing. Ocado’s delivery punctuality is 95%, order accuracy is 99%, and its service footprint now reaches more than 70% of the U.K. population.

“Google Cloud Platform gives us the flexibility and performance to tackle the large and complex data challenges unique to our business.”

—Paul Clarke, Chief Technology Officer, Ocado

The company achieved its success by building in-house almost all the technology and automation that powers its end-to-end e-commerce, fulfillment, and logistics platform. Ocado also developed a new platform, the Ocado Smart Platform (OSP), which offers large brick-and-mortar grocery retailers around the world access to a best-in-class solution for online grocery.

Democratizing machine learning

The shopping journey for online grocery retailing differs significantly from other e-businesses. Customers often buy dozens of products at once, a single household may have multiple buyers using multiple devices, and product shelf life may only be a couple of days.

“We often say that having built an end-to-end platform that can do online grocery scalably and profitably, we can do other forms of online retail; but the reverse does not necessarily follow,” says Paul Clarke, Chief Technology Officer at Ocado. “Google Cloud Platform gives us the flexibility and performance to tackle the large and complex data challenges unique to our business.”

The Ocado business model takes advantage of consumers’ shifting preferences and the links between digital technology and shopping experiences.

“Google Cloud Machine Learning Engine gives us the agility we need. Our developers were able to try out TensorFlow and see firsthand the benefits of machine learning in the cloud.”

—Paul Clarke, Chief Technology Officer, Ocado

The company has been building machine learning into its systems for over five years. Until recently, Ocado machine learning applications required specialist data scientists, typically with PhDs in machine learning, who would build these solutions from the ground up. It also required the specialist who set up the system and costly on-premises infrastructure to train and run these systems.

However, working with Google as a private alpha testing site for Google Cloud Machine Learning Engine accelerated its adoption of artificial intelligence (AI).

“We’ve been talking about how the cloud could democratize AI for some time,” says Paul. “Google Cloud Machine Learning Engine gives us the agility we need. Our developers were able to try out TensorFlow and see firsthand the benefits of machine learning in the cloud.”

TensorFlow is an open source software library for machine learning developed by the Google Brain team. Ocado developers, engineers, and data scientists now use TensorFlow for many of their machine learning projects. They deploy the models they build on Google Cloud Machine Learning Engine, which lets them train models faster across servers, desktop computers, and mobile devices through a single application program interface (API). Additionally, Google Cloud Machine Learning Engine integrates easily with the other Google Cloud Platform products used widely at Ocado.

What do customers really want?

One of the first TensorFlow models Ocado created was a machine learning algorithm that tags and categorizes customer emails and then prioritizes them for response.

The contact center receives thousands of emails each day and Ocado wanted to automate determining which ones needed to be answered immediately and which ones could wait.

For example, a first-time customer expressing their delight in using Ocado doesn’t need to be responded to with the same urgency as a customer who is missing an item from their order or who won’t be home to receive the delivery.

“Enabling agents to respond without having to sort through less-urgent emails improves Ocado’s responsiveness and customer service.”

—James Donkin, General Manager, Ocado

“We get a lot of emails from customers saying, ‘Our service was great,’ or ‘The driver was very courteous,'” says James Donkin, General Manager, Ocado. “But when issues like weather or road conditions potentially affect delivery, we often get surges of urgent questions. Enabling agents to respond without having to sort through less-urgent emails improves Ocado’s responsiveness and customer service.”

Using Google Cloud Machine Learning Engine, TensorFlow, and a large data set culled from several years’ worth of manually categorized customer emails, Ocado experimented on which kind of neural network architecture would best prioritize emails. After testing its models, Ocado implemented the highest-performing one and has been able to respond to urgent messages four times faster. The company also discovered that 7% of its emails don’t require a response at all, which means call center representatives now have more time to devote to higher priority messages.

“Without Google Cloud Machine Learning Engine, it would have been a lot harder to succeed on a project like email classification,” says Roland Plaszowski, who has recently managed several big data projects and initiatives at Ocado.

“Even if we invested significantly in infrastructure, it would be difficult to manage because of the computational intensity. It’s challenging and expensive to run machine learning projects at the same time without infrastructure that you can scale easily.”

Ocado also uses machine learning to predict customer behavior and improve experiences. By analyzing order data, Ocado makes shopping as frictionless as possible. For example, the ordering system can pre-populate customers’ shopping carts with items they are most likely to purchase, remind customers about items they may have forgotten, and notify them of multi-buy offers they haven’t completed, for example, only buying one of a buy one, get one free offer. Based on machine learning from previous purchase data, the Ocado system can also offer new products that are likely to delight customers.

“You will regularly see items that are more personally relevant to you instead of items that are being promoted more generally,” says James. “I’m a vegetarian, so I’m offered specials for vegetarian products that I normally buy and new ones that I’ve never bought. I’m also less likely to see things that I’m not interested in.”

Machines and machine learning

Within the Internet of Things (IoT), Ocado is looking to enhance its warehouse robots with machine learning. An integral part of the OSP, thousands of robots continually stream data into Google Cloud Storage and Google BigQuery.

Ocado data scientists apply machine learning to create a type of swarm intelligence that enables warehouse robots to work cooperatively to achieve a common goal. Projects include modules to search robot telemetry data, such as whether a battery pack is operating within standard tolerances or whether firmware has been successfully loaded, and use it to optimize maintenance schedules or detect patterns in wear and tear.

“Another challenge we’re looking at is how to embed machine learning directly into robots so they become smarter in terms of self-testing, exception handling, and error recovery,” says Paul. “This is a challenging combination of IoT, data analytics, and machine learning that we believe Google BigQuery and Google Cloud Machine Learning are particularly well suited to helping Ocado achieve.”

Scaling for new business

Scalability is also a major reason behind some of Ocado’s cloud initiatives, including the migration of all its on-premises data to the cloud. Ocado wanted to improve customer experiences, empower business teams with greater insight, and reduce IT overhead, so it consolidated onto Google Cloud Platform.

“The old databases just weren’t fast enough,” says Paul. “We needed a solution that could scale with the amount of data we generate and how we use it. Google Cloud Storage and Google BigQuery now provide the backbone, from a data point of view, for the Ocado Smart Platform.”

Ocado estimates its business, product, and transaction data is approaching two petabytes. Combining customer and supply chain data helps both internal Ocado operations and the company’s ambitions to commercialize OSP.

“When compared with other options for expansion internationally, selling OSP as a managed service lets us turn companies that could have been competitors into customers,” says Paul. “We want to build OSP once and then turn it on for multiple business-to-business customers.”

Each time Ocado adds a new hosting customer to OSP, it will launch a customized instance to fit that customer’s requirements. The capacity and performance of each new OSP instance must be able to scale quickly as the backend platform for established retailers with large numbers of products, customers, and transactions.

Ocado’s first OSP customer, Morrisons, is already benefiting from this first-of-a kind solution. Morrisons is one of the UK’s four largest supermarkets and uses OSP to power its online retail business. Using Google Cloud Platform, Ocado has stored, processed, and analyzed terabytes of Morrisons’ data using a dedicated data lake and Google BigQuery.

In addition to using Google Cloud Platform for OSP, Ocado also adopted it for its own online grocery retail business operation. Ocado originally used the Apache Spark and Apache Hadoop open-source frameworks on Google Compute Engine for its data platform. Moving to Google BigQuery frees Ocado business analysts from the complex query setup and workflows associated with Spark and Hadoop. Plus, it lets Ocado share data analytics with suppliers and partners.

Google BigQuery is well integrated with TensorFlow on Google Cloud Machine Learning Engine and Google Cloud Dataproc, the Apache Spark and Apache Hadoop service that lets Ocado use open source data tools for batch processing, querying, streaming, and machine learning. Google Cloud Dataflow and Google Cloud Dataproc handle cluster management, and provide an easy-to-use framework so developers can spend less time and money on administration and more time on delivering valuable business features.

Switching from Hadoop to Google BigQuery revealed a series of cost and performance improvements. For example, Ocado no longer needed to decide how many instances to bring up in a cluster or wait for the instances to spin up. Google handled everything.

“We simply ran our queries and paid for the resources that we use,” adds Roland. “One big win with Google BigQuery is we don’t have to do maintenance. Best of all, we saw Google BigQuery outperform our Hadoop cluster by over 80 times on our largest dataset, and for only two-thirds the cost.”

As the Olympics kicked off in Tokyo at the end of July, we found ourselves reflecting on the beauty of diverse countries and cultures coming together to celebrate greatness and sportsmanship. For this month’s blog, we’d like to highlight some key data and analytics performances that should help inspire you to reach new heights in your data journey.

Let’s review the highlights!

BigQuery ML Anomaly Detection: A perfect 10 for augmented analytics

Identifying anomalous behavior at scale is a critical component of any analytics strategy. Whether you want to work with a single frame of data or a time series progression, BigQuery ML allows you to bring the power of machine learning to your data warehouse. 

In this blog released at the beginning of last month, our team walked through both non-time series and time-series approaches to anomaly detection in BigQuery ML:

• Non-time series anomaly detection
  • Autoencoder model (now in Public Preview)
  • K-means model (already in GA)
• Time-series anomaly detection
  • ARIMA_PLUS time series model (already in GA)

These approaches make it easy for your team to quickly experiment with data stored in BigQuery to identify what works best for your particular anomaly detection needs. Once a model has been identified as the right fit, you can easily port that model into the Vertex AI platform for real-time analysis or schedule it in BigQuery for continued batch processing.

App Analytics: Winning the team event

Google provides a broad ecosystem of technologies and services aimed at solving modern day challenges. Some of the best solutions come when those technologies are combined with our data analytics offerings to surface additional insights and provide new opportunities. 

Firebase has deep adoption in the app development community and provides the technology backbone for many organization’s app strategy. This month we launched a design pattern that shows Firebase customers how to use Crashlytics data, CRM, issue tracking, and support data in BigQuery and Looker to identify opportunities to improve app quality and enhance customer experiences.

Crux on BigQuery: Taking gold in the all-around data competition

Crux Informatics provides data services to many large companies to help their customers make smarter business decisions. While they were already operating on a modern stack and not on the hunt for a modern data warehouse, BigQuery became an enticing option due to performance and a more optimal pricing model. Crux also found advantages with lower-cost ingestion and processing engines like Dataflow that allow for streaming analytics.… when it came to building a centralized large-scale data cloud, we needed to invest in a solution that would not only suit our current data storage needs but also enable us to tackle what’s coming, supporting a massive ecosystem of data delivery and operations for thousands of companies.Mark Etherington
Chief Technology Office, Crux Informatics

Technology is a team sport, and Crux found our support team responsive and ready to help. This decision to more deeply adopt Google Cloud’s data analytics offerings provides Crux with the flexibility to manage a constantly evolving data ecosystem and stay competitive.

You can read more about Crux’s decision to adopt BigQuery in this blog.

Google Trends: A classic emerges a champion

Following up on the launch of our Google Trends dataset in June, we delivered some examples of how to use that data to augment your decision making. 

As a quick recap of that dataset, Google Cloud, and in particular BigQuery, provide access to the top 25 trending terms by Nielsen’s Designated Market Area® (DMA) with a weekly granularity. These trending terms are based on search patterns and have historically only been available on the Google Trends website.https://www.youtube.com/embed/9FJAXMF0ASc?enablejsapi=1&

The Google Trends design pattern addresses some common business needs, such as identifying what’s trending geographically near your stores and how to match trending terms to products to identify potential campaigns. 

Dataflow GPU: More power than ever for those streaming sprints

Dataflow is our fully-managed data processing platform that supports both batch and streaming workloads. The ability of Dataflow to scale and easily manage unbounded data has made it the streaming solution of choice for large workloads with high-speed needs in Google Cloud. 

But what if we could take that speed and provide even more processing power for advanced use cases? Our team, in partnership with NVIDIA, did just that by adding GPU support to Dataflow. This allows our customers to easily accelerate compute-intensive processing like image analysis and predictive forecasting with amazing increases in efficiency and speed. 

Take a look at the times below:

Data Fusion: A play-by-play for data integration’s winning performance

Data Fusion provides Google Cloud customers with a single place to perform all kinds of data integration activities. Whether it’s ETL, ELT, or simply integrating with a cloud application, Data Fusion provides a clean UI and streamlined experience with deep integrations to other Google Cloud data systems. Check out our team’s review of this tool and the capabilities it can bring to your organization.