Every company these days is becoming a data company whether they know it or not. This results in preparing the company to create an ecosystem for data processing. Traditionally, organisations’ data ecosystems consisted of point solutions that used to provide data services. For example, one of the most common questions we get from customers is, “Do I need a data lake, or should I consider a data warehouse? Do you recommend I consider both?” Traditionally, these two architectures have been viewed as separate systems, applicable to specific data types and user skill sets. Increasingly,  we are seeing a blurring of lines between data warehouses and data lakes, which provide customers with an opportunity to create a more comprehensive platform that gives them the best of both worlds.

What if we don’t need to compromise? And we create an end-to-end solution compromising the entire data management and processing stages, from data collection to serving. Thus, Data platforms are typically used to store vast amounts of data in varying formats and doing so without compromising on latency. At the same time, providing a platform for all users throughout the data lifecycle. 

Emerging Trends

There are data solutions and architectures we’re seeing and anticipating. Emerging concepts include data lakehouses, data meshes, and data vaults. Some are not new and have been around in different shapes and formats, however, all of them work naturally within a Google Cloud environment. Lets look into both ends of the spectrum of enabling data and enabling teams. 

Data mesh facilitates a decentralized approach to data ownership, allowing individual lines of business to publish and subscribe to data in a standardized manner, instead of forcing data access and stewardship through a single, centralized team. On the other hand, a data lake house brings raw and processed data closer together, allowing for a more streamlined and centralized repository of data needed throughout the organization. Processing can be done in transit via ELT, reducing the need to copy datasets across systems. This allows for easier data exploration and easier governance. Data vault is designed to separate data driven and model driven parts, so the way data is integrated in the raw vault enables parallel loading so that large implementations can scale out easily.

In Google Cloud, there is no need to keep them separate. In fact, with interoperability among our portfolio of data analytics products, you can easily provide access to data residing in different places, effectively bringing your data lake and data warehouse together on a single platform.

Let’s look at some of the technological innovations that make this reality. BigQuery’s storage API allows treating a data warehouse like a data lake, letting you access the data residing in BigQuery. For example, you can use Spark to access data residing in the data warehouse without it affecting performance of any other jobs accessing it. This is all made possible by the underlying architecture, which separates compute and storage. 

We continue to offer specialized products and solutions around data lake and data warehouse functionality but over time we expect to see a significant enough convergence of the two systems that the terminology will change. At Google Cloud, we consider this combination an “analytics data platform”.

Tactical or Strategical

Key differentiators of Google Cloud’s data analytics platform are being open, intelligent, flexible, and tightly integrated. There are many technologies in the market which provide tactical solutions that may feel comfortable and familiar. However, this can be a rather short-term approach that simply lifts and shifts a siloed solution into the cloud. In contrast, an analytics data platform built on Google Cloud provides modern data warehousing and data lake capabilities with close integration to our AI Platform. It also provides built-in streaming, ML, and geospatial capabilities and an in-memory solution for BI use cases. Depending on your organizational data needs, Google Cloud has the set of products, tools, and services to create the right data platform for you. 

To become a truly data-driven organization, the first step is to design and implement an analytics data platform that meets your technical and business needs. Whether you want to empower teams to own, publish, and share their data across the organization, or you want to create a streamlined store of raw and processed data for easier discovery, there is a solution that best meets the needs of your company.

To learn more about the elements of a unified analytics data platform built on Google Cloud, and the differences in platform architectures and organizational structures, read our Unified Analytics Platform paper.

Engineers that build last-mile delivery services belong to an elite order, a hallowed subcategory. Delivery customers are incredibly demanding when it comes to speed and convenience, and the services they use must take variables like increased traffic, road conditions, human error, and even driver availability into account every day.

Nuro is a company with a new approach to delivery services. Nuro has a fleet of autonomous vehicles designed to address many of the problems related to last-mile delivery. And every day, these vehicles — and their sensors — generate a lot of data before parking for the night. For Nuro engineers, that data can help them understand the impact of new on-road features, make improvements to their vehicles’ software, and ensure even better deliveries for their customers.

For Nuro, the key challenge is how to move petabytes of data as quickly, securely, and easily as possible from their edge environments, like vehicle depots, to Google’s Cloud Storage. For this delivery effort, Nuro selected Google’s Transfer Appliance with its new online transfer capability, now generally available.

Helping Nuro to speed up data delivery from the edge to the cloud

Like many Google Cloud customers, Nuro collects data from remote environments, like vehicle depots, that have different networking and storage capabilities when compared to a traditional data center. For a transfer solution to be effective moving unstructured data from these environments to the cloud, the solution needs to be easy to deploy and automate, while still providing similar performance as a more complicated alternative.

The Transfer Appliance was built for this use case. It arrives to customers as a physical appliance with a preconfigured version of Google’s Storage Transfer Service software already installed. Customers can move files to the appliance by using SFTP or SCP, or, alternately, can mount the appliance as an NFS share and copy target. Data can be stored locally on the appliance or transferred over the network, and secure encryption — at-rest and in-flight — is enabled by default.

With these new appliances, Nuro will be able to automate much of their storage transfer needs. When their autonomous vehicles return to the depot, they can move data like software logs, LIDAR data, and sensor data — all ideal fits for Google’s Cloud Storage — from parked vehicles to the Transfer Appliance. Online transfers can then be performed throughout the day, ensuring a steady stream of valuable data in the cloud for developers to analyze and use in their nightly builds. All of this will help Nuro’s engineering leaders like Jie Pan to run more productive development teams with less operational overhead.

“Our autonomous vehicles generate a tremendous amount of useful data, and our goal is to get that data to our engineers as soon as possible,” said Jie Pan, Engineering Manager at Nuro. “When vehicles return to the depot, we can move data hourly into Cloud Storage over the network. We also have the flexibility to return the Transfer Appliance back to Google Cloud. Most importantly, this rapid transfer architecture gives a meaningful boost to engineering productivity and development velocity.”

Going the extra mile

Engineering and infrastructure leaders understand the value of delivering the right data to the right teams, as fast as possible. By adding preconfigured, over-the-network transfer into a turnkey Transfer Appliance, Google Cloud customers can more easily automate these data deliveries by scheduling regular migrations of on-premises files, objects, and other unstructured data to our Cloud Storage.

As Nuro continues to grow their manufacturing and testing footprint, they plan to use Transfer Appliances to further scale and simplify their data migration from on-premises to Google Cloud. Cutting the time to migrate their data by more than half will make for happier, more productive developers, and that will help Nuro bring us all the future of delivery a little faster.

If you’d like to learn more about Transfer Appliance and its new online transfer capability, click here or reach out to your Google Cloud account team.

Japan is home to millions of vending machines installed on streets and in buildings, sports stadiums and other facilities. Vending machine owners and operators, including beverage manufacturers, stock these machines with different product combinations depending on location and demand. For example, they primarily display coffee and energy drinks in machines placed in offices and sports drinks and mineral water in machines at sports facilities. The combinations also vary by season: for example, owners and operators may display cold beverages in summer and hot beverages in winter. 

Traditionally, vending machine operators have relied on the intuition and experience of sales managers to determine the optimum product mix for each vending machine. However, in recent years, manufacturers such as Coca-Cola Bottlers Japan (CCBJ) have turned to data to analyze and make strategic decisions about when and where to locate products in machines.

CCBJ is the number one Coca-Cola bottler in Asia and vending machines comprise the bulk of its business. The organization operates about 700,000 machines across Tokyo, Osaka, Kyoto, and 35 prefectures. Minori Matsuda, Google Developer Expert and also Data Science Manager at CCBJ, says “The billions of data records collected from 700,000 physical devices are a great asset and a treasure trove we can take advantage of.”

Minori points out that when considering the mix of products in vending machines in sporting facilities, the managers naturally assume sports drinks would generally sell well. However, analysis of purchase data – including hot drinks and hot drinks plus sports drinks – found many parents purchased sweet drinks such as milk tea when they attended games or sessions involving their children.  “Analyzing data gives us new discoveries and, by using catchy storytelling techniques from exploratory data analysis, we are instilling a data culture within our company,” he says. “It’s worth creating by looking at facts rather than making assumptions!”

Minori believes that to analyze the vast amount of data collected from more than 700,000 vending machines, the business needs a powerful analytical platform. However, until recently, CCBJ had to extract data for analysis from its core systems, load this data into a warehouse it created and perform the required analyses.  The billions of records of data generated across the fleet – including transaction data – exposed some challenges for traditional analysis platforms. They could not efficiently process data at a considerable scale: it could take a day to return results and required extensive maintenance due to the size.

CCBJ considered building a machine learning (ML) platform as a layer on top of existing systems in August 2020 and opted for Google Cloud the following month.  “I feel that Google Cloud has an edge in all products and is very well thought out,“ says Minori, noting the scalability and cost of the platform allow the business to take a ‘trial and error’ approach to achieve the best outcomes from ML. Google Cloud also delivered the required visibility and flexibility to help the business deliver change every day against key performance indicators. 

MLOps platform streamlines ML pipeline development

CCBJ built its analysis platform using Vertex AI (formerly AI Platform) centered on a BigQuery analytics data warehouse, and partly using AutoML for tabular data. “We have created a prediction model of where to place vending machines, what products are lined up in the machines and at what price, how much they will sell, and implemented a mechanism that can be analyzed on a map,” says Minori, adding that building the platform with Google Cloud was not difficult. “We were able to realize it in a short period of time with a sense of speed, from platform examination to introduction, prediction model training, on-site proof of concept to rollout.”

The new data analytics platform of CCBJ consists of the following parts:

Data Sources

• The data collected from the vending machines are all stored on BigQuery.

Data Discovery and Feature Engineering

• Minori and other data scientists at CCBJ are using Vertex Notebooks, where they access the data on BigQuery by executing SQL queries directly from the Notebooks. This environment is used for the data discovery process and feature engineering. 

ML Training

• For ML training, CCBJ uses AutoML for Tabular data, Custom model training on Vertex AI, and BigQuery ML. AutoML gives model performance with AUC curves and also feature importance graphs.

ML Prediction and Serving

• For ML prediction, CCBJ uses Online Prediction for AutoML models and Online Prediction for custom models for real-time prediction when the salesperson finds the interesting point
• Batch Prediction is used for generating a large prediction map that covers the whole country
• The prediction results are distributed to sales managers’ tablets

CCBJ started constructing the platform in September 2020, and completed it within a month. The business has conducted proofs of concept at its base in Kyoto since February 2021, and since April, has rolled out the platform to sales managers in 35 prefectures in one metropolitan area. “Data analysis is built into the day-to-day routines of sales managers with 100% utilization,” says Minori. “They can utilize the prediction results on tablets that were able to achieve pretty high accuracy from the start.”

The hardest part was the education of sales managers in the field; having them understand the reasoning behind the ML prediction results for particular outcomes, so they could be convinced to make use of the results. “For example, regarding a new installation location predicted by the model, it seemed that there was no effective information for installation from the map information, but when I actually went there, there was a motorcycle shop and it was a place where young people who like motorcycles gathered,” says Minori. “Or there is a small meeting place where the elderly in the neighborhood are active. 

“In many cases, new discoveries that cannot be understood from map information alone can be derived from the data.”

Minori also points to a phenomenon whereby humans pursued and confirmed factors inferred by the model – meaning that once they experienced analysis and it worked effectively, they asked why the same type of analysis or prediction could not be undertaken next time. The resulting cycle of more inquiries generated, more information gathered and more data captured for analysis meant the accuracy of results was improved.

Minori describes Vertex AI as having a number of strengths in helping CCBJ build a ML data analysis platform. “One of the major merits of Vertex AI was that we were able to realize MLOps that streamlines the entire development life cycle from construction of the ML pipeline to its execution,” he says.

With near real-time data analysis through Google Cloud, CCBJ teams can spend time developing strategies rather than waiting for data requested from the IT systems department. Exploratory data analysis is also considerably easier as repeated trial and error has greatly improved the accuracy of analyses. Before we used Machine Learning, most machine placement processes were done by human senses, by looking at a map to find the suggestion points. By using Machine Learning to generate a massive number of placement point suggestions, the efficiency of routing of salespeople has been dramatically improved. 

In the future, CCBJ aims to automate the continuous training pipeline with Vertex AI. “CCBJ is a tech company that operates in the food industry,” says Minori. With the organization operating a vending machine network of 700,000 units, it would like to create new businesses based on utilization and analyzing data. Some of these businesses may be based on Sustainable Development Goals (SDGs) initiatives such as the utilization of recycled PET bottles, measures to prevent food loss and ways of using vending machines to contribute to local communities, which we have been working on for some time. It would be interesting if we could collaborate with Google Cloud on these in the future.”

Google Cloud Results

• Improves performance and reliability, enabling Pantheon to serve larger customers
• Supports 99.95% uptime across 200,000+ websites
• Reduces cloud infrastructure costs by 40%
• Enables future analytics offerings based on machine learning and big data analytics

Nearly every business needs a web presence—but the vast majority of companies don’t want to be involved in the technical aspects of coding, deployment, hosting, security, and scaling websites. To stay focused on the business value and creative aspects of their websites and avoid managing infrastructure, thousands of companies turn to Pantheon, a website operations and hosting platform that powers over 200,000 websites.

Pantheon promises its customers speed, reliability, and scalability plus world-class collaboration and workflow tools. For five years, the company was able to deliver high service levels in all three areas running its platform on bare-metal virtual cloud servers. However, as its business grew, network links began to saturate under heavy load, risking instability. As Pantheon’s business evolved to focus on servicing some of the largest websites in the world, the company wanted to partner with a more innovative cloud services provider.

“We wanted a partner that could give us what we offer to our own customers: the flexibility to scale smoothly and consume services without building them from scratch,” says David Strauss, CTO, Pantheon. “It was time to move beyond custom containers on managed VMs and extend our cloud strategy to include next-generation technologies for container management and analytics.”

Pantheon evaluated several leading cloud providers and determined that Google Cloud Platform would be the best fit for its business and customers. Engineering had the final say, running a battery of functionality and performance tests at the storage, database, and web server layers.

“In every test our engineers did, Google Cloud Platform came in as better, faster, and more cost effective than the competition,” says Niall Hayes, COO, Pantheon. “We compared MariaDB to Google Cloud SQL and Cassandra to Google Bigtable, and container density improved from 250 to 400 containers per server.”

Migrating 200,000+ sites in 2 weeks

Pantheon wanted to make the transition transparent to its customers, so a fast and smooth migration to Google Cloud Platform was essential. With help from Google, Pantheon completed the migration quickly and moved 500TB of databases, code, and files with zero customer impact.

“We migrated over 200,000 websites to Google Cloud Platform in 2 weeks, including 50,000 that are heavily trafficked and actively developed, and nobody noticed,” says Josh Koenig, Co-founder and Head of Products at Pantheon. “The speed was incredible. There was no downtime, and we filed no additional support tickets with Google during the entire process.”

The platform for platforms

For its content management system runtime environment, Pantheon runs its own homegrown container management technology on Google Compute Engine. To automate scaling for other core services such as its routing layer and distributed file system, it uses Google Kubernetes Engine for cluster management and orchestration.

“Google is the clear leader in Kubernetes and container management, which aligns very well with our open source values and our vision for the future,” says Niall. “With Google Kubernetes Engine we get better resource efficiency, and automated operations and autoscaling take a lot of administration off our plate.”

In addition to smooth scaling, Pantheon and its customers benefit from improved performance and reliability thanks to the high-quality private network offered by Google.

Further, the Google partnership with Fastly enables direct connectivity to Google Cloud Platform to improve performance for edge caching. As a result of these improvements, Pantheon raised its availability service level agreement (SLA) from 99.9% to 99.95% and can now take on even larger customers.

“Google’s network topology, both locally and globally, performs better and more reliably than competing solutions, making Google Cloud Platform the best choice for us and for our customers,” says David. “Google beats any other cloud provider as the best platform-for-platforms.”

Adds Josh: “Google has unbelievable technology around persistence and replication between zones and regions, and that is not something we could find anywhere else. This allows us to offer advanced disaster recovery and failover services to our customers.”

Strengthening customer relationships

Pantheon uses Google BigQuery, a fully managed, cloud-based data warehouse, to integrate with Fastly and analyze website traffic on behalf of its customers. Previously, Pantheon was unable to ingest edge data quickly enough from Fastly, limiting its ability to identify issues and provide the best customer service. Today, Fastly streams logs in real time into Google BigQuery for analysis, giving Pantheon a wealth of insights.

“We use Google BigQuery to identify customers that have outgrown their infrastructure or need to right-size for business growth,” says David. “We can have proactive conversations and add a lot of value to the relationships. Soon, we plan to make Google BigQuery available to our customers so they can better understand their own traffic.”

Adds Niall: “Google Cloud Platform is more data-oriented than other cloud providers, making it a better match for our needs and our customers’ strategic initiatives.”

Integrated, granular security

Pantheon appreciates that Google Cloud services are built for public cloud, with granular security as a core design and development requirement. Employees simply use their G Suite credentials to gain access to Google Cloud Platform infrastructure and services.

“We’ve been a G Suite shop for years because of the paperless collaboration benefits,” says Josh. “G Suite connects our distributed company, and it was very natural to use those same logins for Google Cloud Platform.”

Staying competitive and productive

Moving to Google Cloud Platform opens up new possibilities for services Pantheon can offer to customers in the future, including machine learning and big data analytics, to give them a more complete view of how digital experiences are driving their businesses. Internally, engineers can move faster, do more effective capacity planning, and provide better service as Pantheon moves its products upmarket.

Pantheon expected to save 20% on cloud infrastructure costs by moving to Google Cloud Platform, but was able to double that savings with resource optimization and managed services.

“Since moving to Google Cloud Platform, our platform is more secure, reliable, and scalable than ever. We reduced our cloud infrastructure costs by 40%, and our customers’ sites run 45% faster than industry benchmarks,” says Niall. “Our engineers are Google fans for a reason—they’re happier, more efficient, and more productive on Google Cloud Platform.”