Customers love Cloud Spanner because it gives them the benefits of relational semantics and SQL while also delivering the scale and availability of non-relational databases. Many of these customers want to move even more of their work to Spanner, and have requested smaller instance sizes to support development, testing and small production workloads. We’re happy to announce that they’ll soon get what they asked for: more granular instance sizing is coming to Spanner.

Granular instance sizing will be available in Public Preview soon. With this feature, you can run workloads on Spanner at as low as 1/10th the cost of regular instances, equating to approximately $65/month.

In addition, we are launching new enterprise capabilities to break down operational silos for real-time insights and provide greater database observability to developers: 

• Datastream, now in public preview, is a change data capture (CDC) service that allows enterprises to synchronize data across heterogeneous databases and applications. With Spanner support in Datastream, users will be able to stream data from MySQL or Oracle to Spanner reliably and with minimal latency. 
• BigQuery federation to Spanner (coming soon) lets users query transactional data residing in Spanner, from BigQuery, without moving or copying data. 
• Key Visualizer, available now in public preview, provides interactive monitoring so developers can quickly identify trends and usage patterns in Spanner. 

Democratizing access with more granular instance sizing 

Today, customers provision Spanner instances by specifying the number of nodes they need to run their workloads. Each node can provide up to 10,000 queries per second (QPS) of reads or 2,000 QPS of writes (writing single rows at 1 KB of data per row) and 2 TB of storage. A Spanner node is replicated across 3 zones for regional instances, and 5 or more zones for multi-regional instances. The choice of node count determines the amount of serving and storage resources that are available to the databases in a given instance. 

Historically, the most granular unit for provisioning resources on Spanner has been one node. To enable more granular control, we are introducing Processing Units (PUs); one Spanner node is equal to 1,000 PUs. Customers can now provision in batches of 100 PUs, and get a proportionate amount of compute and storage resources. This will allow teams to run smaller workloads on Spanner at much lower cost. With this feature, customers can start at 100 PUs and scale up as needed in batches of 100 PUs, to up to 1,000 PUs (1 node), all with zero downtime. Subsequently, customers can continue to scale up by adding more nodes, just like what they do today. Customers do have the choice of using either PUs or nodes to provision resources within workloads, when those workloads occupy multiple nodes of capacity. 

To illustrate with an example, let’s say a game developer creates a Spanner instance called “baseball-game” in us-central1, with 100 PU compute capacity at $65/month price.

As you can see below, proportional maximum storage of 205 GB is assigned to the 100 PU instance.

Once the game starts becoming popular and resource demands increase, the user edits the instance to increase the compute capacity to 500 PUs with proportional maximum 1 TB of storage.

The game grows in popularity, and the gaming company prepares to launch a much-awaited capability in the game. Anticipating a sharp increase in usage at launch day, the game developers increase the compute capacity to 3,000 PUs. Compute capacity assigned to the instance over a period of time can be viewed in Cloud Monitoring graphs:

Request Access 
You can request early access to granular instance sizing feature by filling this form.

Breaking down operational silos with Datastream CDC and BigQuery federation

BigQuery Federation. BigQuery has made analytics easy by bringing together data from multiple sources for seamless analysis. Soon you’ll also be able to analyze data in Spanner directly in BigQuery. With  Spanner’s BigQuery federation, you’ll be able to instantly query data residing in Spanner in real-time without moving or copying the data. Simply set up the Spanner as an external data source in BigQuery, as shown below.

Change Data Capture ingest. Now in public preview, Datastream lets you stream change data into Google Cloud from MySQL and Oracle databases. As of today, you can ingest this change data directly into Spanner using a built-in Dataflow template. This lets you migrate data from MySQL and Oracle databases into Spanner in near-real time.

Understanding performance and resource usage with Key Visualizer

Key Visualizer is a new interactive monitoring tool that lets developers and administrators analyze usage patterns in Spanner. It reveals trends and outliers in key performance and resource metrics for databases of any size, helping to optimize queries and reduce infrastructure costs. Designed for performance tuning and instance sizing, Key Visualizer is available today in public preview in the web-based Cloud Console for all Spanner databases at no additional cost. Learn more in the Key Visualizer blog. 

Learn more

• To request early access to granular instance sizing in Spanner, fill out this form.
• To get started with Spanner today, create an instance or try it out with a Spanner Qwiklab.

2020 saw an unprecedented change in consumer behaviour around the world, with shoppers finding new ways of discovering, evaluating and buying products. This has created fresh expectations for both brands and retailers to drive consumer closeness, embrace the digital moment and transform their operations to be more agile and sustainable. These themes have been top of mind in my conversations with our CPG customers, and set the tone at Google Cloud Summit for Retail and Consumer Goods, which concluded this week. I couldn’t be more proud of our team and thankful for our customers that supported us by participating in our sessions and attending the event.

I joined Google Cloud in January 2021 after a long career in the CPG industry, and as I shared in my CPG keynote at the summit, I am thrilled to be helping bring the power of Google to drive industry innovation in CPG. At Google we call this ‘new normal’ the transformation cloud era, where we’re working with customers who want to not just save money on storage or compute, but to use cloud and digital technologies to drive agility across their business. I also call it the era of ‘consumer switching’, because Covid-19 has accelerated the likelihood of consumers to switch brands or the way they shop. Some of these changes are still happening; over 40% shoppers in a recent Google survey reported that in March 2021 they changed brands or shopped online for something they were previously buying in store.

At Google, we have an amazing team of strategists who have been researching, observing, and analyzing the many facets of consumer behavior over the last few years. In the opening keynote at the Summit Capturing the Hearts and Minds of Today’s Consumers, Google’s Human Truths Team kicked off the Retail & Consumer Goods summit by sharing some of their consumer insights, including what behavior patterns they think will “stick” as we move into a post-pandemic world.  I think these insights are especially relevant for brands, as they speak to some of our latest findings on the CPG shopper’s mindset.

Accenture estimates that there could be a 3 trillion dollar shift in value between companies as a result of consumers shifting brands and behaviours. While it is not known who the winners of the shift will be, one thing is certain – those who will be able to leverage data and analytics fastest will benefit the most from these times of rapid change. I shared some of the implications for the CPG industry in my keynote How to grow brands in times of rapid change along with the three key areas in which Google cloud is helping CPG companies drive brand success: 

1. Unlocking consumer growth with data powered insights 
2. Transforming go-to-market in the omnichannel ecosystem
3. Driving connected, efficient, and sustainable operations

Let’s take a quick look at each of them:

Unlocking consumer growth with data-powered insights

The digital marketing ecosystem is transforming for a privacy centric world, and brands are seeing a direct impact in marketing effectiveness. As the CPG industry becomes more consumer-centric and shifts more toward direct-to-consumer (D2C) business models, acquiring and activating consented first-party consumer data presents a clear opportunity to capitalize on new consumer demands. 

As a result, CPGs are turning to consumer data platforms (CDPs) to help them unify, manage, enrich, and secure all of their disparate data from different marketing tools, website analytics, email campaigns, loyalty programs, and more. Google Cloud and our ecosystem of partners can help CPGs build a privacy-centric CDP which brings together all their customer and marketing data into a modern data warehouse, with built-in predictive data visualization tools and models. With a CDP built on Google Cloud, you can integrate data from Google Marketing Platform to drive predictive marketing and media effectiveness. And with pre-built connectors you can also easily integrate non-Google media and data from other enterprise platforms like SAP and Oracle to leverage consumer data for more integrated decision making. Democratize access to data across the organization with our Business Intelligence tool Looker to enable faster decisions in real-time from marketing to supply chain to product innovation.

At the Retail & CPG Summit we shared how retailers and brands can drive consumer closeness in a privacy-centric world  featuring Procter & Gamble’s experience building and activating consumer data in a privacy-safe way to serve consumers better and maximize marketing effectiveness and drive growth across their business. And in a demo, Constellation Brands shared how they’re leveraging real-time data from several commercial sources using Looker to unpack insights and develop action plans.

Transforming go-to-market in the omnichannel ecosystem

Amidst COVID-19 restrictions and rolling lockdowns, ecommerce activity has surged past a point of no return. Direct to consumer (D2C)  or digitally native brands were able to minimize consumers switching during the pandemic  by offering a great online experience. While in-person shopping remains important, consumers are expecting more digital and omnichannel experiences and many will continue to explore new brands and purchase online. CPGs that want to maintain their market leadership can no longer afford to ignore the key role omnichannel capabilities will play in capturing the attention of both retailers and consumers.  Even if D2C sales are not a big portion of your business, you will benefit from first-party data that can help you drive insights and product innovation. 

CPG brands want to transform their older, clunky ordering processes into modern digital shopping experiences. Re-platforming legacy solutions on Google Cloud not only accelerates application innovation, but also makes it easier to quickly launch new features and products. At Google Cloud we have transformed ecommerce for large D2C brands and traditional retailers, so we know what a best in class D2C experience looks like. And we can bring it to your brands. We already help some of the biggest brands in retail modernize ecommerce and enhance product discovery with solutions like Visual Search and Recommendations AI. All of these can help you build a best in class omni channel presence for your brands.

We had several sessions on improving your omni channel experience, including 

Why search abandonment is the metric that matters featuring Macy’s and Conversational Commerce with Google with Albertsons, where we shared how Google’s conversational experiences can help consumers message businesses from wherever they are, and whenever they need them.

Driving connected, efficient, and sustainable operations

The superpowers of  AI/ML are not just for marketing.  Did you know that research from MIT and Google Cloud has found companies that use AI/ML can drive 2x more data-driven decisions, 5x faster decision making, and 3x faster execution? By connecting your operations in real time with demand signals like search, trends, weather, mobility and supercharging this data with AI and ML, you can make smarter and quicker business decisions. For example, you can use search trends to drive demand forecasting and ramp up manufacturing for popular products.

Google Cloud can help you modernize legacy business applications by migrating them to the cloud and using AI/ML and smart analytics to drive business outcomes. Take SAP for example – a Forrester study found that modernizing SAP with Google not only resulted in 56% more efficient IT teams – it also generated 160% 3-year ROI. 

SAP data on Google Cloud breaks down silos across SAP, marketing, manufacturing systems, and external data sources for next-level intelligent operations. For example, with SAP and Google Cloud, you can combine product, media, CRM, digital commerce and site data from SAP and non-SAP sources to uncover stronger consumer insights and fuel product discovery along the path to purchase. You can merge SAP product and sales data with consumer,  market data and Google geo trends to drive targeted promotion outcomes, maximizing the ROI of promotional dollars across retail channels. You can also integrate supply chain and manufacturing data from SAP systems with consumer, marketing and Google geo market data to improve demand forecasting and optimize supply chain logistics. The possibilities are endless. This is why we describe SAP modernization as The Gift That Keeps On Giving. Check out the session on SAP from the Summit and hear from Rodan + Fields on their experience of modernizing SAP on Google Cloud. 

Another solution that excites me is Vertex AI, which transforms the demand forecasting process. Traditional demand forecasting accuracy is a challenge for most CPGs. Current forecasting methods do not take into account granular factors that impact demand, like local weather, demographics, or unforeseen events. With our recently launched Vertex Forecast, Google is making it much easier to start using cutting-edge machine learning models for demand forecasting. In our session Demand Forecasting: Time for Intelligence, Not Intuition featuring American Eagle Outfitters, we share how you can adopt a data science approach to demand forecasting that’s customized to your unique needs.

CPG organizations come to Google for help solving their toughest problems, whether it be driving new consumer growth, unlocking new routes to market, or building connected, sustainable operations. And we bring the best of Google: innovation, culture, infrastructure, AI/ML, and a deep understanding of consumer behaviour to help them build best-in-class brands. 

I’d like to end with a topic that’s very close to my heart. This is around Solving for Sustainability in Retail and Consumer Goods. Our research shows that 62% of shoppers cared about at least one sustainability aspect when purchasing online in 2020. In addition, the events of the past year have triggered consumers to re-evaluate their relationships with brands and prioritize those that are more sustainable in the context of the pandemic. Watch this session to learn more about how retailers and consumer goods companies can leverage technology, data, and machine learning to help make sustainability a core part of the recovery.

All our session content is available on demand. Ready to learn more about how we’re helping CPG brands and manufacturers drive results? Learn more about Google Cloud’s consumer packaged good solutions and reach out to your Google Cloud sales executive to set up a deeper conversation on how we can help you grow your brands today and in the future.

Investment management is a heavily data-driven industry—portfolio managers and investment researchers require a large number of data sources to guide them in shaping their investment strategies. 

New cloud capabilities and technologies enable investment managers to process data faster than ever before and iterate on ideas quickly to fuel innovation in the signal generation process and gain a competitive edge. 

Using the cloud for investment research workflows makes it easier to onboard data from data providers, spin up large compute workloads in the midst of market volatility or during heavy research cycles, and manage complex machine learning or natural language workflows to gain market insights.

We hear from industry leaders that they’re exploring new ways to run investment research. “Differentiated investment strategies require new types of information sources, and new ways to process that information,” David Easthope, senior analyst, Market Structure and Technology, Greenwich Associates. “And that, of course, relies heavily on having access to reliable and scalable storage, computational, and AI / ML resources. More specifically, quantitative strategies can benefit from the computational platforms and embedded AI/ML capabilities the cloud can offer.” 

Google Cloud gives investment managers essential components to work and operate faster as they bring their investment research workflows to the cloud. Here are the key highlights:

1. Simplify, speed up your data acquisition, discovery, and analytics

The foundation of any investment strategy starts with data—acquiring it, detecting patterns, and analyzing it for insights. Enabling data providers to easily share large datasets such as tick history within a high-performance analytics engine can greatly reduce the data engineering overhead when possible.

Once data is onboarded, you can tag business and technical metadata related to your datasets and provide portfolio managers the ability to discover these datasets via a search interface.

We further review analytics options for various scenarios, including aggregating massive datasets, creating dashboards, and incorporating streaming analytics workloads.

2. Take advantage of burst compute workloads

Data engineers and researchers require ready access to burst compute capabilities to perform backtesting, portfolio simulations and run risk calculations. Cloud works well for these workloads due to its elasticity, consumption-based models, and hardware evolution.

Many investment managers are shifting to a container-based strategy along with a Kubernetes-based scheduler for greater consistency, scaling and efficiency in environments with a large number of researchers. Cloud managed services and a rich suite of CI/CD tools can make this vision a reality while improving security and developer productivity.

3. Tackle machine learning (ML) and model deployment with the help from cloud

Quantitative researchers scour vast amounts of market and alternative data sources searching for signals and correlations, while ML engineers have the challenge of taking these signals and moving them to production.

Google Cloud empowers users to create and operationalize their models without wasting valuable time with a comprehensive set of MLOps tools. 

In this paper, we explore multiple solutions for ML and model deployment. Those capabilities reduce the amount of time operationalizing ML models, so quants and data scientists have more time to devote to differentiating activities. 

4. Get the data you need in less time with Natural Language and Document AI

Thousands of financial filings, news articles, and sell-side research reports are generated every day, and it’s difficult for humans alone to process this volume of information. These documents are often generated in many languages and the ability to do entity recognition, sentiment or syntactical analysis in those languages, or perhaps translate them into the language of the portfolio manager is of critical importance. Google Cloud provides these capabilities through pre-trained models, or allows you to train high-quality models with your own datasets.

Getting started

There are plenty of emerging technologies, tools, and approaches available to help investment managers today. At Google Cloud, we can help you access, organize, and utilize these essential components to make your research faster, reliable, and more valuable.

To learn more about these four keys to better investment research, check out our whitepaper for more.