Modern applications need to process large-scale data at millisecond latency to provide experiences like instant gaming leaderboards, fast analysis of streaming data from millions of IoT sensors, or real-time threat detection of malicious websites. In-memory datastores are a critical component to deliver the scale, performance, and availability required by these modern applications. 

Memorystore makes it easy for developers building applications on Google Cloud to leverage the speed and powerful capabilities of the most loved in-memory store: Redis. Memorystore for Redis standard tier instances are a popular choice for applications requiring a highly available Redis instance. Standard tier provides a failover replica across zones for redundancy and provides fast failover with a 99.9% SLA. However, in some cases, your applications will require more read throughput from a standard tier instance. One of the common patterns customers use to scale read queries in Redis is leveraging read replicas.

Introducing Memorystore for Redis Read Replicas

Today we are excited to announce the public preview of Memorystore for Redis Read Replicas, which allows you to seamlessly scale your application’s read requests by 6X with the click of a button.

With read replicas, you can easily add up to five replicas and leverage the read endpoint to automatically load balance read queries across all the available replicas, increasing read performance linearly with each replica added. Additionally, Memorystore’s support for Redis 6 introduced multi-thread I/O, increasing performance significantly for M3 and higher configurations. Combined, you can achieve read requests of more than a million requests per second. 

You will benefit from this new functionality in several ways. You will be able to scale on demand with up to five read replicas and use read endpoint with any redis client to easily load balance read queries across multiple replicas. This new functionality will also improve availability with automatic distribution of replicas across multiple zones. With read replicas, you will also be able to minimize application downtime with fast failover to the replica with the least replication lag. In the future, Memorystore will also easily enable read replicas on existing standard tier instances to increase read throughput. 

You can learn more about how to configure and use read replicas in the Read Replicas Overview. 

Improving performance with Read Replicas and Redis 6

With the launch of read replicas, you can easily increase the read throughput of a Memorystore instance. You can further enhance the read performance by leveraging Redis version 6 along with read replicas.

To understand why combining read replicas and Redis 6 can significantly improve your application’s read performance, let’s look at the various Memorystore configurations that you can use with your applications today. Memorystore Basic and Standard offerings  provide different capacity tiers. The capacity tier determines the single node performance of a basic and standard tier instance. 

The table below outlines the configuration of the various capacity tiers:

Up until version 5, Redis processed commands using a single thread. The processing involved reading the request, parsing the request, processing the request, and writing the response back to the socket. This approach means that all of the processing, which includes writing the response, was sequentially processed by a single vCPU regardless of the number of vCPUs available in the instance.

Redis 6 introduced I\O threading which allows writing the response using parallel threads. This functionality enables Redis 6 to more effectively leverage available vCPUs, thereby increasing the overall throughput compared to lower versions. Memorystore for Redis version 6 leverages I\O threading and automatically configures the optimal number of I\O threads to achieve the best possible performance for the various capacity tiers. We have also improved the overall network throughput for all redis versions by leveraging improvements in the Google Cloud infrastructure. Together these improvements deliver significant performance improvements and come at no additional cost to you. 

So what can you expect from using Redis 6? As outlined in the table, Redis version 5 and lower uses a single thread to process the write requests for all tiers while Redis 6 uses a larger number of I\O threads at higher capacity tiers which provides substantially incremental throughput for higher capacity tiers.

For example, using a Redis 6 standard tier instance with a capacity of 101 GB (M5), we have observed up to a 200% improvement in read/write performance compared to version 5, though the actual value you’ll see is dependent on your workload. You can get the benefits of Redis 6 by upgrading an existing instance or by deploying a new instance.

By enabling read replicas on an instance using Redis 6, you can further improve the read performance. Read throughput scales linearly with the number of replicas so you can increase your read queries on an instance by up to 500% using five read replicas. By combining Redis 6 and read replicas, you can see a 10X increase in read performance compared to a version 5 standard tier instance with no read replicas.

We are excited about the launch of read replicas but this is just one step in our journey to deliver the scale you need at the best price-performance. You can learn more about read replicas pricing on our Memorystore pricing page and get started using the Read Replicas Overview.

Retailers are becoming more skilled at making individual customers feel heard and valued. This is a necessity given the fact that 66% of respondents to a McKinsey survey stated that they expect email marketing messages to be tailored to their needs. While marketing personalization expertise is growing, it’s still difficult to manage, especially at scale.

Ocado Retail, one of the world’s largest dedicated online grocery retailers, delivering to over 645,000 customers in the U.K., has made personalization integral to its success. 

Let’s look at how Ocado retail worked with Google Cloud and partner Cognizant to develop a new data platform to power its personalization efforts from the ground up. 

Unifying data for more holistic, powerful personalization

To achieve its goals of personalization at scale, Ocado Retail needed a central data warehouse that could turn all forms of merchandising, advertising, business, and customer intelligence data into actionable insights. It wanted a means to accelerate customer segment identification, as well as the ideation and launch of relevant campaigns. 

“We standardized on Google Cloud, including BigQuery, as the foundation for our data platform because we knew it was the right solution for now and the future,” says Kieren Johnson, Head of IT at Ocado Retail. “We have an incredibly lean team and we needed a partner with exceptional expertise to help build an ambitious enterprise data warehouse to provide powerful insights. Cognizant was also the clear choice to help us get there.”

Cognizant worked closely with Ocado Retail to make sure its expertise in Google Cloud and other technologies aligned with Ocado Retail’s vision to drive more advanced personalization at higher scales using machine learning. The partner helped build the foundation on BigQuery, and then incorporated other Google Cloud tools such as Cloud Run, Vertex AI, and Vertex AI Natural Language to provide no-ops, all-code warehousing, and analytics capabilities.

Cognizant also took advantage of the Google Cloud Partner Success Services (PSS) program to ensure best practices were being followed throughout the project. PSS provided advisory services that guided Cognizant through the highly complex process of building the new data warehouse for Ocado Retail on Google Cloud.

Building the enterprise data platform in this way allows Ocado Retail to leverage the full power of cloud-based analytics while maintaining a lean team. It also allows the company to greatly scale up its personalization efforts.

Making customers feel valued at every touch

The work Ocado Retail has done with Google Cloud and Cognizant has positioned it to make its growing customer base feel valued, understood, and supported in every interaction. Before launching the project with Cognizant and Google Cloud, Ocado Retail was only able to run a couple of campaigns per week and knew it lacked optimal insight into each campaign’s efficacy. 

“We now run 10 times the number of campaigns we used to with the help of the data platform Cognizant built on Google Cloud,” says Kieren. “We run multiple campaigns every day for different customer segments, and all of that increased activity is entirely driven by data insights. The positive impacts on our marketing and customer service performance have been clear. We’re now working to expand what we do.”

Ocado Retail has enjoyed solid growth since the new data platform went live, including a 13% rise in active customers during fiscal year 2022, and has also seen a reduction in churn. It attributed these improvements to being better able to tailor products and communications to specific customer preferences.

Throughout the project, Cognizant supported data clean up while maximizing the scalable, flexible, and future-proofed data analytics infrastructure offered by Google Cloud.

Increasing data-driven actions

Ocado Retail plans to provide more data-driven insights to its commercial suppliers through a product called Beet Insights. So far, Beet Insights offers suppliers with intelligence about how their products are performing on the shelves. The result has been improving the role data plays throughout the supply chain, from production to purchase and beyond.

“By putting real-time insights about costs, marketing spend, and supply-funded activities into the hands of our commercial team and buyers, we are better positioned to improve our profits,” says Kieren. “At the same time, building data analytics into every part of our business will allow us to build on our personalization efforts.”

From the project’s inception, Ocado Retail ensured that the platform would be scalable and dynamic. Now, it is working to feed more data sources and streams into the warehouse to accelerate time to insights. Ocado Retail believes this next step in the evolution of the platform will unlock even more opportunities to initiate high-impact programs that transform personalization and every customer interaction.

Learn more about what Google Cloud and partners like Cognizant can do for your customer intelligence and personalization.

Business Problem
UKG Ready primarily operates in the Small and Medium Business (SMB) space, so inherently many customers are forced to operate and make key business decisions with less Workforce Management (WFM) / Human Capital Management (HCM) data. In addition to volume, SMB lacks the variety of data needed to create a dynamic and agile organization. This puts SMB at a major disadvantage compared to larger segments.

Project Goals
People Insights module is committed to surfacing insights to customers in the context of their day-to-day duties and aid in decision making. With the SMB customer data limitations mentioned above, the goal of this project was to create a global dataset that augments individual customer data to bring light to less obvious, yet important information.

Challenges
UKG Ready is a highly configurable application that gives customers the opportunity to build solutions on a platform that meets their specific business needs. High configurability gives high flexibility to customers in their usage of the software. However, it becomes nearly impossible to create a global dataset for machine learning and data insights. UKG Ready manages just under 4 million of the US workforce and some 30,000+ customers. Despite the large employee dataset size, machine learning models that are specific to customers are starved for data because the individual customers have a relatively small employee population. Does that mean we cannot support our SMB customers’ decision making with ML?

Result
Partnering with Google, we were able to develop an approach that allowed us to standardize various domain entities (pay categories, time off codes, job titles, etc.) so that we could build a global dataset to augment SMB customer data. Using machine learning we were able to build a common vocabulary across our customer base. This common vocabulary encapsulates the nuances of how our customers manage their business and yet is generalized and standardized such that the data can be aggregated over the variety of customer configurations. This allows us to serve up practical insights to customers through various use cases. Our partnership allowed us to leverage Google Cloud Services to meet the needs of our complex machine learning models, distributed data sets and CI/CD processes.

How
UKG Ready decided to partner with Google for an end-to-end solution for the analytics offering. This allowed us to focus on our core business logic without having to worry about the platform, environment configurations, performance and scalability of the entire solution. We make use of various Google Cloud services such as Cloud Triggers, Cloud Storage, Cloud Functions, Cloud Composer, Cloud Dataflow, Big Query, Vertex AI, Cloud Pub/Sub… to host our analytics solution. Jenkins manages the entire CI/CD pipelines and cloud environments are configured and deployed using Terraform.

The standardization of business entities problem was solved in three distinct steps:

Step 1: Collecting aggregated data
We needed an approach to collect aggregated data from our highly distributed, sharded, multi-tenant data sources. We developed a custom solution that allows us to extract data aggregated at source for PII and GDPR considerations and transfer to Google Cloud Storage in the fastest manner possible. Data is then transformed and stored in Big Query. Services used: GCS, Cloud Functions, DataFlow, Cloud Composer and Big Query. All processes are orchestrated using Cloud Composer and detailed logging is available in Cloud Logging (Stackdriver).

Step 2: Applying NLP (Natural Language Processing)
Once we had the variety of customer configurations or the business entities available, we then applied NLP algorithms to categorize and standardize these in buckets. This approach assumes that customers use natural language for configurations like job titles, pay codes etc.

String Preparation
The input data for string preparation process is an entity string or several strings, that describe one entity object (like name-description pair or code-name pair). The output represents set of tokens that may be used to run a classification/clustering model. The process of string preparation tokenizes strings, replaces shortcuts, handles abbreviations, translates tokens, handles grammatical errors and mistypes

ML Models

Statistical
The idea of the model is to use defined target classes (clusters) and assign several tokens (anchors) to each of them an entity that has any of those tokens would be “attracted” to appropriate class. All other tokens are weighted according to frequencies of usage of theses tokens in the entities with anchor tokens:

Using anchor tokens, we are building kind-of Word2Vec - dimensionality of vector is equal to number of target classes. The higher the specific dimension (cluster) value, the higher the probability of entity to be included in appropriate cluster. Final prediction for entity tokens list for specific class is sum of weights of all the tokens included. Predicted cluster is a cluster that has maximal prediction score.

Lexical Model
We managed to generate reasonable amount of labeled data during statistical model implementation and testing. That opens a possibility to build “classical” NLP model that uses labeled data to train classification neural network using pretrained layers to produce token embeddings or even string embeddings. We started experimentation with pre-trained models like GloVe and got good results with single words and bi-grams but started getting issues in handling of n-grams. Our Google account team came to our rescue and recommended some white papers that helped formulate our strategy. We now use Tensorflow nnlm-en-dim128 model to produce string embeddings – it was trained on 200B records English Google News corpus and produces for each input string 128-dimensional vector. After that we use several Dense and Dropout layers to build a classification model.

Ensembling
To perform ensembling all the model results for each class are cast to probabilities using softmax transformation with scale normalization. Final predicted probability is maximal average score of both models among all the classes scores – appropriate class is predicted class.

The machine learning models are deployed on Vertex AI and are used in batch predictions. Model performance is captured at every prediction boundary and monitored for quality in production.

Step 3: Making available common vocabulary
Having the standardized vocabulary, we then needed a mechanism to have the results be available in UKG Ready reports and customer specific models like Flight Risk and Fatigue. For this we again used Google Services for orchestration, data transformation and data storage.

Once the modeling is complete, we made the customer specific models leveraging the above architecture be available in Reports. We utilized our proven existing technology choices in GCP for orchestration, data transformation and data storage

Results
We are able to build a common vocabulary of our customers’ business entities with good confidence. And be an expert advisor to our SMB customers in their decision-making using machine learning. With the advice of our Google account team and using Google services we can add value to our product in a relatively short amount of time. And we are not done! We continue to use this platform for new use cases, complex business problems and innovative machine learning solutions.

Sample result:

Special thanks to Kanchana Patlolla , AI Specialist, Google for the collaboration in bringing this to light

How will your organization manage this year’s data growth and business requirements? Your actions and strategies involving data and AI will improve or undermine your organization’s competitiveness in the months and years to come. Our teams at Google Cloud have an eye on the future as we evolve our strategies to protect technology choice, simplify data integration, increase AI adoption, deliver needed information on demand, and meet security requirements. 

Google Cloud worked with* IDC on multiple studies involving global organizations across industries in order to explore how data leaders are successfully addressing key data and AI challenges. We compiled the results in our 2023 Data and AI Trends report. In it, you’ll find the metrics-rich research behind the top five data and AI trends, along with tips and customer examples for incorporating them into your plans. 

1: Show data silos the door

Given the increasing volumes of data we’re all managing, it’s no surprise that siloed transactional databases and warehousing strategies can’t meet modern demands. Organizations want to improve how they store, manage, analyze, and govern all their data, while reducing costs. They also want to eliminate conflicting insights from replicated data and empower everyone with fresh data.

A unified data cloud enables the integration of data and insights into transformative digital experiences and better decision making.

Andi Gutmans, GM and VP of Engineering for Databases, Google Cloud

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In the report, you can learn how to adopt a unified data cloud that supports every stage of the data lifecycle so that you can improve data usage, accessibility, and governance. Inform your strategy by drawing on organizations’ examples such as a data fabric that improves customer experiences by connecting more than 80 data silos, as well as other unified data clouds that save money and simplify growth. 

2: Usher in the age of the open data ecosystem

Data is the key to unlocking AI, speeding up development cycles, and increasing ROI. To protect against data and technology lock-in, more organizations are adopting open source software and open APIs.

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Understand how you can simplify data integration, facilitate multicloud analytics, and use the technologies you want with an open data ecosystem, as described in the report. Learn from metrics about global open source adoption and public dataset usage. And explore how global companies adopted open data ecosystems to improve patient outcomes, increase website traffic by 25%, and cut operating costs by 90%.

3: Embrace the AI tipping point

Pulling useful information out of data is easier with AI and ML. Not only can you identify patterns and answer questions faster but the technologies also make it easier to solve problems at scale.

We’ve reached the AI tipping point. Whether people realize it or not, we’re already using applications powered by AI—every day. Social media platforms, voice assistants, and driving services are easy examples.

June Yang, VP, Cloud AI and Industry Solutions, Google Cloud

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Organizations share how they’re reaching their goals using AI and ML by empowering “citizen data scientists” and having them focus on small wins first. Gain tips from Yang and other experts for developing your AI strategy. And read how organizations achieve outcomes such as a reduction of 7,400 tons per year in carbon emissions and a more than 200% increase in ROI from ad spend by using pattern recognition and other AI capabilities. 

4: Infuse insights everywhere

Yesterday’s BI solutions have led to outdated insights and user fatigue with the status quo, based on generic metrics and old information. Research shows that as new tools come online, expectations for BI are changing, with companies revising their strategies to improve decision making, speed up the development of new revenue streams, and increase customer acquisition and retention by providing individuals with needed information on demand.

Organizations are equipping business decision-makers with the tools they need to incorporate required insights into their everyday workflows.

Kate Wright, Senior Director, Product Management, Google Cloud

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In the report, you’ll discover why and how data leaders are rethinking their BI analytics strategies and applications to improve users’ trust and use of data in automated workflows, customizable dashboards, and on-demand reports. Global companies also share how they improve decision making with self-service BI, customer experiences with IoT analysis, and threat mitigation with embedded analytics.

5: Get to know your unknown data

Increasing data volumes can make it harder to know where and what data they store, which may create risk. Case in point: If a customer unexpectedly shares personally identifiable information during a recorded customer support call or chat session, that data might require specialized governance, which the standardized storage process may not provide.

If you don’t know what data you have, you cannot know that it’s accurately secured. You also don’t know what security risks you are incurring, or what security measures you need to take.

Anton Chuvakin, Senior Staff Security Consultant, Google Cloud

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Check out the report to learn about data security risks that are often overlooked and how to develop proactive governance strategies for your sensitive data. You can also read how global organizations have increased customer trust and productivity by improving how they discover, classify, and manage their structured and unstructured data.  

Be ready for what’s next

What’s exciting about these trends is that they’re enabling organizations across industries to realize very different goals using their choice of technologies. And although all the trends depend on each other, research shows you can realize measurable benefits whether you adopt one or all five.

Review the report yourself and learn how you can refine your organization’s data and AI strategies by drawing on the collective insights, experiences, and successes of more than 800 global organizations.