Together we can solve for the future of cloud security. Join us to learn how you can stay ahead of the next generation of threats with Google Cloud – whether you need to keep your organization secure in the cloud, on-premises, or in a hybrid environment. Get fresh insights from industry leaders and engage in interactive sessions that can help you solve your most critical security challenges. 

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The Security Summit is part of our digital Google Cloud Summit series. Check out the other events in the series to explore Google Cloud technology applications in various industries and dive into our latest digital innovations.Original air date:20 July 2021 21:30 Register to watch on demand.

June is the month that holds the summer solstice, and some of us in the northern hemisphere get to enjoy the longest days of sunshine out of the entire year. We used all the hours we could in June to deliver a flurry of new features across BigQuery, Dataflow, Data Fusion, and more.  Let’s take a look!

Simple, Sophisticated, and Secure

Usability is a key tenant of our data analytics development efforts. Our new user-friendly BigQuery improvements this month include:

• Flexible data type casting
• Formatting to change column descriptions 
• GRANT/REVOKE access control commands using SQL

We hope this will delight data analysts, data scientists, DBAs, and SQL-enthusiasts who can find out more details in our blog here.

Beyond simplifying commands, we also recognize that it’s equally important to have more sophistication when dealing with transactions. That’s why we introduced multi-statement transactions in BigQuery.

As you probably know, BigQuery has long supported single-statement transactions through DML statements, such as INSERT, UPDATE, DELETE, MERGE and TRUNCATE, applied to one table per transaction. With multi-statement transactions, you can now use multiple SQL statements, including DML, spanning multiple tables in a single transaction. 

This means that any data changes across multiple tables associated with all statements in a given transaction are committed atomically (all at once) if successful—or all rolled back atomically in the event of a failure. 

We also know that organizations need to control access to data, down to the granular level and that, with the complexity of data platforms increasing day by day, it’s become even more critical to identify and monitor who has access to sensitive data. 

To help address these needs,  we announced the general availability of BigQuery row-level security. This capability gives customers a way to control access to subsets of data in the same table for different groups of users. Row-level security in BigQuery enables different user personas access to subsets of data in the same table and can easily be created, updated, and dropped using DDL statements. To learn more, check out the documentation and best practices.

Simple, Safe, and Smart

Beyond building a simpler, more sophisticated and more secure data platform for customers, our team has been focused on providing solutions powered by built-in intelligence. One of our core beliefs is that for machine learning to be adopted and useful at scale, it must be easy to use and deploy.  

BigQuery ML, our embedded machine learning capabilities, have been adopted by 80% of our top customers around the globe and it has become a cornerstone of their data to value journey.  

As part of our efforts, we announced the general availability of AutoML tables in BigQuery ML.  This no-code solution lets customers automatically build and deploy state-of-the-art machine learning models on structured data. With easy integration with Vertex AI, AutoML in BQML makes it simple to achieve machine learning magic in the background. From preprocessing data to feature engineering and model tuning all the way to cross validation, AutoML will “automagically” select and ensemble models so everyone—even non-data scientists—can use it.   

Want to take this feature for a test drive? Try it today on BigQuery’s NYC Taxi public dataset following the instructions in this blog! 

Speaking of public datasets, we also introduced the availability of Google Trends data in BigQuery to enable customers to measure interest in a topic or search term across Google Search.  This new dataset will soon be available in Analytics Hub and will be anonymized, indexed, normalized, and aggregated prior to publication. 

Want to ensure your end-cap displays are relevant to your local audience?  You can take signals from what people are looking for in your market area to inform what items to place. Want to understand what new features could be incorporated into an existing product based on what people are searching for?  Terms that appear in these datasets could be an indicator of what you should be paying attention to.

All this data and technology can be put to use to deploy critical solutions to grow and protect your business. For example,  it can be difficult to know how to define anomalies during detection. If you have labeled data with known anomalies, then you can choose from a variety of supervised machine learning model types that are already supported in BigQuery ML. 

But what if you don’t know what kind of anomaly to expect, and you don’t have labeled data? Unlike typical predictive techniques that leverage supervised learning, organizations may need to be able to detect anomalies in the absence of labeled data. 

That’s why, we were particularly excited to announce the public preview of new anomaly detection capabilities in BigQuery ML that leverage unsupervised machine learning to help you detect anomalies without needing labeled data.  

Our team has been working with a large number of enterprises who leverage machine learning for better anomaly detection. In financial services for example, customers have used our technology to detect machine-learned anomalies in real-time foreign exchange data.  

To make it easier for you to take advantage of their best practices, we teamed up with Kasna to develop sample code, architecture guidance, and a data synthesizer that generates data so you can test these innovations right away. 

Simple, Scalable, and Speedy

Capturing, processing and analyzing data in motion has become an important component of our customer architecture choices. Along with batch processing, many of you need the flexibility to stream records into BigQuery so they can become available for query as they are written.  

Our new BigQuery Storage Write API combines the functionality of streaming ingestion and batch loading into a single API. You can use it to stream records into BigQuery or even batch process an arbitrarily large number of records and commit them in a single atomic operation.

Flexible systems that can do batch and real-time in the same environment is in our DNA: Dataflow, our serverless, data processing service for streaming and batch data was built with flexibility in mind.  

This principle applies not just to what Dataflow does but also how you can leverage it—whether you prefer using Dataflow SQL right from the BigQuery web UI, Vertex AI notebooks from the Dataflow interface, or the vast collection of pre-built templates to develop streaming pipelines.

Dataflow has been in the news quite a bit recently. You might have noted the recent introduction of Dataflow Prime, a new no-ops, auto-tuning functionality that optimizes resource utilization and further simplifies big data processing. You might have also read that Google Dataflow is a Leader in The 2021 Forrester Wave™: Streaming Analytics, giving Dataflow a score of 5 out of 5 across 12 different criteria.  

We couldn’t be more excited about the support the community has provided to this platform. The scalability of Dataflow is unparalleled and as you set your company up for more scale, more speed, and “streaming that screams”, we suggest you take a look at what leaders at Sky, RVU or Palo Alto Networks have already accomplished.

If you’re new to Dataflow, you’re in for a treat: this past month, Priyanka Vergadia (AKA CloudGirl) released a great set of resources to get you started. Read her blog here and watch her introduction video below!

https://youtube.com/watch?v=WRspZRG9e90%3Fenablejsapi%3D1%26

Simple structure that sticks together

We thrive to be the partner of choice for your transformation journey, regardless where your data comes from and how you choose to unify your data stack.  

Our partners at Tata Consultancy Services (TCS) recently released research that highlights the importance of a unifying digital fabric and how data integration services like Google Cloud Data Fusion can enable their clients to achieve this vision.

We also  announced SAP Integration with Cloud Data Fusion, Google Cloud’s native data integration platform, to seamlessly move data out of SAP Business Suite, SAP ERP and S4/HANA. To date, we provide more than 50 pipelines in Cloud Data Fusion to rapidly onboard SAP data.  

This past month, we introduced our SAP Accelerator for Order to Cash.  This accelerator is a sample implementation of the SAP Table Batch Source feature in Cloud Data Fusion and will help you get started with your end-to-end order to cash process and analytics. 

It includes sample Cloud Data Fusion pipelines that you can configure to connect to your SAP data source, perform transformations, store data in BigQuery, and set up analytics in Looker. It also comes with LookML dashboards which you can access on Github.

Countless great organizations have chosen to work with Google for their SAP data. In June, we wrote about ATB Financial’s journey and how the company uses data to better serve over 800,000 customers, save over CA$2.24 million in productivity, and realize more than CA$4 million in operating revenue through “D.E.E.P”, a data exposure enablement platform built around BigQuery.

Finally, if you are an application developer looking for a unified platform that brings together data from Firebase Crashlytics, Google Analytics, Cloud Firestore, and third party datasets, we have good news!  

This past month, we released a unified analytics platform that combines Firebase, BigQuery, Google Looker and FiveTran to easily integrate disparate data sources,  and infuse data into operational workflows for greater product development insights and increased customer experience. This resource comes with sample code, a reference guide and a great blog!  We hope you enjoy it. See you all next month!

https://youtube.com/watch?v=L25Vfzr2Ciw%3Fenablejsapi%3D1%26

Editor’s note: When Europe’s largest mobile communications company, Vodafone, was asked by the European Commission to help understand population movement across the European Union and the UK to help fight COVID-19, it was able to provide anonymized mobile network-based insights to answer the call. Here’s how Vodafone, with the support of Google Cloud, rapidly mobilized the COVID-19 frontline, while respecting its customers’ privacy.

With the emergence of COVID-19 in early 2020, the European Commission—the executive branch of the European Union (EU)—knew that technology would be instrumental in its fight to control the pandemic. With various lockdowns imposed across its member states, the Commission was keen to predict and prevent the spread of COVID-19 and to manage the related social, political and financial impacts. 

Mobile network data helps track COVID-19 across the EU

Mobile networks produce location data, which can be turned into useful anonymous insights to understand population movement within a geographic area. The European Commission, working with mobile industry association GSMA (Groupe Speciale Mobile Association), asked Europe’s major mobile phone operators for help in producing insights to support the fight against COVID-19. As the largest mobile network operator within the EU, Vodafone saw this as a critical opportunity to participate. 

Vodafone had previous experience of using mobile network data to support pandemic research. For example, in 2019, Vodafone provided mobility pattern analysis to help track the spread of Malaria in Mozambique. And, during the early stages of the COVID-19 pandemic (prior to working with the European Commission), Vodafone assisted the Italian and Spanish governments in understanding their citizens’ mobility patterns. Vodafone had also previously offered anonymized and aggregated population mobility insights to support public transport and tourism authorities and retail organizations in a number of countries. Consequently, Vodafone was perfectly placed to play a greater role in supporting the European Commission’s response to the pandemic. 

When asked to assist the European Commission, Vodafone first considered how it could safely share its data with the governing body without providing details on the individual movements of its customers. It realized it could achieve this through an elaborate set of anonymization and aggregation techniques. Insights are aggregated from a minimum of 50 users and Vodafone only shared these anonymous insights and never the actual raw data with the Commission. As specified by the EU, these insights are then presented onto a large geographical region, typically a city or a county with thousands of people living in that area.

These insights illustrate how people move, helping to determine how lockdowns and self-isolation measures were impacting behaviors.

Using Google Cloud to collate and store population mobility data

In April 2020, Vodafone began migrating its operations, including its mobile data, to Google Cloud on servers in Europe and the UK with elaborate security safeguards, including encryption, building on a previous partnership. 

With the data residing in EU and UK data centers and not the United States, Vodafone could then retrieve anonymous insights from Google Cloud Storage instantaneously. Before supplying any information to the European Commission, however, Vodafone used Dataflow to validate the data and run a series of tests to ensure the database had accurate data, before ingesting and archiving the relevant metrics. For instant access, the data was then made available to the European Commission using a Redis database on Google Kubernetes Engine.

To ensure aggregate Vodafone customer data was always safe, secure, and anonymous, all entry points to the front-end were protected behind Google Cloud Armor, where only specific IP addresses were allowed. Using these tools, seamless data pipelines fed in predefined key performance indicators from each specified European market. While data quality measures ensured the definitions for metrics across markets were consistent and could be accurately compared.

The architecture (pictured below) shows how Vodafone integrated and anonymized its data on Google Cloud.

Live interactive dashboard shows population mobility in real-time

With its data integrated on Google Cloud, Vodafone created a live, interactive dashboard to track mobility patterns and share relevant information with the European Commission in real-time. 

The European Commission Joint Research Center (JRC) was able to gather valuable information from these insights, which enabled them to see where population mobility was aiding the spread of the disease, when cross-referenced with health data. It could also assess the implications of lockdowns on different populations and forecast cross-country spreading.

Mobile data aids disease modeling for multiple stakeholders

The Vodafone data became instrumental in modeling the likely course of the disease too. For example, the University of Southampton in the UK used it to predict the outcome of different coordinated COVID-19 exit strategies across Europe. This research was published in Science Magazine in September 2020. 

The Vodafone data dashboard continues to be used by individual governments, NGOs and organizations to further investigate the impacts of the pandemic and to measure the effectiveness of response strategies alongside the rollout of vaccination programs. The project also helped Vodafone win a DataIQ award for most effective stakeholder engagement. 

Using the learnings from this project, Vodafone has been able to adapt its own B2B solution, called Vodafone Analytics, by adaptIng and migrating the code to work in Google Cloud Platform. This solution has been rolled out across Germany, Greece, Portugal and South Africa, and new countries are being onboarded every day. Vodafone Analytics already has more than 100 customers leveraging it for a variety of use cases—Italian fashion retailer OVS, uses it for its smart retail operation, while global real estate company, JLL, uses it to understand the footfall passing through its properties. 

Working together, Vodafone and Google Cloud continue to help a range of organizations, governments, and NGOs navigate through the ongoing pandemic,  optimize their operations, and help the greater good, without infringing individuals’ fundamental rights to privacy.

To learn more about Google Cloud and Vodafone, watch our full interview here.

As the way people work continues to evolve, keeping security policies in place that protect organizations but give workers the ability to get things done is more important than ever. IT and security teams must aim to stay a step ahead of web-based security threats that come their organization’s way. To help, the Center for Internet Security (CIS) team has released the latest CIS Benchmark 2.1 for Google Chrome. This Benchmark offers independent recommendations on which Chrome policies to configure to help support organizations’ security and compliance needs. Thanks to Chrome being built with security at its core, in many cases, Chrome default settings are aligned with CIS recommendations.

Chrome is secure by default, but we also pride ourselves on providing customizations for enterprises to allow Chrome to better fit the needs of their business. And with hundreds of policies available through Chrome Browser Cloud Management and Group Policy Objects (Note: The CIS Benchmark is also available as a GPO), organizations can do just that. 

Throughout the CIS guide you’ll notice that there are different designations for configuration profiles. Any labeled Level 1, are considered to be a good baseline for an organization. Level 2 profiles are recommended for deployments that require the highest level of security, but note that these settings could have a trade off on user productivity. We recommend looking at each setting and determining if it’s a good fit for your business. 

The benchmark is made up of five sections:

• Enforced Defaults — Notes policies that are configured by default when you install Chrome. Enforcing these settings at an enterprise level can prevent these settings from being changed by business users to less secure options.
• Attack Surface Reduction — Details how to disable web features that may not be necessary in your enterprise environment and could reduce your overall attack surface.
• Privacy — Surfaces settings that improve user privacy.
• Data Loss Prevention — Contains settings that can help prevent data loss and protect your organization’s data. (Note: These recommendations cover additional capabilities that can be added to Chrome through BeyondCorp Enterprise). 
• Forensics (Post Incident) — Shares recommendations on policies that give insights into post incident forensics and analysis. 

Organizations can use these benchmarks to optimize the best way to secure Chrome in their environment. Download the CIS Benchmark here and check out our team’s configuration guide for additional recommendations on how to configure Chrome.

Note: This benchmark was created using a consensus review process composed of subject matter experts. Consensus participants provide perspective from a diverse set of backgrounds such as consulting, software development, audit and compliance, security  research, operations, government, and legal. While these recommendations come from a trusted source, it’s important for each organization to weigh which policies make the most sense for their business.