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Cloud makes it better: What's new and next for data security

Today’s digital economy offers a wealth of opportunities, but those opportunities come with growing risks. It has become increasingly important to manage the risks posed by the intersection of digital resilience and today’s risk landscape. Organizations around the world should be asking themselves: If a risk becomes a material threat, how can we help our employees continue to get work done efficiently and securely despite unexpected disruption, no matter where they are? This new era of “anywhere work” is not only a technology issue, but one that encompasses leadership support and cultural shifts. In a recent webinar, Heidi Shey, principal analyst at Forrester, and Anton Chuvakin, senior staff, Office of the CISO at Google Cloud, had a spirited discussion about the future of data security. They agreed that this is a moment of inflection, when smart organizations are rethinking their entire security approach and using the opportunity to take a closer look at their security technology stack at the same time. Here are some trends that they are seeing today.Greater volume, more variety. The data that organizations generate is not only increasing in volume, but in variety as well. Sensitive information can exist anywhere, including employee communications, messaging applications, and virtual meetings, making traditional techniques for classifying data such as manual tagging less effective. Organizations need to grow their risk intelligence by using artificial intelligence (AI) and machine learning (ML) to better identify and protect sensitive information. At the same time, employees are accessing enterprise data in multiple ways, on multiple devices, wreaking havoc on traditional security perimeters and anomaly detection. A more strategic approach. Multiplying threat vectors and vulnerabilities often drive organizations into a losing game of whack-a-mole as they acquire more and more point solutions, which leads to information silos and visibility gaps. While security modernization doesn’t require a rip-and-replace, its success depends on a more strategic approach to choosing and applying controls. Successful organizations are being deliberate in creating an ecosystem of controls that interoperate and reduce data silos and visibility gaps. Zero Trust. Central to any modern security strategy should be a Zero Trust approach to user and network access, not only for people but also for the growing number of internet-of-things (IoT) devices that exchange enterprise data. Zero Trust means that organizations no longer implicitly trust any user or device inside or outside the corporate perimeters — nor should they. Rather, a company must verify that attempts to connect to a network or application are authorized before granting access. Zero Trust replaces the perimeter security model between a trusted internal network and an untrusted external network – including virtual private networks (VPNs) used to access corporate data remotely. Unlike a traditional perimeter model in which a network could become compromised if a hacker breached the organization or if a malicious insider attempts to steal a company’s sensitive data, a Zero Trust approach helps ensure users only have access to the specific resources they need at a point in time.Growing supply chain networks. As organizations expand their supply chains to increase resilience and efficiency, they need a way for vendors, customers, and other third parties to securely access the data and applications necessary to conduct business. A Zero Trust approach to access can provide a scalable solution to meet this need. Enterprise security solutions with the speed, intelligence, and scale of GoogleCybersecurity is ever-evolving as new threats arise daily. Google Cloud’s approach takes advantage of Google’s experience securing more than 5 billion devices and keeping more people safe online than any other organization. Google Cloud brings our pioneering approaches to cloud-first security to enterprises everywhere they operate, leveraging the unmatched scale of Google’s data processing, novel analytics approaches with artificial intelligence and machine learning, as well as a focus on eliminating entire classes of threats. By combining Google’s security capabilities with those of our ecosystem and alliance partners — including Cybereason, IPNet, ForgeRock, Palo Alto Networks, and SADA — we’re bringing businesses a full stack of powerful and effective solutions for managing data access, verifying identity, sharing signal information, and gaining visibility into vulnerabilities and threats. In concert with our ecosystem of partners, we will be working with Mandiant and its partners to deliver an end-to-end security operations suite with even greater capabilities to help you address the ever changing threat landscape across your cloud and on-premise environments. In sum, Google Cloud brings you the tools, insight, and partnerships that can transform your security to meet the requirements of our rapidly transforming world.  To get a deeper dive into the trends and research driving this change, watch the “Cloud Makes it Better: What’s New and Next for Data Security” webinar with Forrester and Google Cloud.
Quelle: Google Cloud Platform

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Accelerate speed to insights with data exploration in Dataplex

Data Exploration Workbench in Dataplex is now generally available. What exactly does it do? How can it help you? Read on.Imagine you are an explorer embarking on an exciting expedition. You are intrigued by the possible discoveries and are anxious to get started on your journey. The last thing you need is the additional anxiety induced by running from pillar to post to get all the necessary equipment in place – protective clothing is torn, first aid kits are missing, and most of the expedition gear is malfunctioning. You end up spending more time on collecting these items rather than in the actual expedition. If you are a Data Consumer (Data Analyst or Data Scientist), your data exploration journey would be similar. You too, are excited by the insights your data has in store. But, unfortunately, you, too, need to integrate a variety of tools to stand up the required infrastructure, get access to data, fix data issues, enhance data quality, manage metadata, query the data interactively, and then operationalize your analysis.  Integrating all these tools to build a data exploration pipeline will take so much effort that you have little time left to  explore the data and generate interesting insights. This disjointed approach to data exploration is the reason why 68% of companies1 never see business value from their data. How can they? Their best data minds are busy spending 70% of their time2 just figuring out how to make all these different data exploration tools work.How is the data exploration workbench solving this problem?Now imagine you having access to all the best expedition equipment in one place. You can start your exploration instantly and have more freedom to experiment and uncover fascinating discoveries that will help humanity! Wouldn’t it be awesome if you too, as a Data Consumer,  get access to all the data exploration tools in one place? A single unified view that lets you discover and interactively query fully governed high-quality data with an option to operationalize your analysis?  This is exactly what the Data exploration workbench in Dataplex offers. It provides a Spark-powered serverless data exploration experience that lets data consumers interactively extract insights from data stored in Google Cloud Storage and BigQuery using Spark SQL scripts and open source packages in Jupyter NotebooksHow does it  work?Here is how data exploration workbench tackles the four most popular pain points faced by Data Consumers and Data Administrators during the exploration journey:Challenge 1: As a data consumer you spend more time on making different tools work together than on generating insights Solution: Data exploration workbench provides a single user interface where:You have 1-click access to run Spark SQL queries using an interactive Spark SQL editor.You can leverage open-source technologies such as PySpark, Bokeh, Plotly to visualize data and build machine learning pipelines via JupyterLab Notebooks.Your queries and notebooks run on fully managed, serverless Apache Spark sessions – Dataplex  auto-creates user-specific sessions and manages the session lifecycle.You can save the scripts and notebooks as content in Dataplex and enable better discovery and collaboration of that content across your organization. You can also govern access to content using IAM permissions. You can interactively explore data, collaborate over your work, and operationalize it with one-click scheduling of scripts and notebooks.Challenge 2: Discovering the right datasets needed to kickstart data exploration is often a “manual” process that involves reaching out to other analysts/data ownersSolution:  ‘Do we have the right data to embark on further data analysis?’ – This is the question that kickstarts the data exploration journey. With Dataplex, you can examine the metadata of the tables you want to query right from within the data exploration workbench. You can further use the indexed Search to understand not only the technical metadata but business and operational metadata along with the data quality scores for your data. And finally, you get deeper insights into your data by interactively querying  using the Workbench. Challenge 3:  Finding the right query snippet to use —analysts often don’t save and share useful query snippets in an organized or centralized way. Furthermore, once you have access to the code, you now need to recreate the same infrastructure setup to get results.Solution: Data exploration workbench allows users to save Spark SQL queries and Jupyter notebooks as content and share them  across the organization via IAM permissions. It provides a built-in Notebook viewer that helps you examine the output of a shared notebook without starting a Spark session or re-executing the code cells. You can not only share the content of a script or a notebook, but also the environment where the script ran to ensure others can run on the same underlying set up. This way, analysts can seamlessly collaborate and build on the analysis. Challenge 4: Provisioning the infrastructure necessary to support different data exploration workloads across the organization is an inefficient process with limited observability.Solution: Data Administrators can pre-configure Spark environments with the right compute capacity, software packages, and auto-scaling/auto-shutdown configurations for different use cases and teams. They can govern access to these environments via IAM permissions and easily track usage and attribution per user or environment.  How can I get started?To get started with the Data exploration workbench, visit the Explore tab in Dataplex. You choose the lake of your choice and the resource browser will list all the data tables (GCS and BigQuery) in the lake. Before you start: Make sure the lake where your data resides is federated with a Dataproc Metastore instance. Request your data administrator to set up an environment and grant you Developer role or associated or IAM permissions.You can then choose to query the data using Spark SQL scripts or Jupyter notebooks. You will be priced as per the Dataplex premium processing tier for the computational and storage resources used during querying.Data Exploration Workbench is available in us-central1 and europe-west2 regions. It will be available in more regions in the coming months. 1. Data Catalog Study, Dresner Advisory Services, LLC – June 15, 20202. https://www.anaconda.com/state-of-data-science-2020
Quelle: Google Cloud Platform

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Introducing automated failover for private workloads using Cloud DNS routing policies with health checks

High availability is an important consideration for many customers and we’re happy to introduce health checking for private workloads in Cloud DNS to build business continuity/disaster recovery (BC/DR) architectures. Typical BC/DR architectures are built using multi-regional deployments on Google Cloud. In a previous blog post, we showed how highly available global applications can be published using Cloud DNS routing policies. The globally distributed, policy-based DNS configuration provided reliability, but in case of a failure, it required manual intervention to update the geo-location policy configuration. In this blog we will use Cloud DNS health check support for Internal Load Balancers to automatically failover to health instances. We will use the same setup we used in the previous blog. We have an internal knowledge-sharing web application. It uses a classic two-tier architecture: front-end servers tasked to serve web requests from our engineers and back-end servers containing the data for our application. Our San Francisco, Paris, and Tokyo engineers will use this application, so we decided to deploy our servers in three Google Cloud regions for better latency, performance, and lower cost.High level designThe wiki application is accessible in each region via an Internal Load Balancer (ILB). Engineers use the domain name wiki.example.com  to connect to the front-end web app over Interconnect or VPN. The geo-location policy will use the Google Cloud region where the Interconnect or VPN lands as the source for the traffic and look for the closest available endpoint.DNS resolution based on the location of the userWith the above setup, if our application in one of the regions goes down, we have to manually update the geo-location policy and remove the affected region from the configuration. Until someone detects the failure and updates the policy, the end users close to that region will not be able to reach the application. Not a great user experience. How can we design this better? Google Cloud is introducing Cloud DNS health check support for Internal Load balancers. For an internal TCP/UDP load balancer, we can use the existing health checks for a back-end service, and Cloud DNS will receive direct health signals from the individual back-end instances. This enables automatic failover when the endpoints fail their health checks.For example, if the US frontend service is unhealthy, Cloud DNS may return the closest region load balancer IP (in our example, Tokyo’s) to the San Francisco clients depending on the latency.DNS resolution based on the location of the user and health of ILBs backendsEnabling the health checks for the wiki.example.com record provides us with automatic failover in case of a failure and ensures that Cloud DNS always returns only the healthy endpoints in response to the client queries. This removes manual intervention and significantly improves the failover time.The Cloud DNS routing policy configuration would look like this:Creating the Cloud DNS managed zone:code_block[StructValue([(u’code’, u’gcloud dns managed-zones create wiki-private-zone \rn –description=”DNS Zone for the front-end servers of the wiki application” \rn –dns-name=wiki.example.com \rn –networks=prod-vpc \rn –visibility=private’), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3ed50078bcd0>)])]Creating the Cloud DNS Record set:For health checking to work, we need to reference the ILB using the ILB forwarding rule name. If we use the ILB IP instead, then Cloud DNS will not check the health of the endpoint. See the official documentation page for more information on how to configure Cloud DNS routing policies with health checks.code_block[StructValue([(u’code’, u’gcloud dns record-sets create front.wiki.example.com. \rn–ttl=30 \rn–type=A \rn–zone=wiki-private-zone \rn–routing-policy-type=GEO \rn–routing-policy-data=”us-west2=us-ilb-forwarding-rule;europe-west1=eu-ilb-forwarding-rule;asia-northeast1=asia-ilb-forwarding-rule” \rn–enable-health-checking’), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3ed50078b750>)])]Note: Cloud DNS uses the health checks configured on the load balancers itself. Users do not need to configure any additional health checks for Cloud DNS. See the official documentation page for information on how to create health checks for GCP Load Balancers.With this configuration, if we were to lose the application in one region due to an incident, the health checks on the ILB would fail, and Cloud DNS would automatically resolve new user queries to the next closest healthy endpoint.We can expand this configuration to ensure that front-end servers send traffic only to healthy bank-end servers in the region closest to them. We would configure front-end servers to connect to the global hostname backend.wiki.example.com.The Cloud DNS geo-location policy with health checks will use the front-end servers’ GCP region information to resolve this hostname to the closest available healthy back-end tier Internal Load Balancer.Front-end to back-end communication (instance to instance)Putting it all together, we now have set up our multi-regional and multi-tiered application with DNS policies to automatically failover to a healthy endpoint closest to the end user.
Quelle: Google Cloud Platform

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BigQuery’s performance and scale means that everyone gets to play

Editor’s note: Today, we’re hearing from telematics solutions company Geotab about how Google BigQuery enables them to democratize data across their entire organization and reduce the complexity of their data pipelines. Geotab’s telematics devices and an extensive range of integrated sensors and apps record a wealth of raw vehicle data, such as GPS, engine speeds, ambient air temperatures, driving patterns, and weather conditions. With the help of our telematics solutions, our customers gain insights that help them optimize fleet operations, improve safety, and reduce fuel consumption. Google BigQuery sits at the heart of our platform as the data warehouse for our entire organization, ingesting data from our vehicle telematics devices and all customer-related data. Essentially, each of the nearly 3 billion raw data records that we collect every day across the organization, goes into BigQuery, whatever its purpose. In this post, we’ll share why we leverage BigQuery to accelerate our analytical insights, and how it’s helped us solve some of our most demanding data challenges. Democratizing big data with easeAs a company, Geotab manages geospatial data, but the general scalability of our data platform is even more critical for us than specific geospatial features. One of our biggest goals is to democratize the use of data within the company. If someone has an idea to use data to inform some aspect of the business better, they should have the green light to do that whenever they want.Nearly every employee within our organization has access to BigQuery to run queries related to the projects that they have permission to see. Analysts, VPs, data scientists, and even users who don’t typically work with data have access to the environment to help solve customer issues and improve our product offerings.While we have petabytes of information, not everything is big—our tables range in size from a few megabytes up to several hundred terabytes. Of course, there are many tricks and techniques for optimizing performant queries in the BigQuery environment, but most users don’t have to worry about optimization, parallelization, or scalability.Google BigQuery sits at the heart of our platform as the data warehouse for our entire organization.The beauty of the BigQuery environment is that it handles all of that for us behind the scenes. If someone needs insight from data and isn’t a BigQuery expert, we want them to be as comfortable querying those terabytes as they are on smaller tables—and this is where BigQuery excels. A user can write a simple query just as easily on a billion rows as on 100 rows without once thinking about whether BigQuery can handle the load. It’s fast, reliable, and frees up our time to rapidly iterate on product ideation and data exploration.Geotab has thousands of dashboards and scheduled queries constantly running to provide insights for various business units across the organization. While we do hit occasional performance and optimization bumps, most of the time, BigQuery races through everything without a hiccup. Also, the fact that BigQuery is optimized for performance on small tables means we can spread our operations and monitoring across the organization without too much thought—20% of the queries we run touch less than 6 MB of data while 50% touch less than 800 MB. That’s why it’s important that BigQuery excels not only at scale but at throughput for more bite-sized applications. The confidence we have in BigQuery to handle these loads across so many disparate business units is part of why we continue to push for increasingly more teams to take a data-driven approach to their business objectives.Reducing the complexity of the geospatial data pipelineThe ability of BigQuery to manage vast amounts of geospatial data has also changed our approach to data science. On the scale we are operating, with tens of petabytes of data, it’s not feasible for us to operate with anything other than BigQuery. In the past, using open-source geospatial tools, we would hit limits at volumes of around 2.5 million data points. BigQuery allows us to model over 4 billion data points, which is game-changing. Even basic functions, such as ingesting and managing geospatial polygons, used to be a complex workflow to string together in Python with Dataflow. Now, those geographic data types are handled natively by BigQuery and can be streamed directly into a table. Even better, all of the analytics, model building, and algorithm development can happen in that same environment—without ever leaving BigQuery. No other solution that would provide geospatial model building and analytics at this scale in a single environment. Here’s an example. We have datasets of vehicle movements through intersections. Even just a few years ago, we struggled to run an intersection dataset at scale and had to limit its use to one city at a time. Today, we are processing all the intersection data for the entire world every day without ever leaving BigQuery. Rather than worry about architecting a complex data pipeline across multiple tools, we can focus on what we want to do with the data and the business outcomes we are trying to achieve. BigQuery is more than a data warehouseWe frequently deal with four or five billion data points in our analytics applications and BigQuery functions like a data lake. It’s not just our SQL database—it also easily supports all of our unstructured data, such as BLOBS from our CRM systems or GIS data files as well as images. It’s been a fascinating experience to see SQL consuming more and more unstructured data and applying a more relational structure that makes it consumable and familiar to analysts with traditional database management skills. A great example is BigQuery’s support for JSON functions, which allows us to take hierarchical non-uniform data structures of metadata from things like OpenStreetMap and store it natively in BigQuery with easy access to descriptive keys and values. As a result, we can hire a wider range of analysts for roles across the business, not just PhD-level data scientists, knowing they can work effectively with the data in BigQuery. Even within our data science team, most of the things that we needed Python to accomplish a few years ago can now be done in SQL. That allows us to spend more time deriving insights rather than managing extended parts of the data pipeline. We also leverage SQL capabilities, such as stored procedures, to run within the data warehouse and churn through billions of data points with a five-second latency.The ease of using SQL to access this data has made it possible to democratize data across our company and give everyone the opportunity to use data to improve outcomes for our customers and develop interesting new applications. Reimagining innovation with Google CloudOver the years, we haven’t stayed with BigQuery because we have to—we want to. Google Cloud is helping us drive the insights that will fuel our future and the future of all organizations looking to raise the bar with data-driven insights and intelligence. BigQuery’s capabilities have continued to evolve along with our needs, with the addition of increasingly complex analytics, data science methodologies, geospatial support, and BQML. BigQuery offers Geotab an environment that provides a unique ability to manage, transform and analyze geospatial data at enormous scale. It also makes it possible to aggregate all kinds of other structured and unstructured data needed for our business into a single source of truth—against which all of our analytics can be performed.
Quelle: Google Cloud Platform

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How UX researchers make Google Cloud better with user feedback

Customer experiences are critical to user experience (UX) researchers at every level of developing Google Cloud products. Whether it’s migrating a user to Google Cloud, helping them understand it once they are there, or delving into using Cloud services, one thing is clear: learning from the people who use Google Cloud is fundamental.Understanding our usersUX researchers touch various points of the customer journey, like migration, cloud operations, and data analytics. In each of these areas, understanding the customer’s business needs and goals grants the UX researcher greater insight into how to provide the best possible experience. This is primarily done by engaging with user feedback. From widely-used products like Google Kubernetes Engine and BigQuery, to the targeted solutions of the Recommendation API and Error Reporting tools, Google Cloud’s team of UX researchers are pursuing a deep understanding of customer’s workflows and pain points. Between in-person and remote sessions with UX researchers and online surveys, our Google User Experience Research program offers a range of options for customers to engage in user feedback.Applying insights to our productsGoogle’s researchers work with our product development team to act on user feedback. Insights learned during one of Google Cloud’s early customer migrations resulted in the co-creation of our beta and general availability versions of Migrate for Windows Containers in Google Slides. Not only did this underscore the importance of proactive and collaborative customer integration, but it was well received in the developer community. The user group also broadened the Google Cloud team’s perspective on the Error Reporting system by requesting that the product solutions expand to handle more categories of errors and events. As a result, the Error Reporting System now catches more types of issues, which are also presented to customers, making the reports more useful for them. Advocating for user insightsResearchers are the champions of the usability in applications, a principle which guides UX researchers as they tap into real world customer experiences to inspire new roadmaps and ways of working. This process is made possible when users share their lived experiences with our researchers. If you are interested in helping Google Cloud become more helpful for everyone, sign-up to be a part of the Cloud UX team’s user participant pool here.
Quelle: Google Cloud Platform

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Paperstack uses Google Cloud to empower e-commerce sellers

Facing tight margins, e-commerce retailers are always trying to find the perfect balance between keeping inventory in stock and meeting changing market demands. This challenge has been exacerbated by the COVID-19 pandemic, where costs and supply chain disruptions continue to rise.  Recognizing the challenges facing e-commerce companies, we builtPaperstack. Our competitive financing enables e-commerce companies to routinely purchase inventory, invest in advertising, and even hire new talent. Many customers of Paperstack are e-commerce brands that use platforms like Shopify, Wix, Etsy, Square, and others and have been generating revenue for at least 12 months. Most of them use funds to fuel their marketing efforts, buy larger quantities of inventory, and cover fees for freelancers. At the same time, we empower e-commerce sellers to streamline operations with sophisticated machine learning (ML) algorithms that automatically track, analyze, and display critical business metrics on a personalized dashboard while removing bias. “The funding process can be very demoralizing. I was feeling discouraged by the time I came across Paperstack. They brought genuine interest and excitement instead of frustration and disappointment. Funding was straightforward, fair and easy. I remain grateful for their advice, excellent communication and encouragement. They are truly standouts in the messy world of small business funding.” — Allison Tryk, Founder/CEO, Floramye Since launching in 2021, Paperstack onboarded over 250 e-commerce companies that generated over 10 million in demand for working capital in on-demand funding, successfully rolling out new products and increasing their sales. As Paperstack continues to grow, we’ll introduce additional solutions and services that enable e-commerce sellers to further lower overhead costs and profitably scale their business.  “Assel, Vadim and the Paperstack team have been wonderful to work with. They are the embodiment of what a funding partner should be – a partner that genuinely wants you to succeed. They provided us with working capital at a crucial time of growth for our company. Since working with Paperstack, we have been able to expand our team and our space while allowing us to grow our revenues. Not only does Paperstack provide funding, they have also built a wonderful network of entrepreneurs and consistently deliver value through their resources such as their podcasts. They are truly a game changing partner and we are proud to have partnered with them.” — Charlene Li and Vincent Li, founders, EatableDesigning a commercially viable product, accelerating time to marketWe felt the time had come to positively disrupt the e-commerce space by helping small online merchants overcome basic startup costs so they can compete on a global scale. Prior to Paperstack, Assel Beglinova spent over 3 years in banking where she helped thousands of customers to get access to credit. She saw how outdated the process was, and realized that there was so much innovation needed when it came to the internet economy. We also knew we needed to play an active role in closing the funding gap for women, who receive less than three percent of e-commerce venture capital. Being a female immigrant founder and experiencing the realities of fundraising for women founders, Assel made it her mission to empower founders who look and sound like her with the capital and resources they need to grow.Paperstack founders Assel Beglinova and Vadim LidichAfter formulating a business plan and building a pre-market version of Paperstack inGoogle Data Studio, we applied to join theGoogle for Startups Accelerator: Women Founders program. We wanted to make Paperstack a reality and hoped the accelerator would help us design a commercially viable product and speed time to market. It did.Participating in the program gave us immediate access to theGoogle Cloud e-commerce team, the incredible technical knowledge of dedicatedGoogle for Startups experts, and Google Cloud credits which we used to affordably trial and deploy Google Cloud solutions. We also connected with mentors, introduced ourselves to Google Cloud customers, and talked to many e-commerce companies that had completed different Google for Startups accelerators.“First and foremost as a Black woman founder navigating scaling my business with historically limited access to capital, it’s amazing to see another woman changing this narrative. It’s been so great being a part of the Paperstack portfolio. Not just because of the extra capital, but also the hands-on support from Assel and team. They’ve held fireside chats with industry experts for us and Ivan has armed me with my own personal library of supplier and investor contacts. The funding was a great bridge for us to work on increasing brand awareness and cover our overhead including our warehouse rent. We used the funds to improve PR packaging and increased our team of ambassadors from 8 to 40 within one month. Paperstack is truly working to stand out from other providers through the resources they provide, an intuitive dashboard, and feasible fees for small businesses.” — Alicia Scott, Founder & CEO, Range BeautyIn less than three months, we leveraged thesecure-by-design infrastructure of Google Cloud and the expertise of Google Cloud engineers to build the first commercial iteration of Paperstack. Our backend is written in JavaScript, which we seamlessly deploy onApp Engine and take advantage of features such as auto scaling. In addition, we useCloud Functions to create and connect event driven services—and work closely with Google Cloud partnerMongoDB to integrate, optimize, and deploy our databases. We rely onData Studio to power customizable and personalized dashboards, while innovating quickly and easily onGoogle Workspace. We’re also looking forward to exploring additional Google CloudAI and machine learning products such asVertex AI to further expand the capabilities of our business analytics.Scaling Paperstack with the Google for Startups Accelerator: Women FoundersLaunching, scaling, and commercializing a market-ready platform on a limited budget would not have been possible without the amazing support of the Google for Startups Accelerator: Women Founders. Since completing the program, we’ve received positive feedback from investors, raised several rounds of funding, and participated in additional industry accelerators such asTechstars Equitech Accelerator —a partner of Google for Startups—and theFinTech Sandbox Accelerator.“I am absolutely in love with Paperstack and what they are building. Since helping me land funding I was struggling to access otherwise, they took a chance on my business The Established which allowed me to finally initiate some projects we had been keeping at bay due to lack of resources. I have since strongly connected with the founders and I love what they are doing to build a community and network in which I can feel seen and supported as an marginalized founder.” — Essence Iman, Founder/CEO,  The EstablishedAlthough we’ve come a long way, our journey is only beginning. We plan to launch Paperstack in new markets worldwide and empower millions of e-commerce companies to build economically sustainable businesses with the financial resources and tools our company provides. We’re also dedicated to helping women founders in the e-commerce space get equal access to capital by designing our underwriting and funding evaluation process in an inclusive, bias-free way. That means our underwriting technology does not disadvantage people who didn’t go to target school, or those who don’t come from a privileged background. As a result, we’ve noticed that 80% of our customers are women and minority founders – or 16 times more than the industry average! As we expand the Paperstack team, we’ll continue to work closely with our partners at Google for Startups to connect with the right people, products, and best practices to grow our success.If you want to learn more about how Google Cloud can help your startup, visit our pagehere to get more information about our program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more.
Quelle: Google Cloud Platform

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Google Cloud Certifications adds new sustainable benefits and donation opportunities

We are thrilled to announce some new Google Cloud certification benefits that reinforce our commitment to Google Cloud certified individuals and our global sustainability strategy. Read on for a look at what’s to come for our certified community.New Google Cloud certified digital toolkit for all Google Cloud certified individualsAn official Google Cloud certified digital toolkit will now be awarded to all Google Cloud certified and recertified individuals, including those with the Cloud Digital Leader, Associate Cloud Engineer, and Professional Google Cloud certifications. The assets in this digital toolkit are an exciting new addition to the Google Cloud certification benefits, and were designed to help any Google Cloud certified individual show off their certification accomplishment. And the best part –  they’re instantly available once becoming officially Google Cloud certified. Keep an eye out for new designs that will become available to the Google Cloud certified community on an ongoing basis.The assets include: Google Cloud Certified Google Meet background: Use this digital background to proudly display your certified status during Google Meet meetings Google Cloud Certified official email signature: Use this template to easily add your Google Cloud certification(s) on your email signatureGoogle Cloud Certified social media profile banner: Update your LinkedIn profile with a banner to better stand out across your network#GoogleCloudCertified social media bannerOur Google Cloud certified community can access their digital toolkit in the Google Cloud Certified Group.Sustainable options for Google Cloud certified professional merchandiseIndividuals who become newly Google Cloud certified at the professional level will unlock exclusive Google Cloud certified professional merchandise, which will now be shipped in sustainable, low carbon-footprint shipping boxes – that are reusable and made with 100% recycled materials. We are excited to also launch a new global fulfillment platform that will allow us to fulfill orders locally in Europe and India.  This will not only deliver items faster but will also reduce carbon emissions from transit.  Merchandise will continue to be sourced through sustainable suppliers that align with Google’s sustainability practices.The merchandise unlocked by individuals who achieve a Professional Google Cloud certification features brands that respect our planet, such as Timbuk2, which uses 100% nylon and polyester from  pre- and post- consumer materials to construct their backpacks. Celebrate your Google Cloud certification with a charitable donationIn lieu of selecting merchandise, individuals who certify or renew a professional level certification can celebrate their certification by requesting Google Cloud to donate ($55 USD) to one of two charitable organizations. We’re proud to share that we’re working with Pratham.org, one of the largest NGO organizations in India that focuses on improving the quality of education in India and ALERTWildfire, a network of nearly 1,000 specialized camera installations used by first responders and volunteers to detect, monitor, and fight wildfires before they become too big. The cameras also support critical evacuation efforts by relaying real-time information when it’s needed most.Interested in becoming Google Cloud certified? Check out our Google Cloud certifications and take advantage of the available Google Cloud certified benefits.
Quelle: Google Cloud Platform

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Building a resilient architecture with Cloud SQL

Customers build and deploy many applications that have varied requirements from an availability perspective. The databases that store and manage the data created and used by these applications play a key role in determining the overall availability of the applications. Some applications can tolerate a longer recovery time or RTO (Recovery Time Objective) and have ways to deal with some amount of data loss or RPO ( Recovery Point Objective). Other critical applications have a requirement for no data loss i.e. the RPO has to be zero and be able to return to service quickly i.e. a short RTO.. The databases supporting these applications should have capabilities to meet the various RPO and RTO requirements that the applications need. Cloud SQL is Google Cloud’s fully managed relational database service for MySQL, PostgreSQL, and SQL Server. It provides full compatibility with the source database engines while reducing operational costs by automating database provisioning, storage capacity management, and other time-consuming tasks. Cloud SQL has built-in features to ensure business continuity with reliable and secure services, backed by a 24/7 SRE team providing a 99.95% SLA for the service.This guidediscusses the features in Cloud SQL that can be used to build a resilient database architecture. We list the planned and unplanned events that can impact the availability of the Cloud SQL instance. We discuss the unique capabilities of Cloud SQL that can control and limit the impact of planned maintenance events in terms of downtime. Planned events could be configuration updates or patching activities that are needed to keep the database instance in optimal health.We look at the various types of unplanned events that can cause an outage and discuss features that can be used by customers to reduce the RPO and RTO. The features include  database backup and recovery capabilities that form the foundation of an availability strategy and can protect against failures and human errors and reduce the data loss exposure to a minimum.For environments where the RPO needs to be zero, we discuss the Cloud SQL High Availability configuration that provides a RPO of zero. The replication capabilities of Cloud SQL and how replicas can be used in an availability architecture, both in the same region and using cross-region replicas as a building block to address the disaster recovery requirements, are also covered in the guide.Finally, the guide briefly discusses best practices for applications to manage connections to the database, use observability to monitor load on the database and handle failures gracefully.
Quelle: Google Cloud Platform

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Announcing Apache Iceberg support for BigLake

Apache Iceberg is a popular open source table format for customers looking to build data lakes. It provides many features found in enterprise data warehouses, such as transactional DML, time travel, schema evolution, and advanced metadata that unlocks performance optimization. Iceberg’s open specification allows customers to run multiple query engines on a single copy of data stored in an object store. Backed by a growing community of contributors, Apache Iceberg is becoming the de facto open standard for data lakes, bringing interoperability across clouds for hybrid analytical workloads and systems to exchange data. Earlier this year, we announced BigLake, a storage engine that enables customers to store data in open file formats (such as Parquet) on Google Cloud Storage and run GCP and open source query engines on it in a secure, governed, and performant manner.  BigLake unifies data warehouses and lakes by enabling BigQuery and open source frameworks like Spark to access data with fine-grained access control. Today, we are excited to announce that this support now extends to the Apache Iceberg format, enabling customers to take advantage of Iceberg’s capabilities to build an open format data lake while benefiting from native GCP integration using BigLake. “Besides BigQuery, a large segment of our data is stored on GCS. Our Datalake leveraged Iceberg to tap into this data in an efficient and scalable way on top of incredibly large datasets. BigLake integration makes this even easier by making this data available to our large BigQuery user base and leverage its powerful UI. Our users now have the ability to realize most BigQuery benefits on GCS data as if this was stored natively.”  — Bo Chen, Sr. Manager of Data and Insights at Snap Inc.Build a secure and governed Iceberg data lake with BigLake’s fine-grained security modelBigLake enables multi-compute architecture: Iceberg tables created in supported open source analytics engines can be read using BigQuery.code_block[StructValue([(u’code’, u”# Creation of table using Iceberg format with Dataproc Spark rnrnCREATE TABLE catalog.db.table (col1 type1, col2 type2) USING iceberg TBLPROPERTIES(bq_table='{bigquery_table}’, bq_connection='{bigquery_connection}’);”), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3edb58d5c110>)])]Once the table has been created in Spark, easily query using BigQuery:code_block[StructValue([(u’code’, u’# Query table using the BigQuery console rnrnSELECT COL1, COL2 FROM bigquery_table LIMIT 10;’), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3edb59443e10>)])]Apache Spark already has rich support for Iceberg, allowing customers to use Iceberg’s core capabilities, such as DML, transactions, and schema evolution, to carry out large-scale transformation and data processing. Customers can run Spark using Dataproc (managed clusters or serverless), or use built-in support for Apache Spark in BigQuery (stored procedures) to process Iceberg tables hosted on Google Cloud Storage. Regardless of your choice of Spark, BigLake automatically makes those Iceberg tables available for end users to query.Administrators can now use Iceberg tables, similar to BigLake tables, and don’t need to provide end users access to the underlying GCS bucket. The end user access is delegated through BigLake, simplifying access management and governance.  Administrators can further secure Iceberg tables using fine-grained access policies, such as row, column level access control, or data masking, extending the existing BigLake governance framework to Iceberg tables. BigQuery utilizes Iceberg’s metadata for query execution, providing a performant query experience to end users.This set of capabilities enables customers to store a single copy of data on object stores using Iceberg and run BigQuery as well as Dataproc workloads on it in a secure, governed, and performant manner, eliminating the need to duplicate data or write custom infrastructure. For GCP customers who store their data on BigQuery Storage and Google Cloud Storage, BigLake now further unifies data lake and warehouse workloads.  Customers can directly query, join, secure, and govern data across BigQuery storage and Iceberg tables on Google Cloud Storage. In the coming months, we will extend Apache Iceberg to Amazon S3 and Azure data lake Gen 2, enabling customers to build multi-cloud Iceberg data lakes. Differentiate your Iceberg workloads with native BigQuery and GCP integrationThe benefits of running Iceberg on Google Cloud extend beyond realizing Iceberg’s core capabilities and BigLake’s fine-grained security model. Customers can use native BigQuery and GCP integration to use BigQuery’s differentiated services on Iceberg tables created over Google Cloud Storage data. Some key integrations most relevant in the context of Iceberg are:  Securely exchange Iceberg data using Analytics Hub – Iceberg as an open standard provides interoperability between various storage systems and query engines to exchange data. On Google Cloud, customers use Analytics hub to share BigQuery & BigLake tables with their partners, customers, and suppliers without needing to copy data. Similar to BigQuery tables, data providers can now create shared datasets to share Iceberg tables on Google Cloud storage. Consumers of the shared data can use any Iceberg compatible supported query engine to consume the data, providing an open and governed model of sharing and consuming data.  Run data science workloads on Iceberg using BigQueryML – Customers can now use BigQueryML to extend their machine learning workloads to Iceberg tables stored on Google cloud storage, enabling customers to realize AI value on data stored outside of BigQuery. Discover, detect and protect PII data on Iceberg using Cloud DLP – Customers can now use Cloud DLP to identify, discover and secure PII data elements contained in Iceberg tables, and secure sensitive data using BigLake’s fine-grained security model to meet workload compliance.Get Started Learn more about BigLake support for Apache Iceberg by watching this demo video, and a panel discussion of  customers building using BigLake with Iceberg. Apache Iceberg support for BigLake is currently in preview, sign up to get started. Contact a Google sales representative to learn how Apache Iceberg can help evolve your data architecture.Special mention to the engineering leadership of Micah Kornfield, Anoop Johnson, Garrett Casto, Justin Levandoski and team to make this launch possible.
Quelle: Google Cloud Platform

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Introducing Sensitive Actions to help keep accounts secure

At Google Cloud, we operate in a shared fate model, working in concert with our customers to help achieve stronger security outcomes. One of the ways we do this is to identify potentially risky behavior to help customers determine if action is appropriate. To this end, we now provide insights on what we are calling Sensitive Actions. Sensitive Actions, now available in Preview, are focused on understanding IAM account, or user account, behavior. They are changes made in a Google Cloud environment that are security relevant — and therefore important to be aware of and evaluate — because they may be precursors to an attack, an effort to make other attacks possible, or part of an effort to monetize a compromised account. They can quickly highlight potentially malicious activities that are facilitated by authentication cookie theft, and are another defense-in-depth mechanism that Google Cloud offers to help address this attack vector. The Sensitive Actions that are detected today will appear in two places. They are available in Security Command Center Premium, the primary source for security and risk alerts in Google Cloud, as Observations from the Sensitive Actions Service. They are also available in Cloud Logging, where we recommend that customers integrate them into their monitoring workflows. Sensitive Actions include the following list of action names (mapped to the MITRE ATT&CK tactics that these actions may correspond to) and descriptions: To ensure that adversaries do not have mechanisms to disable this protection or hide logs from users, Sensitive Actions is an on-by-default service now enabled for Cloud customers. In cases where customers have certain privacy controls or policy restrictions applied to their logging pipeline, their logs will not be analyzed by this service.You can learn more about Sensitive Actions and our overall recommendations for keeping your account secure by visiting our documentation here.
Quelle: Google Cloud Platform