Kategorie: Google Cloud Platform

Cloud CISO Perspectives: February 2022

As the war in Ukraine continues to unfold, I want to update you on how we’re supporting our customers and partners during this time. Google is taking a number of actions. Our security teams are actively monitoring developments, and we offer a host of security products and services designed to keep customers and partners safe from attacks. We have published security checklists for small businesses and medium-to-large enterprises, to enable entities to take necessary steps to promote resilience to malicious cyber activity.Below, I’ll recap the latest efforts from the Google Cybersecurity Action Team such as our second Threat Horizons Report, and highlight new capabilities from our cloud security product teams who have been working to deliver new controls, security solutions and more to earn the trust of our customers globally. Munich Cyber Security ConferenceEarlier this month, I joined a panel at the Munich Cyber Security Conference (Digital Edition) to discuss supply chain risks and cyber resiliency. It was great to see a packed agenda featuring diverse voices from the security industry along with government leaders and policymakers coming together to discuss the challenges we’re working to collectively solve in cybersecurity. One area of particular focus is securing the software supply chain. During the panel, we talked about Google’s approach to building our own internal software and incorporating open source code in a secure way. This has been the foundation of our BeyondProd approach.We implement multiple layers of safeguards like multi-party change controls and a hardened build process that produces digitally signed software that our infrastructure explicitly validates before executing. We’ve since turned this into an open framework that all organizations can use to assess themselves and their supply chains: SLSA. How we collectively as an industry secure the software supply chain and prevent vulnerabilities in open source software will continue to be critical for cloud and SaaS providers, governments and maintainers throughout 2022. Google Cloud Security Talks  On March 9, we’ll host our first Cloud Security Talks of 2022 that will focus on how enterprises can modernize their approach to threat detection and response with Google Cloud. Sessions will highlight how SecOps teams can leverage our threat detection, investigation and response capabilities across on-premise, cloud, and hybrid environments, including new SOAR capabilities from our recent acquisition of Siemplify. Registerhere. Google Cybersecurity Action Team Highlights Here are the latest updates, products, services and resources from our cloud security teams this month: Security FIDO security key support for GCE VMs: Physical security keys can now be used to authenticate to Google Compute Engine virtual machine (VM) instances that use our OS Login service for SSH management. Security keys offer some of the strongest protection against phishing and account takeovers and are strongly recommended in administrative workflows like this.IAM Conditions and Tags support in Cloud SQL: We introduced IAM Conditions and Tags in Cloud SQL which bring powerful new capabilities for finer-grained administrative and connection access control for Cloud SQL instances. Achieving Autonomic Security Operations: Anton Chuvakin and Iman Ghanizada from the Cybersecurity Action Team shared their latest blog post on how organizations can achieve Autonomic Security Operations by leveraging key learnings from SRE principles. The post highlights multiple ways automation can serve as a force multiplier to achieve better outcomes in your SOC.Certificate Manager integration with External HTTPS Load Balancing: We released the public preview of our Certificate Manager service and integration with External HTTPS Load Balancing to help simplify the way you deploy HTTPS services for your customers. You can bring your own TLS certificates and keys if you have an existing certificate lifecycle management solution or use Google Cloud’s fully managed TLS offerings. Another helpful feature of this release is integration of alerts on certificate expiry into Cloud Logging.Virtual Machine Threat Detection: The cloud is impacted by unique threat vectors but also offers novel opportunities to build effective detection into the platform natively. This dynamic underpins our latest Security Command Center Premium capability: Virtual Machine Threat Detection (VMTD). VMTD helps ensure strong protection for VM-based workloads by providing agentless memory scanning that can detect threats like cryptomining malware inside your Google Compute Engine VMs.Chrome Browser Cloud Management: A large part of enterprise security is protecting endpoints that access the web overall and a big part of this is not only using a secure browser like Chrome, but also how you get to manage and support that. We have a lot of these capabilities in Chrome Browser Cloud Management along with our overall zero trust approach. We also recently extended CIS benchmark coverage to include Chrome. Google Cloud architecture diagramming tool: We recently launched the brand new Google Cloud Architecture Diagramming Tool. This is an awesome tool for cloud architects, developers and security teams alike, and it’s another opportunity for us to be helpful in providing pre-baked reference architectures into the tools. Watch out for more on this as we build in more security patterns. Some of the Best Security Tools Might Not be “Security Tools”: Remember, there are many problems in risk management, security and compliance that don’t need specialist security tools. In fact some of the best tools might be from our data analysis and AI stacks such as our Vertex AI capability. Check out these new training features from the team. Stopping website attacks with reCAPTCHA Enterprise: reCAPTHA Enterprise is a great solution that mitigates many of the issues in the OWASP Automated Threat Handbook and can be deployed seamlessly for your website. Industry updatesOpen source software security: Just a few weeks after technology companies (including Google) and industry foundations convened at the White House summit on open source security, the OpenSSFannounced the Alpha-Omega project. The project aims to help improve software supply chain security for 10,000 OSS projects through direct engagement of software security experts and automated testing. Microsoft and Google are supporting the Alpha-Omega Project with an initial investment of $5 million. Building cybersecurity resilience in healthcare: Taylor Lehmann and Seth Rosenblatt from Google’s Cybersecurity Action team recently outlined best practices healthcare leaders can adopt to build resilience for IT systems, overcome attacks to improve both security and business outcomes, and above all, protect patient care and data.  Threat IntelligenceThreat Horizons Report Issue 2: Providing timely, actionable cloud threat intelligence to our customers so they can take action to protect their environments is critical and this is the aim of our Threat Horizons report series. Customers benefit from guidance on how to securely use and configure the cloud, which is why we operate within a “shared fate” model that exemplifies a true partnership with our customers regarding their security outcomes. In the latest Google Cybersecurity Action Team Threat Horizons Report, we observed vulnerable instances of Apache Log4j are still being sought by attackers, which requires continued vigilance by customers and cloud providers alike in ensuring patching is effective. Additionally, Google Cloud Threat Intelligence has observed that the Sliver framework is being used by adversaries post initial compromise in attempts to ensure they maintain access to networks. Check out the fullreport for this month’s findings and best practices you can adopt to stay protected against these and other evolving threats.ControlsAssured Workloads for EU: Organizations around the world need confidence they can meet their unique and evolving needs for security, privacy, and digital sovereignty as they use cloud services. Assured Workloads for EU, now GA, allows GCP customers to create and maintain workloads with data residency in their choice of EU Google Cloud regions, personnel access and customer support restricted to EU persons located in the EU, and cryptographic control over data access using encryption keys stored outside Google Cloud infrastructure.Client Authorization for gRPC Services with Traffic Director: One way developers use the open source gRPC framework is for backend service-to-service communications. The latest release of Traffic Director now supports client authorization by proxyless gRPC services. This release, in conjunction with Traffic Director’s capability for managing mTLS credentials for Google Kubernetes Engine (GKE) enables customers to centrally manage access between workloads using Traffic Director.Don’t forget to sign-up for our newsletter if you’d like to have our Cloud CISO Perspectives post delivered every month to your inbox. We’ll be back next month with more updates and security-related news.Related ArticleCloud CISO Perspectives: January 2022Google Cloud CISO Phil Venables shares his thoughts on the latest security updates from the Google Cybersecurity Action Team.Read Article
Quelle: Google Cloud Platform

Four ways Google Cloud Marketplace simplifies buying cloud software

Discovering, purchasing, and managing solutions on Google Cloud has never been easier thanks to Google Cloud Marketplace, where you can explore thousands of enterprise-ready products and services that integrate with Google Cloud. Marketplace simplifies buying solutions from Google and top software vendors as all purchases are seamlessly added to your existing Google Cloud invoice, so you receive just one bill from Google. We’re continuously improving how our customers can evaluate, procure, and manage cloud software online because we know that’s increasingly how you want to buy. In fact, in 2021 our marketplace third-party transaction value was up more than 500% YoY from 2020 (Q1-Q3). Let’s revisit the top four improvements we’ve made to the cloud software buying and selling experience in just the last few months that have accelerated buying momentum.1. Find what you’re looking for fasterWith enhanced filters, quickly discover top solutions ready to run in your Google Cloud instance. Filters are now more intuitive, allowing you to browse solutions by industry, type, category, use case, and more. Check out the “free trial” filter if you want to try a solution before buying. And if you’re looking for inspiration, we surface popular, new, and featured products across categories. Once you’ve found what you’re looking for, you’ll benefit from more detailed product cards to help you evaluate what’s best for your organization. Explore the Google Cloud Marketplace catalog to find innovative solutions to your business problems.2. Buy and sell the way you wantWe’ve added new subscription models and payment schedules, making it simpler than ever to save money and meet your organization’s procurement needs. Through Google Cloud Marketplace, you can negotiate with third-party vendors and retire Google Cloud committed spend on first and third-party purchases. Many of our software vendors now have the ability to offer flexible subscription models, including flat fees, usage-based, hybrid flat fee + usage, and committed use discounts that customers can pay for monthly or up front. These partners can now also include standard or customized terms to meet your organization’s procurement needs. Learn more about initiating and accepting customized offers. And to make buying easier in large organizations, billing admins can now set up a procurement governance workflow that allows end-users to submit procurement requests for new SaaS products directly from Google Cloud Marketplace.For the many software vendors accelerating growth via the Google Cloud Marketplace, in addition to these new pricing tools and the partner investments we announced last month, we’ve also broadened migration to Producer Portal, our new offer publishing, deal-making, and analytics portal. This new experience makes many of the new pricing features possible. Plus we’ve improved partner disbursements to set you up for success as your marketplace deal flow grows – all in pursuit of being the easiest cloud to go-to-market with. If you’re interested in selling your enterprise-grade solution to Google Cloud customers, here’s how to get started.3. Manage purchases convenientlyAfter purchasing a great solution, you’ve got to set up and manage it. We’ve also made a few improvements to speed up your post-purchase time-to-value: The new Your Orders page provides a unified experience where you can view and manage all third-party subscription purchases under a single billing account. Your Products helps developers easily find, access, and receive relevant updates for the products that they used or are available to them within their project so they always have them at their fingertips.Plus we’ve simplified SaaS setup for admins with the Service Account Provisioning UI. It used to be that the software vendor had to manually share service account details with you, and someone on your end needed to provide access via command-line interface (CLI). We’ve made this easier: software vendors can now get the access their product needs with just a few clicks. And if you’re a SaaS vendor looking to speed up customer onboarding, read how you can incorporate this feature into your product. 4. Maintain control over your solutions – easilyOnce customers build or buy, establishing and enforcing standards and controls across your cloud landscape can be a headache, especially in large organizations. Luckily, Google Cloud Marketplace has Service Catalog functionality built-in to make teams more productive by helping you efficiently distribute approved solution configurations across your organization. Enforcing governance policies in-console with Service Catalog not only simplifies compliance, but it also saves engineering time by reducing manual configuration steps and post-build reviews. And now with the ability to publish Terraform configurations, Service Catalog allows for a consistent, flexible approach that reduces the need for organizations to learn multiple infrastructure-as-code (IaC) solutions. Ready to get started? Be up-and-running fast with our Service Catalog quickstart guide.We know you’ll love these enhancements, and we’re not slowing down. We can’t wait to share more about what we’re working on soon.Related ArticleGoogle Cloud doubles-down on ecosystem in 2022 to meet customer demandGoogle Cloud will double spend in its partner ecosystem over the next few years, including new benefits, incentives, programs, and training.Read Article
Quelle: Google Cloud Platform

Developing high-quality ML solutions

When a deployed ML model produces poor predictions, it can be due to a wide range of problems. It can be the result of bugs that are typical in any program—but it can also be the result of ML-specific problems. Perhaps data skews and anomalies are causing model performance to degrade over time. Or the data format is inconsistent between the model’s native interface and the serving API. If  models aren’t monitored, they can fail silently. When a model is embedded into an application, issues like this can create poor user experiences. If the model is part of an internal process, the issues can negatively impact business decision-making. Software engineering has many processes, tools, and practices to ensure software quality, all of which help make sure that the software is working in production as intended. These tools include software testing, verification and validation, and logging and monitoring. In ML systems, the tasks of building, deploying, and operating the systems present additional challenges that require additional processes and practices. Not only are ML systems particularly data-dependent because they inform decision-making from data automatically, but they’re also dual training-serving systems. This duality can result in training-serving skew. ML systems are also prone to staleness in automated decision-making systems.These additional challenges mean that you need different kinds of testing and monitoring for ML models and systems than you do for other software systems—during development, during deployment, and in production. Based on our work with customers, we’ve created a comprehensive collection of guidelines for each process in the MLOps lifecycle. The guidelines cover how to assess, ensure, and control the quality of your ML solutions. We’ve published this complete set of guidelines on the Google Cloud site. To give you an idea of what you can learn, here’s a summary of what the guidelines cover:Model development: These guidelines are about building an effective ML model for the task at hand by applying relevant data preprocessing, model evaluation, and model testing and debugging techniques. Training pipeline deployment: These guidelines discuss ways to implement a CI/CD routine that automates the unit tests for model functions and the integration tests of the training pipeline components. The guidelines also help you apply an appropriate progressive delivery strategy for deploying the training pipeline.Continuous training: These guidelines provide recommendations for extending your automated training workflows with steps that validate the new input data for training, and that validate the new output model that’s produced after training. The guidelines also suggest ways to track the metadata and the artifacts that are generated during the training process.Model deployment: These guidelines address how to implement a CI/CD routine that automates the process of validating compatibility of the model and its dependencies with the target deployment infrastructure. These recommendations also cover how to test the deployed model service and how to apply progressive delivery and online experimentation strategies to decide on a model’s effectiveness.Model serving: These guidelines concern ways to monitor the deployment model throughout its prediction serving lifetime to check for performance degradation and dataset drift. They also provide suggestions for monitoring the efficiency of model service.Model governance: These guidelines concern setting model quality standards. They also cover techniques for implementing procedures and workflows to review and approve models for production deployment, as well as managing the deployed model in production.To read the full list of our recommendations, read the document Guidelines for developing high-quality ML solutions.Acknowledgements: Thanks to Jarek Kazmierczak, Renato Leite, Lak Lakshmanan, and Etsuji Nakai for their valuable contributions to the guide.
Quelle: Google Cloud Platform

Google data experts share top data practitioner skills needed in 2022

It’s 2022 and nanosatellites, NFTs, and autonomous cars that deliver your pizza are in full force. In a world where people rely on simple technology to untangle complex problems, companies must deliver simple experiences to be successful in today’s landscape. For many cloud providers this means enabling tightly integrated data offerings that simplify the data delivery process without losing sight of the sophisticated needs of the modern data consumer. But while the name of the game is helping companies reach informed decisions from their data simpler and faster, what about the data practitioners – data analysts, data engineers, database administrators, developers, etc – who use these cloud data tools and technologies everyday? To proactively stay ahead of data cloud market trends in 2022 should data practitioners invest their time in specializing their data cloud skill sets (e.g. go deep in, say, data pipelining skills) or instead invest their time generalizing their data cloud skill sets (e.g. growing proficiencies in a mix of data analytics, databases, AI/ML, and more domains)? Skill deep or wide with data – that is the questionFor Abdul Razack, VP, Solutions Engineering, Technology Solutions and Strategy at Google Cloud, the answer is a bit of both. “Data practitioners need to be broad in terms of their technology skills, but specialized with respect to the domain or domains in which they apply them. The reason why is because many things that used to be separate skill sets are now converging – like business analytics, streaming, machine learning, data pipelines, and data warehousing. Data practitioners need to be able to implement end-to-end workflows that solve specific business problems using skills from each category.”It’s true, thousands of customers are choosing Google’s data cloud because it offers a unified and open approach to cloud that enables their practitioners to break down silos, begin and end projects without leaving the data platform, and innovate faster across their organization. The data practitioners who mirror Google data cloud’s frame of mind of being smart and agile across data domains in their skilling and learning will reap the benefits of solving more nuanced problems – building out internet-scale applications, fine tuning smart processes with analytics and AI, constructing data meshes that make product building simple, etc – at a larger scale than they would if they specialized in just one or two areas alone.Of course at the end of the day it depends on what tools a data practitioner is using to complete their workflows. There’s only so much you can learn and skills you can develop when you’re using limited tools.“Of course at the end of the day it depends on what tools a data practitioner is using to complete their workflows. There’s only so much you can learn and skills you can develop when you’re using limited tools. Growing data proficiencies across the board is made a lot easier when you’re using a data platform like BigQuery to address all these needs. BigQuery eliminates the choices you have to make – for instance you don’t have to choose between streaming data and data at rest, batch and realtime, or business intelligence and data science. This freedom gives data professionals a huge advantage when they’re building their skill sets and taking on more complex projects.” -Abdul Razack – VP, Solutions Engineering, Technology Solutions and Strategy, Google CloudKnowing your value is half the battle when upskillingWhile some experts think technology is the limiting factor of whether or not you can even go wide or go deep in the first place, others like Google Cloud’s Head of Data and Analytics Bruno Aziza purport that it also depends on who you are, who you wish to be, and what investments your company is making to ensure you can become that person. “If you wish to set yourself up to be a Chief Data Officer, then you’ll want to understand how technologies fit together across your data estate first” said Aziza. “Only after you feel like you’re the go-to ‘data person’ can you then decide which part of the technology stack you want to double-down on.”Only after you feel like you’re the go-to ‘data person’ can you then decide which part of the technology stack you want to double-down on.But technology isn’t everything. Aziza notes, “Make sure you focus on the  business impact that your data work provides.  You want to spend as much time as you can with your business counterparts to understand their business goals and challenges. The Harvard Business Review provides great guidance on how to succeed as a Chief Data Officer.”Even if you don’t have your sights set on a C-suite role, both Aziza and Razack contend that the number one skill data practitioners should tackle in 2022 is actually a broad and perhaps abstract one: develop and exercise the curiosity to solve problems with a data-driven strategy. That is, today’s data practitioners should always be interested in educating themselves in the industry and continually upskilling in something. And their employers should also be invested in helping practitioners develop those interests, most likely through exposure to learning materials, engaging in career conversations, subsidized courses, or incentives attached to pursuing a new certification or skill.  “Every industry is going through a digital transformation and the ability to identify what data to collect, how to prepare the data, and how to derive insights from it is critical. Therefore, the ability to find business challenges and formulate a data-driven approach to address those problems is the most important skill to have.” Abdul Razack – VP, Solutions Engineering, Technology Solutions and Strategy, Google CloudWhether you’re a data engineer, data analyst, citizen data scientist, or data practitioner by any other name, asking more questions and being curious to learn more should be that thing that you gravitate towards in those spare moments…Be a constant learner. New concepts pop up all the time and you want to be the person who can learn the fastest so you can advance your company’s mission and contribute back to the community.Take the example of the “Data Mesh” I just wrote about in VentureBeat.  You’ll find 3 types of attitudes towards this new concept. There are Disciples who encourage continued learning only from the source – like the author of a new book or the creator of a theory. There are Distractors who tell you that new skills, trends, and technologies are fake news. And there are Distorters like vendors who will sell you one easy fix solution. But it’s the data practitioner who needs to proceed with caution when interacting with all three types  and forge their own path to discovering the truth when they’re learning and building skills. And for better or worse, this comes with trial and error, experimentation, and an eagerness to grow relative to where they began.”Ready to start data upskilling? Start here.For those interested in keeping up their data curiosities, check out our Data Journeys video series. Each week Bruno Aziza investigates a new authentic customer’s data journey – from migrating to cloud or  building a data platform to carrying out new data for good initiatives. Learn how they did it, their data dos and don’ts, and what’s next for them on their journey. These videos include a flavor of both specializing your data competencies and broadening your data competencies. For those interested in deepskilling, connect with Google’s data community at our upcoming virtual event: Latest Google Cloud data analytics innovations. Register and save your spot now to get your data questions answered live by GCP’s top data leaders and watch demos from our latest products and features including BigQuery, Dataproc, Dataplex, Dataflow, and more.If you have any questions or need support along your learning journey – we’re here for you! Sign up to be a Google Cloud Innovator, and join the Google Cloud Data Analytics Community.Related ArticleThe top three insights we learned from data analytics customers in 2021Google Cloud announces the top data analytics stories from 2021 including the top three trends and lessons they learned from customers th…Read Article
Quelle: Google Cloud Platform

Cloud Spanner myths busted

Intro to Cloud SpannerCloud Spanner is an enterprise-grade, globally distributed, externally consistent database that offers unlimited scalability and industry-leading 99.999% availability. It requires no maintenance windows and offers a familiar PostgreSQL interface. It combines the benefits of relational databases with the unmatched scalability and availability of non-relational databases. As organizations modernize and simplify their tech stack, Spanner provides a unique opportunity to transform the way they think about and use databases as part of building new applications and customer experiences.But choosing a database for your workload can be challenging; there are so many options in the market and each one has a different onboarding and operating experience. At Google Cloud we know it’s hard to navigate this choice and are here to help you. In this blog post, I want to bust the seven most common misconceptions that I regularly hear about Spanner so that you can confidently make your decision.Myth #1 Only use Spanner if you have a massive workloadThe truth is that Spanner powers Google’s most popular, globally available products, like YouTube, Drive, and Gmail, and has enabled many large scale transformations including that of Uber, Niantic and Sharechat. It is also true that Spanner processes more than 1 Billion queries per second at peak.At the same time, many customers also use Spanner for their smaller workloads (both in terms of transactions per second and storage size) for availability and scalability reasons. For example, Google Password Manager has small workloads that run on Spanner. These customers cannot tolerate downtime, require high availability to power their applications and seek scale insurance for future growth scenarios.Limitless scalability with the highest availability is critical in many industry verticals such as gaming and retail, especially when a newly launched game goes viral and becomes an overnight success or when a retailer has to handle a sudden surge in traffic due to a  Black Friday/Cyber Monday sale.  Regardless of workload size, every customer on the journey to the cloud wants the benefits of scalability and availability while reducing the operational burden and the costs associated with patching, upgrades and other maintenance.Myth #2 Spanner is too expensiveThe truth is, when looking at the cost of a database, it is better to consider Total Cost of Ownership (TCO) and the value it offers rather than the raw list price. We deliver significant value to our customers starting at this price including critical things like availability, price performance, and reduced operational costs. Availability: Spanner provides high availability and reliability by synchronously replicating data. When it comes to Disaster Recovery, Spanner offers 0-RPO and 0-RTO for zonal failures in case of a regional instance and regional failure in case of multi-regional instances. Less downtime, more revenue!Price-performance: Spanner offers one of the industry’s leading price-performance ratios which makes it a great choice if you are running a demanding, performance sensitive application. Great customer experiences require consistent, optimal latencies!Reduced operational cost: With Spanner, customers enjoy zero downtime upgrades and schema changes, and no maintenance windows. Sharding is automatically handled so the challenges associated with scaling up traditional databases don’t exist. Spend more time innovating, and less time administering!Security & Compliance: By default, Spanner already offers encryption for data-in-transit via its client libraries and for data-at-rest using Google-managed encryption keys. CMEK support for Spanner lets you now have complete control of the encryption keys. Spanner also provides VPC Service Controls support and has compliance certifications and necessary approvals so that it can be used for workloads requiring ISO 27001, 27017, 27018, PCI DSS, SOC1|2|3, HIPAA and FedRAMP.With Spanner, you have peace of mind knowing that your data’s security, availability and reliability won’t be compromised.And best of all, with the introduction of Granular Instance Sizing, you can now get started for as little as $65/month and unlock the tremendous value spanner offers.Pro tip : Use the auto-scaler to right size your Spanner instances. Take advantage of TTL to reduce the amount of data stored.Myth #3 You have to make a trade off between scale, consistency, and latencyThe truth is, depending on the use case and instance configuration, users can use Spanner such that they don’t have to pick between consistency, latency and scale.To provide strong data consistency, Spanner uses a synchronous, Paxos-based replication scheme, in which replicas acknowledge every write request. A write is committed when a majority of the replicas (e.g 2 out of 3), called a quorum, agree to commit the write. In the case of regional instances, the replicas are within the region and hence the writes are faster than in the case of multi-region instances, where the replicas are distributed across multiple regions. In the latter case, forming a quorum on writes can result in slightly higher latency. Nevertheless, Spanner multi-regions are carefully designed in geographical configurations that ensure that the replicas can communicate fast enough and write latencies are acceptably low.A read can be served strong (by default) or stale. A strong read is a read at a current timestamp and is guaranteed to see all the data that has been committed up until the start of the read. A stale read is a read executed at a timestamp in the past. In case of a strong read, the serving replica ​​will guarantee that you will see all data that has been committed up until the start of the read. In some cases, this means that the serving replica has to contact the leader to ensure that it has the latest data. In case of a multi-region instance where the read is served from a non-leader replica, this would mean that read latency can be slightly higher than if it was served from a leader region. Stale reads are performed over data that was committed at a  timestamp in the past and can, therefore, be served at very low latencies by the closest replica that is caught up until that timestamp. If your application is latency sensitive, stale reads may be a good option and we recommend using a stale read value of 15 seconds. Myth #4 Spanner does not have a familiar interfaceThe truth is that Spanner offers the flexibility to interact with the database via a SQL dialect based on ANSI 2011 standard as well as via a REST or gRPC API interface, which are optimized for performance and ease-of-use. In addition to Spanner’s interface, we recently introduced a PostgreSQL interface for Spanner, that leverages the ubiquity of PostgreSQL to meet development teams using an interface that they are familiar with. The PostgreSQL interface provides a rich subset of the open-source PostgreSQL SQL dialect, including common query syntax, functions, and operators. It supports a core collection of open-source PostgreSQL data types, DDL syntax, and information schema views. You get the PostgreSQL familiarity, and relational semantics at Spanner scale. Learn more about our PostgreSQL interface here.Myth #5 The only way to get observability data is via the Spanner Console​​The truth is that Spanner client libraries support OpenCensus Tracing and Metrics, which gives insight into the client internals and aids in debugging production issues. For instance, client-side traces and metrics include sessions and transactions related information. Spanner also supports the OpenTelemetery receiver, which provides an easy way for you to process and visualize metrics from Cloud Spanner System tables, and export these to the Application Monitoring (APM) tool of your choice. This could be either an open source combination of a time-series database like Prometheus coupled with a Grafana dashboard, or it could be a commercial offering like Splunk, Datadog, Dynatrace, NewRelic or AppDynamics. We’ve also published reference Grafana dashboards, so that you can debug the most common user journeys such as “Why is my tail latency high” or “Why do I see a CPU spike when my workload did not change”. Here is a sample docker service, to show how the Cloud Spanner receiver can work with Prometheus exporter and Grafana dashboards.We are continuing to embrace open standards, and continuing to integrate with our partner ecosystem. We also continue to evolve the observability experience offered by the Google console so that our customers get the best experience wherever they are. Myth #6 Spanner is only for global workloads requiring copies in multiple regions The truth is that, while Spanner offers a range of multi-region instance configurations, it also offers regional configuration in each GCP region. Each regional node is replicated in 3 zones within the region, while a multi-regional node is replicated at least 5 times across multiple regions. A regional configuration offers 4 nines of availability and protection against zonal failures.Typically, multi-regional instance configurations are indicated if your application runs workloads in multiple geographical locations or your business needs 99.999% of availability and protection against regional failures. Learn more here.Myth #7 Spanner schema changes require expensive locksThe truth is that Spanner never has table level locks. Spanner uses a multi-version concurrency control architecture to manage concurrent versions of schema and  data allowing ad-hoc and online qualified schema changes that do not require any downtime, additional tools, migration pipelines or complex rollback/backup plans. When issuing a schema update you can continue writing and reading from the database without interruption while Spanner backfills the update, whether you have 10 rows or 10 billon rows in your table.The same mechanism can be used for Point-in-time recovery (PITR) and snapshot queries using stale reads to restore both schema and the state of data at a given query-condition and timestamp up to a maximum of seven days.Now that we’ve learned the truth about Cloud Spanner, I invite you to get started – visit our website.Related ArticleImproved troubleshooting with Cloud Spanner introspection capabilitiesCloud-native database Spanner has new introspection capabilities to monitor database performance and optimize application efficiency.Read Article
Quelle: Google Cloud Platform

Announcing Google Cloud 2022 Summits [frequently updated]

Register for our 2022 Google Cloud Summit series, and be among the first to learn about new solutions across data, machine learning, collaboration, security, sustainability, and more. You’ll hear from experts, explore customer perspectives, engage with interactive demos, and gain valuable insights to help you accelerate your business transformation. Bookmark the Google Cloud Summit series website to easily find updates as news develops. Can’t join us for a live broadcast? You can still register to enjoy all summit content, which becomes available for on-demand viewing immediately following each event. Upcoming eventsData Cloud Summit | April 6, 2022Mark your calendars for the Google Data Cloud Summit, April 6, 2022. Join us to explore the latest innovations in AI, machine learning, analytics, databases, and more. Learn how organizations are using a simple, unified, open approach with Google Cloud to make smarter decisions and solve their most complex business challenges.At the event, you will gain insights that can help move you and your organization forward. From our opening keynote to customer spotlights to sessions, you’ll have the chance to uncover up-to-the-minute insights on how to make the most of your data.Equip yourself with the technology, the confidence, and the experience to capitalize on the next wave of data solutions. Register today for the 2022 Google Data Cloud Summit.Related ArticleRead Article
Quelle: Google Cloud Platform