Juni 2021 - Seite 21 von 55 - Cloud Computing Köln

As your organization evolves, the cloud can be a powerful tool to drive growth, improve efficiency, and reduce costs. In fact, the cloud is so powerful that most organizations find themselves running on multiple clouds—a full 92% of enterprises surveyed by Flexera1 reported adopting a deliberate multicloud strategy in some way, shape, or form: on one or more public clouds, on-premises data centers (or private clouds)—and let’s not forget the edge locations. Multicloud, in short, is very much the reality for today’s enterprises. The problem is that cloud platforms each come with their own proprietary approach to management. This creates inconsistency in operations, making it hard and more expensive to maintain security and compliance across environments. It also hampers developer productivity, places a strain on precious talent, and adds to overall costs. Can you get the advantages of using distinct cloud providers while minimizing the complexity and cost?Have no fear–there’s good news. Running on multiple clouds doesn’t have to be hard. It doesn’t have to be expensive. It doesn’t have to be a burden on your operations teams. Done right, you can go from running on multiple clouds out of necessity, to turning those multicloud assets into a net positive for your developers, your platform administrators, and your organization’s bottom line. To show you how, first, let’s recap why organizations tend to find themselves running on multiple clouds. Then, we’ll walk through a few ways Google is bringing cloud services like Anthos, Looker, BigQuery Omni, Apigee API management, and others to multicloud environments in way that positions your organization to take advantage of everything the cloud has to offer. Why do organizations run on multiple clouds?For many organizations, working with multiple cloud providers is about selecting best-of-breed capabilities. For example, one provider may have broader compute options, another may specialize in data analytics and AI services, and another may support legacy environments. The decision to run on multiple clouds is often made in the boardroom. Sometimes it’s a business decision to avoid cloud provider lock-in, to comply with regulations that aim to avoid over-reliance on a single cloud infrastructure, or to satisfy geography-specific consumer protection laws e.g., GDPR, the California Consumer Privacy Act and GAIA-X. Companies also find themselves running on multiple clouds over time, say, as the result of an acquisition. Faced with workloads that are already running effectively on the non-preferred cloud, organizations sometimes come to the conclusion that they are not worth re-platforming.Regardless of the road you took to running on multiple clouds, you need to make it work well, minimizing complexity, keeping costs low, and enabling staff, rather than creating extra work for them. You want a platform to simplify and enhance your multicloud assets, and you need tools that are multicloud-ready. Here are just a few of the ways Google can help you succeed in your multicloud journey.From containers to a modern open cloudWhen operating in multiple cloud environments, organizations often start by looking for consistency and portability, turning to containers to re-package their workloads into a portable format that can run on multiple clouds, while standardizing skills and processes for platform teams. Google created Kubernetes to manage large fleets of our own containers, and open sourced it to help others achieve the same. Then, to make it easier for organizations to run Kubernetes, we created Google Kubernetes Engine (GKE), a reliable, secure and fully managed service. A few years later we introduced Anthos, a secure managed platform designed to simplify the management of Kubernetes clusters on any public or private cloud by extending a GKE-like experience along with our best open-source frameworks, with a Google Cloud-backed control plane for consistent management of services in distributed environments.Today, multicloud organizations can leverage our full open cloud approach, which uses open-source technologies to let them deploy—and, if desired, migrate—critical workloads running on both VMs and containers and reimagine them in a modern microservices-based architecture. Anthos can also help you to leverage consistent Google Cloud services in other clouds. For example, we introduced Apigee hybrid to give you the flexibility to deploy API runtimes in a hybrid environment while using cloud-based Apigee capabilities such as developer portals, API monitoring and analytics. Apigee hybrid exposes trusted data residing across clouds through APIs to support faster app builds. We also brought hybrid AI capabilities to Anthos, designed to let you use our differentiated AI technologies wherever your workloads reside. By bringing AI on-prem, you can now run your AI workloads near your data, all while keeping them safe. In addition, hybrid AI simplifies the development process by providing easy access to best-in-class AI technology on-prem. The first of our hybrid AI offerings, Speech-to-Text On-Prem, is now generally available on Anthos through the Google Cloud Marketplace, and going forward, we are committed to bringing additional Google Cloud services, development tooling, and engineering practices to other environments for a truly consistent multicloud experience.Uncover new insights with a multicloud data analytics platformIf you want to make the best decisions for your business, you need access to your data and the ability to quickly derive insights from it, often in real time. That doesn’t change when your data is in multiple clouds. Unfortunately, the cost of moving data between cloud providers isn’t sustainable for many businesses, and it’s still difficult to analyze and act on data across clouds. We want you to be able to take advantage of our analytics, artificial intelligence, and machine learning capabilities regardless of where your data resides. A data cloud allows you to securely unify data across your entire organization, so you can break down silos, increase agility, innovate faster, get more value from your data, and support business transformation.To better serve customers across multiple environments, last year we launched BigQuery Omni, a new way of analyzing data stored in multiple public clouds that’s made possible by BigQuery’s separation of compute and storage. While competitors require you to move or copy your data from one public cloud to another—and charge high egress fees in the process—BigQuery Omni does not. And because BigQuery Omni is powered by Anthos, you can query data without having to manage the underlying infrastructure. With BigQuery Omni for Azure, now in public preview, we’re enabling more organizations to analyze data across public clouds from a single pane of glass. This, along with BigQuery Omni for AWS, helps customers access and securely analyze data across Google Cloud, AWS, and Azure.Then there’s Looker, a unified business intelligence and embedded analytics platform across your multicloud ecosystem. Looker’s in-database architecture supports a wide range of databases and SQL dialects. Using Looker, you can directly query data stored across multiple clouds to deliver governed real-time data at web scale where and when it’s needed, whether that’s through BI reports and dashboards, embedded analytics, automated data-driven workflows or completely custom data app experiences. This is why we’re excited to announce the continued expansion of Looker’s multicloud support, now including Looker hosted on Azure and support for more than 60 distinct database dialects. Now, you can host your Looker instance on the leading cloud provider of your choice: Google Cloud, AWS or Azure. Build cloud-native apps across clouds, at scaleIn an ideal world, development teams would not need to worry about the details of their specific platforms. They could modernize their existing apps, build cloud-native microservices, and deploy to any cloud platform for consistent service delivery anywhere. Additionally, they’d be able to manage all their clusters with a single pane of glass from the infrastructure layer through to service performance and topology—all in a uniform way. For many organizations, multicloud is only worth it if it can effectively address these needs. Anthos gives you the ability to run Kubernetes anywhere: private clouds and Google Cloud, but also Azure, and AWS. And no matter where you are running, Google Cloud’s suite of development tools are able to seamlessly integrate into your environment, making it easier for developers and operators to build, deploy and manage applications. For example, developers can write Kubernetes applications within their preferred IDE with Cloud Code.Secure your apps and data wherever they are When you’re running in several environments, you really can’t overlook security. Google Cloud solutions aim to secure everything in your multicloud environment, from the user to the network to the app to your data. We also provide threat detection and investigation across these surfaces, even for organizations that do not run their systems in our cloud. Our trusted cloud enables your digital transformation while also supporting your risk, security, compliance and privacy transformation. Our platform also delivers transparency and ensures digital sovereignty across data, operations, and software. We provide a secure foundation that you can verify and independently control, enabling you to move from your own data centers to the cloud while maintaining control over data location and operations—all while ensuring compliance with local regulations. Get started on your multicloud journeySome cloud providers dismiss customers who see multicloud as their path forward and don’t offer their cloud services where the customer needs them to be. That’s not our approach. Our goal is to support you regardless of where your data resides or where your applications run. If you’re ready to take your cloud deployment to the next level, check out our whitepaper, 5 ways Google can help you succeed in a hybrid and multicloud world. Or reach out to us and see if Google Cloud multicloud technologies can be what takes you there.1. Gartner Research
Quelle: Google Cloud Platform

18. Juni 2021

da Agency

New training helps you get started with GKE—for free!

Kubernetes adoption continues to grow unabated across all industries. This fall, 91% of IT and security professionals reported that their businesses are using Kubernetes for container orchestration. Now, technical professionals are faced with figuring out how to make the most of Kubernetes.For many businesses, a cloud provider’s managed Kubernetes service offers a more convenient on-ramp than going it alone with an open-source distribution. Google Kubernetes Engine (GKE) provides easier “one-click” cluster deployment, as well as the convenience of utilizing the assortment of products and tooling available in the cloud. Training and documentation available from Google Cloud can also help ease the transition as companies go through their modernization journeys. But there’s simply a lot to learn, and it can be tough to know where to begin.On June 22, Google Cloud will offer a no-cost, half-day training, Cloud OnBoard: Getting Started with Google Kubernetes Engine. In this training event, I’ll walk you through what you need to get started with adopting and managing GKE. The event will have four main sections: Introduction to Building with Kubernetes, Create and Configure GKE Clusters, Deploy and Scale in Kubernetes, and Securing GKE for Your Google Cloud Platform Access. Below is a brief overview of the topics you need to know about when learning how to use GKE as well as what you can expect from the Cloud OnBoard:Why Kubernetes?The first section, Introduction to Building with Kubernetes, we’ll go over the benefits of building with Kubernetes for your business. It will explore the challenges businesses face when modernizing their applications and when adopting cloud-native technologies and architectures such as microservices.How to create GKE clustersIn the second section, Create and Configure GKE Clusters, we’ll begin with the basics. I’ll demonstrate how to spin up a cluster using the gcloud command-line interface (CLI) and go into the capabilities of the Cloud SDK. Then, we’ll cover configuration and cluster management basics. This section will also cover the differences between GKE Standard mode and the new GKE Autopilot mode. We’ll also explore an example of a company that might choose GKE Autopilot mode for their needs.Running apps and services in GKEIn the third section, Deploy and Scale Apps in Kubernetes, we’ll go over general basics around running workloads in Kubernetes. You’ll learn the essentials you’ll need for deploying and scaling your applications. We’ll explore Kubernetes workload and service types, workload autoscaling capabilities, and how to create your first apps and services to run in GKE.Security for GKEAfter learning the basics of why, how, and what you’ll be using to run your workloads, the last key fundamental topic this training will cover is how to secure GKE and securely access Google Cloud services. Working in the cloud provides you with many tools to help run your business more securely, but that also means there’s a lot to learn about what those tools do and how they factor into the architectures you’ll build with GKE. We’ll go over the key security features and capabilities of Google Cloud and how to use them to help run your GKE clusters securely. You can check out our GKE Hardening Guide before watching my demo for this section, to get an even better understanding of GKE security. Ready to get started with GKE? Sign up here to reserve your seat forCloud OnBoard: Getting Started with Google Kubernetes Engineon June 22.Related ArticleIn case you missed it: All our free Google Cloud training opportunities from Q1Since January, we’ve introduced a number of no-cost training opportunities to help you grow your cloud skills. We’ve brought them togethe…Read Article
Quelle: Google Cloud Platform

18. Juni 2021

da Agency

How to get the most from Cloud SQL for SQL Server

At Google Cloud, we believe moving to the cloud shouldn’t have to mean starting over from scratch. That’s why we’re on a mission to give you choices for how you run your enterprise workloads, including migrating and modernizing your Windows workloads. In 2019, we launched Cloud SQL for Microsoft SQL Server so you can bring your existing, on-premises SQL Server databases and applications with you to the cloud. Our fully managed relational service, Cloud SQL, is an essential part of how we help enterprises focus on innovation, not only on infrastructure. In this post, we’ll explore some best practices for leveraging Cloud SQL for SQL Server, so you can better understand when and how to utilize our SQL Server managed offering. We’ll cover:Provisioning Cloud SQL for SQL ServerConnecting your data to your analytics Ensuring your data is secureUnderstanding high availabilityIf you’re looking for other database solutions for your data, read more about Google Cloud’s managed database services. As you start setting up your deployments, there are several key considerations you should keep in mind: 1. Provisioning your SQL Server instanceWe offer the same standardized machine types for Cloud SQL for SQL Server as PostgreSQL and MySQL, allowing you to take advantage of the full breadth and capability of the resources for instances up to 96 vCPU cores, 624 GB of RAM, and 30 terabyte SSD. One unique benefit is that Cloud SQL for SQL Server only runs on SSD—there’s no HDD option. We’ve found that there’s increased resiliency and fewer issues with offering a single option. You can initiate the creation of a machine or modify an existing instance the same way you would any other Cloud SQL instance using the console, gcloud commands, or our API. To seed your instance with data, Cloud SQL lets you import native backup (BAK) files so you can see your data offline. If you’d like to bring in data actively with minimal disruption, choose transactional replication to set up Cloud SQL as a subscriber.Once your Cloud SQL instance is running, you can set additional parameters and settings. For example, we recommend autoscaling your storage instead of pre-provisioning all the storage you need. Cloud SQL allows you to enable an automatic storage increase setting on the disk, so you don’t have to worry about having the correct allocation for project growth in the future. You can also use database flags for many SP_configure settings, including adjusting SQL Server parameters, adjusting options, and configuring and tuning an instance. This also includes setting a collation type to define the default sorting rules, case, and accent sensitivity for your databases. To get the most from your high availability (HA) configurations, and take full advantage of Cloud SQL’s 99.95% service-level agreement (SLA), select a regional availability and configure maintenance windows based on the best times to make any changes. We do our best to minimize disruptions by scheduling maintenance as quickly and as infrequently as possible, but our main priority is ensuring our service is secure and highly available. We get a lot of questions about the best way to utilize automatic backups for disaster recovery or restoring to other instances in other clouds or on-premises. By default, automatic backups run daily at the time you set. These are only storage snapshots of the persistent disk, which have no impact on Cloud SQL performance as it doesn’t leverage the database engine. For more frequent backups, set up manual backups using APIs or gcloud commands. However, you’ll need to manage the retention of those backups yourself, so we suggest leveraging manual backups in conjunction with automatic ones.Related ArticleRead Article2. Understanding high availability configurationsIn simple terms, the high availability configuration provides data redundancy. If a zone or instance becomes unavailable, your data will still be available to clients. How does this work? A Cloud SQL instance configured for HA (also known as a regional instance), is located in a primary and secondary zone and contains both a primary instance and a standby instance. Unlike SQL Server replication, Cloud SQL uses regional persistent disks (RePDs) to reduce downtime. Using synchronous replication to each zone’s persistent disk, all writes to your primary instance are synced to the standby instance. If a primary instance is unresponsive for approximately 60 seconds or a zone fails, the HA configuration switches over to the standby instance under the same IP and keeps your data available to applications. Another advantage is that high availability, or regional instances, only incur cost for a single license for the active resource. If you’d like to learn more, read about licensing pricing here. 3. Keeping security top of mindAt Google Cloud, ensuring security continues to be a top priority. That’s why we offer several cloud-wide platform features and differentiated security capabilities that ensure all of our products and services, including Cloud SQL, are as consistent and secure as possible. Google Cloud encrypts all your at-rest data by default. Data in transit is encrypted when data moves outside of Google’s network, but might not always be encrypted by default within. You can use SSL/TLS certificates to keep data secure when connecting to an instance using its public IP, and there are also additional security measures you can apply. You can also use customer-managed encryption keys (CMEK) as part of Cloud Key Management, allowing you to add your own cryptographic keys for data at rest in Cloud SQL. You have three connectivity options in Cloud SQL: Private IP—This is the easiest and most secure way to connect and access your Cloud SQL instance in your SQL Server database. You can set this as part of your VPC or peer-to-VPC networks.Public IP (with Cloud SQL Proxy)—If you’re coming from a different environment or cloud and need to use a public IP, we recommend using Cloud SQL Proxy whenever possible. Cloud SQL Proxy manages your SSL connectivity and settings without requiring you to authorize other networks.Public IP—If you prefer manual management options, we offer public IP addresses for your Cloud SQL instance. However, we strongly recommend following security best practices to avoid additional risk and exposure to threats.Our final tip deals with login credentials: Cloud SQL for SQL Server provides a default SQL Server user to help ensure the service’s integrity and security. If you would like to grant additional privileges beyond what is issued by default, you can use explicit syntax. You can also create more SQL Server users if you prefer to manage data access that way. 4. Transforming your SQL Server data into valuable insightsOne of the most common requests we hear from our SQL Server customers is that they want to use analytics services, such as SQL Server Recording Studio, Analytics Studio, and Integration Services. To help customers use their preferred services, we recommend running them separately in Compute Engine and then connecting them to your Cloud SQL instance. Your native tools, such as Query Optimizer or other Microsoft products can also be adopted for use in Cloud SQL by connecting them directly in your instance.Cloud SQL also lets you bring your data into other services in Google Cloud’s robust analytics ecosystem if you want to modernize your stack. For instance, a standard JDBC connection can join common services like Dataflow or Cloud Data Fusion, letting you create more complex pipelines for data transformation and data analytics purposes.To learn more about best practices for Cloud SQL for SQL Server check out the documentation here.Related ArticleUnderstanding the value of managed database servicesFully managed, relational cloud database services like Cloud SQL offload common database administration tasks for MySQL, PostgreSQL and S…Read Article
Quelle: Google Cloud Platform