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Today we are announcing the Preview of BigQuery Remote Functions. Remote Functions are user-defined functions (UDF) that let you extend BigQuery SQL with your own custom code, written and hosted in Cloud Functions, Google Cloud’s scalable pay-as-you-go functions as a service. A remote UDF accepts columns from BigQuery as input, performs actions on that input using a Cloud Function, and returns the result of those actions as a value in the query result. With Remote Functions, you can now write custom SQL functions in Node.js, Python, Go, Java, NET, Ruby, or PHP. This ability means you can personalize BigQuery for your company, leverage the same management and permission models without having to manage a server.In what type of situations could you use remote functions?Before today, BigQuery customers had the ability to create user defined functions or UDFs in either SQL or javascript that ran entirely within BigQuery. While these functions are performant and fully managed from within BigQuery, customers expressed a desire to extend BigQuery UDFs with their own external code. Here are some examples of what they have asked for:Security and Compliance: Use data encryption and tokenization services from the Google Cloud security ecosystem for external encryption and de-identification. We’ve already started working with key partners like Protegrity and CyberRes Voltage on using these external functions as a mechanism to merge BigQuery into their security platform, which will help our mutual customers address strict compliance controls. Real Time APIs: Enrich BigQuery data using external APIs to obtain the latest stock price data, weather updates, or geocoding information.Code Migration: Migrate legacy UDFs or other procedural functions written in Node.js, Python, Go, Java, .NET, Ruby or PHP. Data Science: Encapsulate complex business logic and score BigQuery datasets by calling models hosted in Vertex AI or other Machine Learning platforms.Getting StartedLet’s go through the steps to use a BigQuery remote UDF. Setup the BigQuery Connection: 1. Create a BigQuery Connection a. You may need to enable the BigQuery Connection APIDeploy a Cloud Function with your code: 1. Deploying your Cloud Function a. You may need to enable Cloud Functions API b. You may need to enable Cloud Build APIs 2. Grant the BigQuery Connection service account access to the Cloud Function a. One way you can find the service account is by using the bq cli show commandcode_block[StructValue([(u’code’, u’bq show –location=US –connection $CONNECTION_NAME’), (u’language’, u”)])]Define the BigQuery remote UDF: 1. Create the remote UDFs definition within BigQuery a. One way to find the endpoint name is to use the gCloud cli functions describe commandcode_block[StructValue([(u’code’, u’gcloud functions describe $FUNCTION_NAME’), (u’language’, u”)])]Use the BigQuery remote UDF in SQL: 1. Write a SQL statement as you would calling a UDF 2. Get your results! How remote functions can help you with common data tasksLet’s take a look at some examples of how using BigQuery with remote UDFs can help accelerate development and enhance data processing and analysis.Encryption and DecryptionAs an example, let’s create a simple custom encryption and decryption Cloud Function in Python. The encryption function can receive the data and return an encrypted base64 encoded string. In the same Cloud Function, the decryption function can receive an encrypted base64 encoded string and return the decrypted string. A data engineer would be able to enable this functionality in BigQuery.The Cloud Function receives the data and determines which function you want to invoke. The data is received as an HTTP request. The additional userDefinedContext fields allow you to send additional pieces of data to the Cloud Function.code_block[StructValue([(u’code’, u’def remote_security(request):rn request_json = request.get_json()rn mode = request_json[‘userDefinedContext’][‘mode’]rn calls = request_json[‘calls’]rn not_extremely_secure_key = ‘not_really_secure’rn if mode == “encryption”:rn return encryption(calls, not_extremely_secure_key)rn elif mode == “decryption”:rn return decryption(calls, not_extremely_secure_key)rn return json.dumps({“Error in Request”: request_json}), 400′), (u’language’, u”)])]The result is returned in a specific JSON formatted response that is returned to BigQuery to be parsed.code_block[StructValue([(u’code’, u’def encryption(calls,not_extremely_secure_key):rn return_value = []rn for call in calls:rn data = call[0].encode(‘utf-8′)rn cipher = AES.new(rn not_extremely_secure_key.encode(‘utf-8′)[:16],rn AES.MODE_EAXrn )rn cipher_text = cipher.encrypt(data)rn return_value.append(rn str(base64.b64encode(cipher.nonce + cipher_text))[2:-1]rn )rn return json.dumps({“replies”: return_value})’), (u’language’, u”)])]This Python code is deployed to Cloud Functions where it awaits to be invoked.Let’s add the User Defined Function to BigQuery so we can invoke it from a SQL statement. The additional user_defined_context is what is sent to Cloud Functions as additional context in the request payloadso you can use multiple remote functions mapped to one endpoint.code_block[StructValue([(u’code’, u’CREATE OR REPLACE FUNCTION `<project-id>.demo.decryption` (x STRING) RETURNS STRING REMOTE WITH CONNECTION `<project-id>.us.my-bq-cf-connection` OPTIONS (endpoint = ‘https://us-central1-<project-id>.cloudfunctions.net/remote_security’, user_defined_context = [(“mode”,”decryption”)])’), (u’language’, u”)])]Once we’ve created our functions, users with the right IAM permissions can use them in SQL on BigQuery.If you’re new to Cloud Functions, be aware that there are very minimal delays known as “cold starts”. The neat thing is you can call APIs as well, which is how our partners at Protegrity and Voltage enable their platforms to perform encryption and decryption of BigQuery data.Calling APIs to enrich your dataUsers, such as data analysts, can use the user defined functions created easily without needing other tools and moving the data out of BigQuery.You can enrich your dataset with many more APIs, for example, the Google Cloud Natural Language API to analyze sentiment on your text without having to use another tool.code_block[StructValue([(u’code’, u’def call_nlp(calls):rn return_value = []rn client = language_v1.LanguageServiceClient()rn for call in calls:rn text = call[0]rn document = language_v1.Document(rn content=text, type_=language_v1.Document.Type.PLAIN_TEXTrn )rn sentiment = client.analyze_sentiment(rn request={“document”: document}rn ).document_sentimentrn return_value.append(str(sentiment.score))rn return_json = json.dumps({“replies”: return_value})rn return return_json’), (u’language’, u”)])]Once the Cloud Function is deployed and the remote UDF definition is created on BigQuery, you are able to invoke the NLP API and return the data from it for use in your queries.Custom Vertex AI endpointData Scientists can integrate Vertex AI endpoints and other APIs, all from the SQL console for custom models. Remember, the remote UDFs are meant for scalar executions.You are able to deploy a model to a Vertex AI endpoint, which is another API, and then call that endpoint from Cloud Functions.code_block[StructValue([(u’code’, u’def predict_classification(calls):rn # Vertex AI endpoint detailsrn client = aiplatform.gapic.PredictionServiceClient(client_options=client_options)rn endpoint = client.endpoint_path(rn project=project, location=location, endpoint=endpoint_idrn )rn # Call the endpoint for eachrn for call in calls:rn content = call[0]rn instance = predict.instance.TextClassificationPredictionInstance(rn content=content,rn ).to_value()rn instances = [instance]rn parameters_dict = {}rn parameters = json_format.ParseDict(parameters_dict, Value())rn response = client.predict(rn endpoint=endpoint, instances=instances, parameters=parametersrn )’), (u’language’, u”)])]Try it out todayTry out the BigQuery remote UDFs today!Related ArticleRead Article
Quelle: Google Cloud Platform

12. Mai 2022

da Agency

Introducing AlloyDB for PostgreSQL: Free yourself from expensive, legacy databases

Enterprises are struggling to free themselves from legacy database systems, and need an alternative option to modernize their applications. Today at Google I/O, we’re thrilled to announce the preview of AlloyDB for PostgreSQL, a fully-managed, PostgreSQL-compatible database service that provides a powerful option for modernizing your most demanding enterprise database workloads. Compared with standard PostgreSQL, in our performance tests, AlloyDB was more than four times faster for transactional workloads, and up to 100 times faster for analytical queries. AlloyDB was also two times faster for transactional workloads than Amazon’s comparable service. This makes AlloyDB a powerful new modernization option for transitioning off of legacy databases.As organizations modernize their database estates in the cloud, many struggle to eliminate their dependency on legacy database engines. In particular, enterprise customers are looking to standardize on open systems such as PostgreSQL to eliminate expensive, unfriendly licensing and the vendor lock-in that comes with legacy products. However, running and replatforming business-critical workloads onto an open source database can be daunting: teams often struggle with performance tuning, disruptions caused by vacuuming, and managing application availability. AlloyDB combines the best of Google’s scale-out compute and storage, industry-leading availability, security, and AI/ML-powered management with full PostgreSQL compatibility, paired with the performance, scalability, manageability, and reliability benefits that enterprises expect to run their mission-critical applications.As noted by Carl Olofson, Research Vice President, Data Management Software, IDC, “databases are increasingly shifting into the cloud and we expect this trend to continue as more companies digitally transform their businesses. With AlloyDB, Google Cloud offers large enterprises a big leap forward, helping companies to have all the advantages of PostgreSQL, with the promise of improved speed and functionality, and predictable and transparent pricing.”AlloyDB is the next major milestone in our journey to support customers’ heterogeneous migrations. For example, we recently added Oracle-to-PostgreSQL schema conversion and data replication capabilities to our Database Migration Service, while our new Database Migration Program helps you accelerate your move to the cloud with tooling and incentive funding. “Developers have many choices for building, innovating and migrating their applications. AlloyDB provides us with a compelling relational database option with full PostgreSQL compatibility, great performance, availability and cloud integration. We are really excited to co-innovate with Google and can now benefit from enterprise grade features while cost-effectively modernizing from legacy, proprietary databases.”—Bala Natrajan, Sr. Director, Data Infrastructure and Cloud Engineering, PayPal Let’s dive into what makes AlloyDB uniqueWith AlloyDB, we’re tapping into decades of experience designing and managing some of the world’s most scalable and available database services, bringing the best of Google to the PostgreSQL ecosystem. At AlloyDB’s core is an intelligent, database-optimized storage service built specifically for PostgreSQL. AlloyDB disaggregates compute and storage at every layer of the stack, using the same infrastructure building blocks that power large-scale Google services such as YouTube, Search, Maps, and Gmail. This unique technology allows it to scale seamlessly while offering predictable performance.Additional investments in analytical acceleration, embedded AI/ML, and automatic tiering of data means that AlloyDB is ready to handle any workload you throw at it, with minimal management overhead.Finally, we do all this while maintaining full compatibility with PostgreSQL 14, the latest version of the advanced open source database, so you can reuse your existing development skills and tools, and migrate your existing PostgreSQL applications with no code changes, benefitting from the entire PostgreSQL ecosystem. Furthermore, by using PostgreSQL as the foundation of AlloyDB, we’re continuing our commitment to openness while delivering differentiated value to our customers.“We have been so delighted to try out the new AlloyDB for PostgreSQL service. With AlloyDB, we have significantly increased throughput, with no application changes to our PostgreSQL workloads. And since it’s a managed service, our teams can spend less time on database operations, and more time on value added tasks.”—Sofian Hadiwijaya, CTO and Co-Founder, Warung PintarWith AlloyDB you can modernize your existing applications with:1. Superior performance and scaleAlloyDB delivers superior performance and scale for your most demanding commercial-grade workloads. AlloyDB is four times faster than standard PostgreSQL and two times faster than Amazon’s comparable PostgreSQL-compatible service for transactional workloads. Multiple layers of caching, automatically tiered based on workload patterns, provide customers best-in-class price/performance.2. Industry-leading availabilityAlloyDB provides a high-availability SLA of 99.99% inclusive of maintenance. AlloyDB automatically detects and recovers from most database failures within seconds, independent of database size and load. AlloyDB’s architecture also supports non-disruptive instance resizing and database maintenance. The primary instance can resume normal operations in seconds, while replica pool updates are fully transparent to users. This ensures that customers have a highly reliable, continuously available database for their mission-critical workloads.“We are excited about the new PostgreSQL-compatible database. AlloyDB will bring more scalability and availability with no application changes. As we run our e-commerce platform and its availability is important, we are specially expecting AlloyDB to minimize the maintenance downtime.”—Ryuzo Yamamoto, Software Engineer, Mercari (Souzoh, Inc.)3. Real-time business insights AlloyDB delivers up to 100 times faster analytical queries than standard PostgreSQL. This is enabled by a vectorized columnar accelerator that stores data in memory in an optimized columnar format for faster scans and aggregations. This makes AlloyDB a great fit for business intelligence, reporting, and hybrid transactional and analytical workloads (HTAP). And even better, the accelerator is auto-populated, so you can improve analytical performance with the click of a button. “At PLAID, we are developing KARTE, a customer experience platform. It provides advanced real-time analytics capabilities for vast amounts of behavioral data to discover deep insights and create an environment for communicating with customers. AlloyDB is fully compatible with PostgreSQL and can transparently extend column-oriented processing. We think it’s a new powerful option with a unique technical approach that enables system designs to integrate isolated OLTP, OLAP, and HTAP workloads with minimal investment in new expertise. We look forward to bringing more performance, scalability, and extensibility to our analytics capabilities by enhancing data integration with Google Cloud’s other powerful database services in the future.”—Takuya Ogawa, Lead Product Engineer, PLAID4. Predictable, transparent pricingAlloyDB makes keeping costs in check easier than ever. Pricing is transparent and predictable, with no expensive, proprietary licensing and no opaque I/O charges. Storage is automatically provisioned and customers are only charged for what they use, with no additional storage costs for read replicas. A free ultra-fast cache, automatically provisioned in addition to instance memory, allows you to maximize price/performance.5. ML-assisted management and insights Like many managed database services, AlloyDB automatically handles database patching, backups, scaling and replication for you. But it goes several steps further by using adaptive algorithms and machine learning for PostgreSQL vacuum management, storage and memory management, data tiering, and analytics acceleration. It learns about your workload and intelligently organizes your data across memory, an ultra-fast secondary cache, and durable storage. These automated capabilities simplify management for DBAs and developers. AlloyDB also empowers customers to better leverage machine learning in their applications. Built-in integration with Vertex AI, Google Cloud’s artificial intelligence platform, allows users to call models directly within a query or transaction. That means high throughput, low-latency, and augmented insights, without having to write any additional application code.Get started with AlloyDBA modern database strategy plays a critical role in developing great applications faster and delivering new experiences to your customers. The AlloyDB launch is an exciting milestone for Google Cloud databases, and we’re thrilled to see how you use it to drive innovation across your organization and regain control and freedom of your database workloads.To learn more about the technology innovations behind AlloyDB, check out this deep dive into its intelligent storage system. Then, visit cloud.google.com/alloydb to get started and create your first cluster. You can also review the demos and launch announcements from Google I/O 2022.Related ArticleAlloyDB for PostgreSQL under the hood: Intelligent, database-aware storageIn this technical deep dive, we take a look at the intelligent, scalable storage system that powers AlloyDB for PostgreSQL.Read Article
Quelle: Google Cloud Platform

12. Mai 2022

da Agency

Google Cloud unveils world’s largest publicly available ML hub with Cloud TPU v4, 90% carbon-free energy

At Google, the state-of-the-art capabilities you see in our products such as Search and YouTube are made possible by Tensor Processing Units (TPUs), our custom machine learning (ML) accelerators. We offer these accelerators to Google Cloud customers as Cloud TPUs. Customer demand for ML capacity, performance, and scale continues to increase at an unprecedented rate. To support the next generation of fundamental advances in artificial intelligence (AI), today we announced Google Cloud’s machine learning cluster with Cloud TPU v4 Pods in Preview — one of the fastest, most efficient, and most sustainable ML infrastructure hubs in the world.Powered by Cloud TPU v4 Pods, Google Cloud’s ML cluster enables researchers and developers to make breakthroughs at the forefront of AI, allowing them to train increasingly sophisticated models to power workloads such as large-scale natural language processing (NLP), recommendation systems, and computer vision algorithms. At 9 exaflops of peak aggregate performance, we believe our cluster of Cloud TPU v4 Pods is the world’s largest publicly available ML hub in terms of cumulative computing power, while operating at 90% carbon-free energy. “Based on our recent survey of 2000 IT decision makers, we found that inadequate infrastructure capabilities are often the underlying cause of AI projects failing. To address the growing importance for purpose-built AI infrastructure for enterprises, Google launched its new machine learning cluster in Oklahoma with nine exaflops of aggregated compute. We believe that this is the largest publicly available ML hub with 90% of the operation reported to be powered by carbon free energy. This demonstrates Google’s ongoing commitment to innovating in AI infrastructure with sustainability in mind.” —Matt Eastwood, Senior Vice President, Research, IDCPushing the boundaries of what’s possibleBuilding on the announcement of Cloud TPU v4 at Google I/O 2021, we granted early access to Cloud TPU v4 Pods to several top AI research teams, including Cohere, LG AI Research, Meta AI, and Salesforce Research. Researchers liked the performance and scalability that TPU v4 provides with its fast interconnect and optimized software stack, the ability to set up their own interactive development environment with our new TPU VM architecture, and the flexibility to use their preferred frameworks, including JAX, PyTorch, or TensorFlow. These characteristics allow researchers to push the boundaries of AI, training large-scale, state-of-the-art ML models with high price-performance and carbon efficiency.co-here.jpglg ai research.jpgmeta.jpgsalesforce.jpgIn addition, TPU v4 has enabled breakthroughs at Google Research in the areas of language understanding, computer vision, speech recognition, and much more, including the recently announced Pathways Language Model (PaLM) trained across two TPU v4 Pods.“In order to make advanced AI hardware more accessible, a few years ago we launched theTPU Research Cloud (TRC) program that has provided access at no charge to TPUs to thousands of ML enthusiasts around the world. They have published hundreds of papers and open-source github libraries on topics ranging from ‘Writing Persian poetry with AI’ to ‘Discriminating between sleep and exercise-induced fatigue using computer vision and behavioral genetics’. The Cloud TPU v4 launch is a major milestone for both Google Research and our TRC program, and we are very excited about our long-term collaboration with ML developers around the world to use AI for good.”—Jeff Dean, SVP, Google Research and AISustainable ML breakthroughsThe fact that this research is powered predominantly by carbon-free energy makes the Google Cloud ML cluster all the more remarkable. As part of Google’s commitment to sustainability, we’ve been matching 100% of our data centers’ and cloud regions’ annual energy consumption with renewable energy purchases since 2017. By 2030, our goal is to run our entire business on carbon-free energy (CFE) every hour of every day. Google’s Oklahoma data center, where the ML cluster is located, is well on its way to achieving this goal, operating at 90% carbon-free energy on an hourly basis within the same grid. In addition to the direct clean energy supply, the data center has a Power Usage Efficiency (PUE)1 rating of 1.10, making it one of the most energy-efficient data centers in the world. Finally, the TPU v4 chip itself is highly energy efficient, with about 3x the peak FLOPs per watt of max power of TPU v3. With energy-efficient ML-specific hardware, in a highly efficient data center, supplied by exceptionally clean power, Cloud TPU v4 provides three key best practices that can help significantly reduce energy use and carbon emissions.Breathtaking scale and price-performanceIn addition to sustainability, in our work with leading ML teams we have observed two other pain points: scale and price-performance. Our ML cluster in Oklahoma offers the capacity that researchers need to train their models, at compelling price-performance, on the cleanest cloud in the industry. Cloud TPU v4 is central to solving these challenges. Scale: Each Cloud TPU v4 Pod consists of 4096 chips connected together via an ultra-fast interconnect network with the equivalent of an industry-leading 6 terabits per second (Tbps) of bandwidth per host, enabling rapid training for the largest models.Price-performance: Each Cloud TPU v4 chip has ~2.2x more peak FLOPs than Cloud TPU v3, for ~1.4x more peak FLOPs per dollar. Cloud TPU v4 also achieves exceptionally high utilization of these FLOPs for training ML models at scale up through thousands of chips. While many quote peak FLOPs as the basis for comparing systems, it is actually sustained FLOPs at scale that determines model training efficiency, and Cloud TPU v4’s high FLOPs utilization (significantly better than other systems due to high network bandwidth and compiler optimizations) helps yield shorter training time and better cost efficiency.Table 1: Cloud TPU v4 pods deliver state-of-the-art performance through significant advancements in FLOPs, interconnect, and energy efficiency.Cloud TPU v4 Pod slices are available in configurations ranging from four chips (one TPU VM) to thousands of chips. While slices of previous-generation TPUs smaller than a full Pod lacked torus links (“wraparound connections”), all Cloud TPU v4 Pod slices of at least 64 chips have torus links on all three dimensions, providing higher bandwidth for collective communication operations.Cloud TPU v4 also enables accessing a full 32 GiB of memory from a single device, up from 16 GiB in TPU v3, and offers two times faster embedding acceleration, helping to improve performance for training large-scale recommendation models. PricingAccess to Cloud TPU v4 Pods comes in evaluation (on-demand), preemptible, and committed use discount (CUD) options. Please refer to this page for more details.Get started todayWe are excited to offer the state-of-the-art ML infrastructure that powers Google services to all of our users, and look forward to seeing how the community leverages Cloud TPU v4’s combination of industry-leading scale, performance, sustainability, and cost efficiency to deliver the next wave of ML-powered breakthroughs. Ready to start using Cloud TPU v4 Pods for your AI workloads? Reach out to your Google Cloud account manager or fill in this form. Interested in access to Cloud TPU for open-source ML research? Check out our TPU Research Cloud program.AcknowledgementsThe authors would like to thank the Cloud TPU engineering and product teams for making this launch possible. We also want to thank James Bradbury, Software Engineer, Vaibhav Singh, Outbound Product Manager and Aarush Selvan, Product Manager, for their contributions to this blog post.1. We report a comprehensive trailing twelve-month (TTM) PUE in all seasons, including all sources of overhead.Related ArticleCloud TPU VMs are generally availableCloud TPU VMs with Ranking & Recommendation acceleration are generally available on Google Cloud. Customers will have direct access to TP…Read Article
Quelle: Google Cloud Platform

12. Mai 2022

da Agency

Google Cloud at I/O: Everything you need to know

We love this time of year. This week is Google I/O, our largest developer conference, where developer communities from around the world come together to learn, catch up, and have fun. Google Cloud and Google Workspace had a big presence at the show, talking about our commitment to building intuitive and helpful developer experiences to help you innovate freely and quickly. We do the heavy lifting, embedding the expertise from years of Google research in areas like AI/ML and security, so you can easily build secure and intelligent solutions for your customers.So, what’s happening at I/O this year? Let’s start with the keynotes… Google I/O keynote Google and Alphabet CEO Sundar Pichai kicked off Day 1 of I/O with a powerhouse keynote highlighting recent breakthroughs in machine learning, including one of the fastest, most efficient, and most sustainable ML infrastructure hubs in the world. Google Cloud’s machine learning cluster with Cloud TPU v4 pods (in Preview), allows researchers and developers to make AI breakthroughs by training larger and more complex models faster, to power workloads like large-scale natural language processing (NLP), recommendation systems, and computer vision. With eight TPU v4 pods in a single data center, generating 9 exaflops of peak performance, we believe this system is the world’s largest publicly available ML hub in terms of cumulative computing power, while operating at 90% carbon-free energy. Read more about the ML hub with Cloud TPU v4 here.“Early access to TPU v4 has enabled us to achieve breakthroughs in conversational AI programming with our CodeGen, a 16-billion parameter auto-regressive language model that turns simple English prompts into executable code.” —Erik Nijkamp, Research Scientist, Salesforce“…we saw a 70% improvement in training time for our ‘extremely large’ model when moving from TPU v3 to TPU v4… The exceptionally low carbon footprint of Cloud TPU v4 Pods was another key factor.…”—Aidan Gomez, CEO and Co-Founder, CohereIn the keynote, Sundar also announced new AI-enabled features in Google Workspace focused on users, that are designed to help people thrive in the hybrid workplace. New advancements in NLP enable summaries in Spaces to help users catch up on missed conversations by providing a helpful digest. Automated meeting transcription for Google Meet allows users who didn’t attend a meeting to stay in the loop, or for attendees to easily reference the discussion at a later time. Users can also now leverage portrait restore, which automatically improves video image quality — even on devices with lower quality webcams. And they can filter out the reverberation in large spaces with hard surfaces, giving users “conference-room-quality” audio whether they are in their basement, kitchen, or garage. These new features deliver high quality experiences, allowing Google Workspace users to benefit from our AI leadership.Developer keynoteNext up, we heard from Jeanine Banks, Google Vice President of Developer Experiences and DevRel, and a number of product teams who led us through a flurry of exciting new updates about everything from Android to Flutter to Cloud. On the Google Cloud front, we announced the preview of Cloud Run jobs, which can reduce the time developers spend performing administrative tasks such as database migration, managing scheduled jobs like nightly reports, or doing batch data transformation. With Cloud Run jobs, you can execute your code on the highly scalable, fully managed Cloud Run platform, but only pay when your jobs are executing — and without having to worry about managing infrastructure. Learn more about Cloud Run jobs here.Then, we announced the preview of AlloyDB for PostgreSQL, a new fully managed, relational database service that gives enterprises the performance, availability, and ease of management they need to migrate from their expensive legacy database systems and onto Google Cloud. AlloyDB combines proven, disaggregated storage and compute that powers our most popular, globally available products such as Google Maps, YouTube, Search, and Ads — with PostgreSQL, an open source database engine beloved by developers.Our performance tests show that AlloyDB is four times faster for transaction processing and up to 100 times faster for analytical queries than standard PostgreSQL. It’s also two times faster than AWS’ comparable PostgreSQL-compatible service for transactional workloads. AlloyDB’s fully-managed database operations and ML-based management systems can relieve administrators and developers from daunting database management tasks. Of course, AlloyDB is fully PostgreSQL-compatible, meaning that developers can reuse their existing development skills and tools. It also offers an impressive 99.99% SLA inclusive of maintenance, and no complex licensing or I/O charges. You can learn more about AlloyDB for PostgreSQL here.“Developers have many choices for building, innovating and migrating their applications. AlloyDB provides us with a compelling relational database option with full PostgreSQL compatibility, great performance, availability, and cloud integration. We are really excited to co-innovate with Google and can now benefit from enterprise grade features while cost-effectively modernizing from legacy, proprietary databases.”—Bala Natrajan, Sr. Director, Data Infrastructure and Cloud Engineering at PayPalCloud keynote – “The cloud built for developers” Moving on to the Cloud keynote, Google Cloud’s very own Aparna Sinha, Director of Product Management, Google Cloud and Google Workspace’s Matthew Izatt, Product Lead, gave the I/O audience exciting cloud updates. Aparna reiterated the benefits of Cloud Run jobs and AlloyDB, while showcasing how our services integrate nicely to give you a full stack specifically tailored for backend, web, mobile and data analytics applications. These stacks also natively embed key security and AI/ML features for simplicity. Specifically, with build integrity, a new feature in Cloud Build, you get out-of-the-box build provenance and “Built by Cloud Build” attestations, including details like the images generated, the input sources, the build arguments, and the built time, helping you achieve up to SLSA Level 2 assurance. Next, you can use Binary Authorization to help ensure that only verified builds with the right attestations are deployed to production. You can get the same results as the experts — without having to be a security expert yourself. Aparna also announced the preview of Network Analyzer, showing how developers can troubleshoot and isolate root causes of complex service disruptions quickly and easily. The new Network Analyzer module in Network Intelligence Center can proactively detect network failures to prevent downtime caused by accidental misconfiguration, over-utilization, and suboptimal routes. Network Analyzer is available for services like Compute Engine, Google Kubernetes Engine (GKE), Cloud SQL, and more. You can visit the Network Analyzer page to learn more. Something that really got the developer audience excited was the announcement of the preview of Immersive Stream for XR allowing you to render eXtended Reality experiences using powerful Google Cloud GPUs, and stream these experiences to mobile devices around the world. Immersive Stream for XR integrates the process of creating, maintaining, and scaling high-quality XR. In fact, XR content delivered using Immersive Stream for XR works on nearly every mobile device regardless of model, year, or operating system. Also, your users can enjoy these immersive experiences simply by clicking a link or scanning a QR code. “We know that our new and existing customers expect unique and innovative campaigns for two of the most unique and innovative vehicles in our brand’s history, and Google Cloud helped us create something very special to share with them.”—Albi Pagenstert, Head of Brand Communications and Strategy, BMW of North America To learn more, visit xr.withgoogle.com, and check out this video to see for yourself!And finally, Matthew brought it all home, highlighting the incredible innovation coming from Google Workspace. He detailed how we are making it easier for developers to extend and customize the suite, and simplify integration with existing tools. For example, Google Workspace Add-ons allow you to build applications using your preferred stack and languages; you just build once, and your application is available to use across Google Workspace apps such as Gmail, Google Calendar, Drive and Docs. Matthew also shared how we are improving the development experience by allowing you to easily connect DevOps tools like PagerDuty to the Google Workspace platform. Finally, he noted the critical role that Google Workspace Marketplace can play in increasing the growth and engagement of your application. If you’re interested in learning about how we’re using machine learning to help make people’s work day more productive and impactful, here’s where you can find all of this week’s Workspace news. Sessions and workshopsWhew… that was a lot of cloud updates in three keynotes! But wait… there’s more!Google Cloud also had 14 cloud breakout sessions and 5 workshops at I/O, covering loads of different topics. Here’s the full list for you, all available on demand:SessionsAn introduction to MLOps with TFXAsynchronous operations in your UI using Workflows and FirestoreAuto alerts for Firebase users with Functions, Logging, and BigQueryConversational AI for business messagingDevelop for Google Cloud Platform faster with Cloud CodeExtending Google Workspace with AppSheet’s no-code platform and Apps ScriptFraudfinder: A comprehensive solution for real data science problemsFrom colab to Cloud in five stepsLearn how to enable shared experiences across platformsLearn to refactor Cloud applications in Go 1.18 with GenericsModern Angular deployment with Google CloudRun your jobs on serverlessThe future of app development with cloud databasesWhat’s new in the world of Google Chat appsWorkshopsApply responsible AI principles when building remote sensing datasetsBuild an event-driven orchestration with Eventarc and WorkflowsBuilding AppSheet apps with the new Apps Script connectorFaster model training and experimentation with Vertex AISpring Native on GCP – what, when, and why?And finally, what would I/O be without some massively fun interactive experiences? Take our cloud island at I/O Adventure featuring custom interactive demos and sandboxes. Here, attendees can explore content, chat with Googlers, and earn some really cool swag. So that’s a wrap on Google Cloud announcements at I/O. We’ll have lots more exciting announcements in the next few months that will make your developer experience even simpler and more intuitive. In the meantime, join our developer community, Google Cloud Innovators, where you’ll make lots of awesome new friends. And be sure to register for Google Cloud Next ’22 in October. We can’t wait to see you again!
Quelle: Google Cloud Platform