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Join us for our first Google Cloud Security Talks of 2021, a live online event on March 3rd where we’ll help you navigate the latest in cloud security.We’ll share expert insights into our security ecosystem and cover the following topicsSunil Potti and Rob Sadowski will kick off Security Talks on March 3rd.Thomas Kurian and Juan Rajlin join us for a conversation on overcoming risk management challenges in the Cloud.This will be followed by a roundtable to get insight into cloud risk management with Phil Venables and leaders from the industry.Javier Soltero and Karthik Lakshminarayan will talk about information governance in Google Workspace and how it can enable users to access data safely and securely while preserving privacy.Following this will be a panel discussion on the future of Confidential Computing with Raghu Nambiar (AMD), Harold Giménez (Hashicorp), Solomon Cates (Thales), Nelly Porter & Sam Lugani.You will learn about the unique components of the Chronicle security analytics platform that enable security teams to supercharge their security telemetry with Mike Hom.Peter Blum and Emil Kiner will present the innovations we are making with machine learning to better protect networks. You will also learn about Chrome browser’s security capabilities, including how Chrome helps support a zero trust environment, with Philippe Rivard and Robert Shield.Finally, Timothy Peacock will do a deep dive into Container Threat Detection, a built-in service of Security Command Center that detects the most common container runtime attacks and alerts you to any suspicious activity. We look forward to sharing our latest security insights and solutions with you. Sign-up now to reserve your virtual seat.Related ArticleNew research reveals who’s targeted by email attacksOur new study examines over a billion phishing and malware emails and their anonymized targets to better understand what factors influenc…Read Article
Quelle: Google Cloud Platform

11. Februar 2021

da Agency

Build a chatbot resume on Google Cloud

Getting the job you want requires you to stand out to potential employers—especially in the current job market. Recently I did just that by building a conversational chatbot on Google Cloud that answers questions about my professional experience (plus some surprises). Not only did I stand out, but I learned how to build and host my own chatbot on my website. Creating a new Dialogflow agent1. If you don’t have one, Create a Google Cloud project – for new users there’s a $300 credit that was more than enough for this application in my case. 2. After you have a Google Cloud project and have your GCP account, go to the Dialgoflow Essentials Console. (Google has two different products Dialogflow CX and Dialogflow Essentials, and we’ll be using Essentials for this simple application). On the top left you should see something that allows you to choose a location first (in case you have data location requirements), and then create a new agent.After you click that button, name your agent and associate it with your Google Cloud Project. Here are the values I chose for my agent:Give your agent some understanding3. Let’s create an intent. The way the agent communicates is by inferring the “Intents” of its interlocutor. When a user writes or says something the agent matches the expression to the best intent that you created in your Dialogflow agent. For each Dialogflow agent, you define many intents, where your combined intents can handle a complete conversation. So we need to create these: find the intents button on the left side navigation bar.And then in the centre click “create intent” to create a new one.Creating an intent has two main parts: (1) what the agent expects its interlocutor to write or say and (2) what the agent says in response. For example, I want to create an intent where the interlocutor is asking about my certifications, and my agent responds with which certifications I have. For this I need to give it “Training Phrases”. In practice, it helps me to think about this as a sink or funnel: I start by deciding I want my agent to be able to talk about a topic (the response, the bottom of the funnel) so I’ll have to think about the kinds of sentences that I want to fall into that funnel (the training phrases, the catching area of the funnel). The example will make it clear:3.1. Let’s create that top part of the intent. Click on “Add Training Phrases “Add some training phrases that exemplify what the intent should capture. I name the intent “Certifications” and I add some sentences like this:It’s best if you add more than 10 sentences that cover the range of ways you want to capture the conversation into the “Certifications” funnel response. 3.2. Now the bottom part of the funnel: what should the agent say in response? Click “Add Response”Here’s what I’ve put in my case:Click “Save” on the top of the page. Let’s try it out: on the top right hand side of the page, look for “Try it now”. Notice how I can ask a question that has different words (accreditations, diploma) and still get the agent to understand what the intent is, and therefore what answer to give.This is what the NLP models are doing for you: from your dozen examples, they understand the kinds of sentences that the agent should link to that intent, and then return the appropriate answer. In the funnel analogy: it’s capturing related questions into the same funnel and responding with the appropriate answer. 4. Next, let’s change the Welcome Intent. As a best practice, you should start the conversation with a greeting plus a few lines on what your specific agent can do for the user. This way you can direct the conversation in the right direction. To change the Default Welcome intent, first save the work you have done earlier. Click on the “Save” button on the top right hand of the page. Next, click again on “Intents” on the left of the navigation bar and then click on “Default Welcome Intent” on the main menu. In the “Responses” section you’ll see the default responses.Which you can then change to something more appropriate, like:Once you have changed the default responses to something that fits your application, click Save. 5. Go create more intents! For a conversational-resume these should be questions that you’d expect to get from a recruiter. I have some general intents like “Favourite Project” (trained with sentences like “What was Filipe’s favourite project in his career?” and “Tell me what Filipe is most proud of achieving. ”) or “Strengths” (trained with sentences like “What are some of Filipe’s main strengths?” and “Tell me what kinds of tasks people turn to Filipe for?”). Because I have a background in data science and programming, I also have intents that ask about my statisticals skills, or familiarity with Cloud technologies. Don’t forget to keep testing your agent on the panel on the right, to see if it responds as you want to inputs from interlocutors. Once done, you are ready to deploy your agentHost your agent on a website6. Let’s get a website! The easiest way here is to click here to get a google site. Just use a template or create a blank one. Later, if you buy your own domain, you can host it there. That’s what I did: www.filipegracio.com is built on top of a Google Site. 7. Now we’re going to get the agent on the website. Go back to the Dialogflow console, go to integrations, and turn on the Dialogflow Messenger option.When you do this, you’ll see a new window appear with a bit of code you’ll be able to embed on your site. Make sure your integration is enabled. Here’s what the bit of code looks like:Copy that code with the little clipboard symbol on the bottom right. 8. Next we just need to put the agent on your site! You do this back on your created website. While editing the content of Google website, on a blank page section, double click and you’ll see this wheel show up, click on Embed.And now embed the code of the bot that you copied from the Dialogflow console. Like so:After you do this, and “Publish” the website (on the top right there’s the button) your website should be available to the public with your agent ready to answer everything the visitors ask about. Explore your creativity You can make your chatbot be about whatever you want. It can help your business, it can promote your hobby, and it can help you find a job. If you use it like I did, put a link to the website on top of your resume, and make sure it’s visible on your social profiles, share it online. People will notice and you’ll be proving that you have skills, that you made a special effort, and that you think creatively. Good luck!Related ArticleRespond to customers faster and more accurately with Dialogflow CXNew Dialogflow CX Virtual Agents can jumpstart your contact center operational efficiency goals, drive CSAT up and take care of your huma…Read Article
Quelle: Google Cloud Platform

11. Februar 2021

da Agency

Freedom of choice: 9TB SSDs bring ultimate IOPS/$ to Compute Engine VMs

Applications that perform low-latency, I/O-intensive operations need to run on virtual machines with high-performance storage that’s tightly coupled with compute. This is especially important for applications built around real-time analytics, e-commerce, gaming, social media, and advertising platforms. Custom machine types in Compute Engine not only let you attach high-performance Local SSD, but give you the flexibility to customize your VMs to your workload’s exact needs. Today, we are excited to announce that you can attach 6TB and 9TB Local SSD to second-generation general-purpose N2 Compute Engine VMs, for great IOPS per dollar. 9TB Local SSD delivers up to 2.4 million IOPS and 9.4 GB/s of throughput at direct-attach latencies, on any N2 VM with 24 or more vCPUs. And because you can attach these SSDs to any N2 VM shape (including custom shapes), you can define the exact VM that your application needs in terms of CPU, RAM, and SSD. You don’t need to attach more CPU and memory than what your I/O-intensive or storage-intensive workload demands, so you can optimize specifically for IOPS/$ or density/$—or a combination thereof.Disclaimer: Results are based on Google Cloud’s internal benchmarkingMaximum storage performance with fewer vCPUs6TB and 9TB Local SSDs have been available for N1 VMs, allowing you to achieve that maximum 2.4 million IOPS with 32 vCPUs or more. With N2 VMs, you need as few as 24 vCPUs to drive that same performance. This translates to a 7% better total cost of ownership for N2 VMs, relative to N1s. Some applications afford you the flexibility to optimize performance further, at different I/O queue depths or different block sizes. Using performance benchmarking tools like FIOcan help you make the optimal choice. As shown below, internal testing demonstrates that Local SSDs offer consistent performance across a broad range of configurations that your workloads might demand.Disclaimer: Results are based on Google Cloud’s internal benchmarkingMaximum throughputAttaching Local SSD to a VM is also a good strategy for workloads that demand high storage throughput. As you can see from the charts below, Local SSD can deliver close to maximum throughput at a wide range of block sizes (4K, 16K, 128K) and I/O depths, depending on the needs of your databases and applications.Disclaimer: Results are based on Google Cloud’s internal benchmarkingGet started todayLocal SSD are priced per-GB irrespective of the VM to which they are attached. Visit our pricing page for specific pricing in your region. 6TB and 9TB Local SSDs are now Generally Available on both N2 and N2D VMs. For more details, check out our documentation for Local SSDs. If you have questions or feedback, check out the Getting Help page.Related ArticleLocal SSDs + VMs = love at first (tera)byteIn Google Cloud Storage you can now attach 6TB and 9TB local SSDs to virtual machines (VMs) for higher throughput and IOPS per VM.Read Article
Quelle: Google Cloud Platform

11. Februar 2021

da Agency

How to trigger Cloud Run actions on BigQuery events

Many BigQuery users ask for database triggers—a way to run some procedural code in response to events on a particular BigQuery table, model, or dataset. Maybe you want to run an ELT job whenever a new table partition is created, or maybe you want to retrain your ML model whenever new rows are inserted into the table.In the general category of “Cloud gets easier”, this article will show how to quite simply and cleanly tie together BigQuery and Cloud Run. Because if you love BigQuery and you love Cloud Run, how can you not love when they get together?!Cloud Run will be triggered when BigQuery writes to its audit log. Every data access in BigQuery is logged (there is no way to turn it off), and so all that we need to do is to find out the exact log message that we are looking for.Follow along with me.Find the BigQuery eventI’m going to take a wild guess here and assume that you don’t want to muck up your actual datasets, so create a temporary dataset named cloud_run_tmp in your project in BigQuery.In that project, let’s create a table into which we will insert some rows to try things out. Grab some rows from a BigQuery public dataset to create this table:Then, run the insert query that we want to build a database trigger for:Now, in another Chrome tab, click on this link to filter for BigQuery audit events in Cloud Logging.I found this event:Note that there will be several audit logs for a given BigQuery action. In this case, for example, when we submit a query, a log will be generated immediately. But only after the query is parsed does BigQuery know which table(s) we want to interact with, so the initial log will not have the table name. Keep in mind that you don’t want any old audit log… make sure to look for a unique set of attributes that clearly identifies your action.In the case of inserting rows, this is the combination:The method is google.cloud.bigquery.v2.JobService.InsertJobThe name of the table being inserted to is the protoPayload.resourceNameThe dataset id is available as resource.labels.dataset_idThe number of inserted rows is protoPayload.metadata.tableDataChanged.insertedRowsCountWrite the Cloud Run ActionNow that we know the payload that we are looking for, we can write the Cloud Run action. Let’s do it in Python as a Flask App (full code is on GitHub).First, we make sure that this is the event we want to process:Once we have identified that this is the event we want, then we carry out the action that we want to do. Here, let’s do an aggregation and write out a new table:The Dockerfile for the container is simply a basic Python container into which we install Flask and the BigQuery client library:Deploy Cloud RunBuild the container and deploy it using a couple of gcloud commands:Setup Event TriggerIn order for the trigger to work, the service account for Cloud Run will need a couple of permissions:Finally create the event trigger:The important thing to note is that we are triggering on any Insert log created by BigQuery. That’s why, in the action, we had to filter these events based on the payload.What events are supported? An easy way to check is to look at the Web Console for Cloud Run. Here are a few to get your mind whirring:Try it outNow, try out the BigQuery -> Cloud Run trigger and action. Go to the BigQuery console and insert a row or two:Watch as a new table called created_by_trigger gets created! You have successfully triggered a Cloud Run action on a database event in BigQuery. Enjoy!ResourcesAll the code, along with a README with instructions, is on GitHub.This blog post is an update to the book BigQuery: The Definitive Guide. My goal is to update the book contents approximately once a year, and provide updates in the form of blogs like this.You can find previous such update blogs linked from the GitHub repository of the book.Thanks to Prashant Gulati.Related Article3 cool Cloud Run features that developers loveCloud Run developers enjoy pay-per-use pricing, multiple concurrency and secure event processing.Read Article
Quelle: Google Cloud Platform

11. Februar 2021

da Agency

Docker Index Shows Continued Massive Developer Adoption and Activity to Build and Share Apps with Docker

It’s been one year since we started publishing the Docker Index (stats, trends and analysis from developers and dev teams based on anonymized data from millions of Docker users). At that time we saw how Docker was being used at an incredible scale to power application building globally. Today we are excited to share the latest edition of the Docker Index, this time with some yearly and quarterly comparisons.

Every time we pull these user stats, we are blown away by the sheer volume and continued growth in activity happening across the Docker developer community. It’s clear to see that collaborative application development platforms are the foundation for developers who want to build, share, and run modern apps. We are also thrilled to see this type of growth more than one year after refocusing Docker on making developers’ lives easier. The Docker community has stayed with us and continues to grow at a tremendous pace, giving us very encouraging signals about the path that Docker is taking.

To begin, there has now been a total of 318 billion all time pulls on Docker Hub, an increase of 145% year-over-year. That’s right, the total number of pulls has increased by nearly 1.5x in the past year. In addition, there were nearly 30 billion Docker Hub pulls in our fourth quarter. This could very well be because essentially all businesses had to rapidly shift to digital, making the demand for great apps higher than ever.

Those large pull numbers were driven by an equally large number of Docker users. There are now 7.3 million total Docker Hub accounts, up approximately 45% year-over-year. It’s great to see more and more developers signing up for a Free, Pro or Team subscription, underscoring the value they find in new subscription features we have rolled out such as audit logs, vulnerability scanning and higher rate limits.

There are approximately 8.3 million application container image repos on Hub, representing a nearly 40% year-over-year increase in the application components that developers rely on to build apps. Docker Hub remains the world’s largest library and community for trusted, high-quality images and we are so proud of its growth. This rich content is one of the reasons that JFrog decided to partner with us, as premium access to Docker Official Images was strategic to their customers.

Installations of Docker Desktop have now reached 3.3 million, which is a year-over-year increase of approximately 38%. This underscores the importance of giving developers choice and meeting them where they are. Docker Desktop remains unique in the market as the leading platform for developers working locally on their desktop, accelerating inner loop development speed.

Docker has been hard at work continuing to release features that make life easier for developers so that they can get back to the fun part of their work – building and sharing awesome apps. You can expect the next Docker Index to be released mid-year after DockerCon Live 2021 with a fresh set of usage and growth stats fueled by our amazing users!

For further reading, check out Part 1 and Part 2 of the top Docker blog posts of 2020 and this post on 2021 developer trends and predictions by Docker CEO Scott Johnston.
The post Docker Index Shows Continued Massive Developer Adoption and Activity to Build and Share Apps with Docker appeared first on Docker Blog.
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