David Nguyeneplus.dev·6 hours agoManage Kubernetes in Google Cloud: Challenge LabIntroduction In a challenge lab you’re given a scenario and a set of tasks. Instead of following step-by-step instructions, you will use the skills learned from the labs in the course to figure out how to complete the tasks on your own! An automated ...DiscussTip & TricksKubernetes
Raghuveer Sriramanraghuveer.me·Mar 24, 2024Using Google Cloud Run to process batch jobsCloud Run helps you deploy containerized workloads at scale. Using it as a backend for web server use cases is quite well known, but it also can be very useful for large batch jobs that tend to be CPU heavy, especially if the job can be divided into ...DiscussGoogle Cloud Platform
Constantin Lungudatawise.dev·Mar 21, 2024RANGE data type in BigQueryI work quite a lot with temporal/SCD2 type table so the new (still in preview) RANGE data type in BigQuery (and its supporting methods) are a welcome addition. What does it do? So instead of storing valid_from & valid_to in separate columns, we n...Discuss·59 readsPractical BigQuerySQL
Sourav Dhimandsourav155.hashnode.dev·Mar 20, 2024Newbie's View of Google Cloud ServicesIntroduction Google Cloud Platform (GCP) provides a massive and ever-expanding toolkit for building, deploying, and managing applications at scale. Understanding these services is essential for any organization or developer exploring cloud-based solu...DiscussGoogle
Shiv IyerProshiviyer.hashnode.dev·Mar 14, 2024How to Use the PIVOT Operator in Google BigQuery to Convert Rows to Columns for Better Data AnalysisImplementing the PIVOT operator in Google BigQuery allows you to transform rows into columns, effectively reshaping your data for analysis that requires a more traditional spreadsheet-like view where each row represents a unique category and each col...DiscussGCP
Shiv IyerProshiviyer.hashnode.dev·Mar 14, 2024How to Combine BigQuery, TensorFlow, and AI Platform for Complete Machine Learning ProcessesIntegrating BigQuery with TensorFlow and AI Platform for building and deploying machine learning models involves a combination of data management, model training, and deployment strategies that utilize the strengths of each platform. Below, I’ll illu...Discuss·7 likesgoogle cloud
Mileke Kolawolewtfiscloud.hashnode.dev·Mar 5, 202426. CI/CD with Google Cloud Artifact RegistryIntroduction In our previous exploration, we streamlined deployments with Google Cloud Deploy. These deployment pipelines rely on outputs from your CI/CD process, typically build artifacts like container images, packages, or other distributable files...DiscussArtifact Registry
Mileke Kolawolewtfiscloud.hashnode.dev·Feb 23, 202418. Deploying Applications on Kubernetes: Understanding Deployments and ServicesFundamentals of Deployments and Services Introduction In our last article, we learned how to set up GKE clusters in two different modes. While having a Kubernetes cluster up and running is a major milestone, it is similar to having a well set-up stag...Discussgoogle cloud
Mileke Kolawolewtfiscloud.hashnode.dev·Feb 23, 202417. Setting up your Google Kubernetes ClusterIntroduction In our previous article, we explored the benefits of Google Kubernetes Engine (GKE) and the distinction between its Autopilot and Standard modes. Now, it's time for action! In this article, we'll go through the process of setting up your...DiscussKubernetes
Constantin Lungudatawise.dev·Feb 22, 2024Why you should care about partition pruning in BigQueryWhen it comes to performance improvements and cost savings, handling only as much data as we need is very important. And partitioning is a cornerstone here. Now, when working with tables that are partitioned, BigQuery tries to exclude the partition...Discuss·52 readsPractical BigQuerybigquery