Vignesh C's Projects
Launch a frontend and multpile backend services on AWS Elastic Container Service. AWS Fargate used for serverless compute engine for containers.
Amazon Web Services is a subsidiary of Amazon providing on-demand cloud computing platforms and APIs to individuals, companies, and governments, on a metered pay-as-you-go basis. Build, Deploy, and Manage Websites, Apps or Processes On AWS' Secure, Reliable Network.
Connect and manage MQTT-based devices using Cloud IoT Core (using simulated devices) Ingest a stream of information from Cloud IoT Core using Cloud Pub/Sub. Process the IoT data using Cloud Dataflow. Analyze the IoT data using BigQuery.
A small microservices ecosystem, showcasing an order management workflow, such as one might find in retail and online shopping. It is built using Kafka Streams, whereby business events that describe the order management workflow propagate through this ecosystem.
Devops Tutorial for Beginners Docker, Kubernetes, Terraform, Ansible, Jenkins and Azure Devops
Simple Java, Nodejs and Python programs built and run using docker container
Deploy and manage containerized applications on Google Kubernetes Engine (GKE) and the other tools on Google Cloud. deploy solution elements—including infrastructure components like pods, containers, deployments, and services—along with networks and application services. deploy practical solutions, including security and access management, resource management, and resource monitoring.
We will be using a sample application that we have called Vote-App which is representative of a N-Tier microservice architecture, containerized application that will be deployed to a Kubernetes cluster provided by GKE.
AutoML Vision provides an interface for all the steps in training an image classification model and generating predictions on it.
Build a BI dashboard with Data Studio as the front end, powered by BigQuery on the back end.
Create a Machine Learning model in BigQuery to predict whether or not a new user is likely to purchase in the future.
Use BigQuery to find public datasets. Query and explore the public taxi cab dataset. Create a training and evaluation dataset to be used for batch prediction. Create a forecasting (linear regression) model in BigQuery ML. Evaluate the performance of your machine learning model.
Configure Cloud Data Fusion Create a Cloud Data Fusion data transformation pipeline Connect Cloud Data Fusion to a couple of data sources Apply basic transformations Join the two data sources using Cloud Data Fusion Split data to perform an A/B experiment Write data to a sink
Explore the Cloud Dataprep UI to build a data transformation pipeline that runs at a scheduled interval and outputs results into BigQuery
Build a continuous integration pipeline using Cloud Source Repositories, Cloud Build, build triggers, and Container Registry. Deploy applications to the Google Cloud services App Engine, Kubernetes Engine, and Cloud Run.
Create a Teradata source system running on a Compute Engine instance Prepare the Teradata source system for the schema and data transfer Configure the schema and data transfer service Migrate the schema and data from Teradata to BigQuery Translate Teradata SQL queries into compliant BigQuery Standard SQL
Launch a Kafka instance and use it to communicate with Pub/Sub. Configure a Kafka connector to integrate with Pub/Sub Setup topics and subscriptions for message communication Perform basic testing of both Kafka and Pub/Sub services Connect IoT Core to Pub/Sub
Kubernetes is an open-source container-orchestration system for automating computer application deployment, scaling, and management. Kubernetes Helps to Build, Deliver, and Scale Containerized Apps.
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