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Analyzing logs in real time using Fluentd and BigQuery

This tutorial shows how to log browser traffic and analyze it in real time. This is useful when you have a significant amount of logging from various sources and you want to debug issues or generate up-to-date statistics from the logs. The tutorial describes how to send log information generated by an NGINX web server to BigQuery using Fluentd, and then use BigQuery to analyze the log information. It assumes that you have basic familiarity with Google Cloud Platform (GCP), Linux command lines, application log collection, and log analysis. Introduction Logs are a powerful tool for providing a view into how large-scale … Continue reading Analyzing logs in real time using Fluentd and BigQuery

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Use Of Cloud IoT Core

Use IoT Core to create a registry Use IoT Core to create a device Use Stackdriver Logging to view device logs Enable APIs In this section, you check that all the APIs you will use in this lab are enabled. In the GCP Console, on the Navigation menu (), click APIs & Services. Scroll down and confirm that your APIs are enabled. Cloud IoT API Cloud Pub/Sub API Container Registry API If an API is disabled, click Enable APIs and services at the top, search for the API by name, and enable it for your project. Make sure you are in the correct Qwiklabs project. … Continue reading Use Of Cloud IoT Core

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Build A Tool in the Google docs that read the sentiment of your document by using Google’s Natural Language API

The Natural Language API is a pretrained machine learning model that can analyze syntax, extract entities, and evaluate the sentiment of text. It can be called from Google Docs to perform all of these functions. This post will walk you through calling the Natural Language API to recognize the sentiment of selected text in a Google Doc and highlight it based on that sentiment. What are we going to be building? Once this post is complete, you will be able to select text in a document and mark its sentiment, using a menu choice, as shown below. Text will be highlighted in … Continue reading Build A Tool in the Google docs that read the sentiment of your document by using Google’s Natural Language API

How To Set up a Development Environment in Python in Google Cloud

In this blog, you set up a Python development environment on Google Cloud Platform, using Google Compute Engine to create a virtual machine (VM) and installing software libraries for software development. You perform the following tasks: Provision a Google Compute Engine instance. Connect to the instance using SSH. Install a Python library on the instance. Verify the software installation. Compute Engine is just one resource provided on Google Cloud Platform. Google Cloud Platform Google Cloud Platform (GCP) consists of a set of physical assets, such as computers and hard disk drives, and virtual resources, such as virtual machines (VMs), that … Continue reading How To Set up a Development Environment in Python in Google Cloud

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Building an IoT Analytics Pipeline on Google Cloud Platform step by step

let’s start with the definition of IoT: The term Internet of Things (IoT) refers to the interconnection of physical devices with the global Internet. These devices are equipped with sensors and networking hardware, and each is globally identifiable. Taken together, these capabilities … Continue reading Building an IoT Analytics Pipeline on Google Cloud Platform step by step

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TensorFlow Machine Learning on the Amazon Deep Learning AMI

TensorFlow is a popular framework used for machine learning. The Amazon Deep Learning AMI comes bundled with everything you need to start using TensorFlow from development through to production. In this post, you will develop, visualize, serve, and consume a TensorFlow machine learning model using the Amazon Deep Learning AMI.  Objectives Upon completion of this post you will be able to: Create machine learning models in TensorFlow Visualize TensorFlow graphs and the learning process in TensorBoard Serve trained TensorFlow models with TensorFlow Serving Create clients that consume served TensorFlow models, all with the Amazon Deep Learning AMI Prerequisites You should be familiar … Continue reading TensorFlow Machine Learning on the Amazon Deep Learning AMI

How To Scale multiple Azure SQL Databases with SQL elastic pools

What is SQL elastic pool? SQL Database elastic pools are a simple, cost-effective solution for managing and scaling multiple databases that have varying and unpredictable usage demands. The databases in an elastic pool are on a single Azure SQL Database server and share a set number of resources at a set price.  Elastic pools in Azure SQL Database enable SaaS developers to optimize the price performance for a group of databases within a prescribed budget while delivering performance elasticity for each database. Details: SaaS developers build applications on top of large scale data-tiers consisting of multiple databases. A common application pattern is to provision a single database … Continue reading How To Scale multiple Azure SQL Databases with SQL elastic pools

Big Data and Machine Learning Fundamentals using GCP for Data Professionals.

You Will get a brief overview of GCP and tools that power machine learning and Big data in GCP. So What Is GCP? GCP stands for Googles cloud platform ….means in a simple way Googles offering of could solutions . Now I hope that you have understanding about what is cloud. If you ever working in AN organizations that have there own data center you have a bit idea about cloud . It simply means giving access to do what you wanna do with a machine but restricts your physical level access. So how you as an organisation get benefit … Continue reading Big Data and Machine Learning Fundamentals using GCP for Data Professionals.