filmov
tv
Introduction to SQL for BigQuery and Cloud SQL
Показать описание
We would like ❤️ to advise you that you must subscribe to the Qwiklabs YouTube official channel, Bookmark the Qwiklabs and online Inter college website so that you can get information about upcoming events.
You must complete a series of tasks within the allocated time period. Instead of following step-by-step instructions, you'll be given a scenario and a set of tasks - you figure out how to complete it on your own! An automated scoring system (shown on this page) will provide feedback on whether you have completed your tasks correctly.
other channels
_________________________________________________
Leave a LIKE ❤️ and SUBSCRIBE !!
________________________________________________________
Challenged labs links :--
Create and Manage Cloud Resources :
Perform Foundational Infrastructure Tasks in Google Cloud
Build and Secure Networks in Google Cloud
Deploy to Kubernetes in Google Cloud
Set Up and Configure a Cloud Environment in Google Cloud
Implement DevOps in Google Cloud
Ensure Access & Identity in Google Cloud
Build Interactive Apps with Google Assistant
Integrate with Machine Learning APIs
Build a Website on Google Cloud
Perform Foundational Data, ML, and AI Tasks in Google Cloud
Insights from Data with BigQuery
Create ML Models with BigQuery ML
Engineer Data in Google Cloud
________________________________________________
_________________________________________________
Overview
SQL (Structured Query Language) is a standard language for data operations that allows you to ask questions and get insights from structured datasets. It's commonly used in database management and allows you to perform tasks like transaction record writing into relational databases and petabyte-scale data analysis.
This lab serves as an introduction to SQL and is intended to prepare you for the many labs and quests in Qwiklabs on data science topics. This lab is divided into two parts: in the first half, you will learn fundamental SQL querying keywords, which you will run in the BigQuery console on a public dataset that contains information on London bikeshares.
In the second half, you will learn how to export subsets of the London bikeshare dataset into CSV files, which you will then upload to Cloud SQL. From there you will learn how to use Cloud SQL to create and manage databases and tables. Towards the end, you will get hands-on practice with additional SQL keywords that manipulate and edit data.
Objectives
In this lab, you will learn how to:
Distinguish databases from tables and projects.
Use the SELECT, FROM, and WHERE keywords to construct simple queries.
Identify the different components and hierarchies within the BigQuery console.
Load databases and tables into BigQuery.
Execute simple queries on tables.
Learn about the COUNT, GROUP BY, AS, and ORDER BY keywords.
Execute and chain the above commands to pull meaningful data from datasets.
Export a subset of data into a CSV file and store that file into a new Cloud Storage bucket.
Create a new Cloud SQL instance and load your exported CSV file as a new table.
Run CREATE DATABASE, CREATE TABLE, DELETE, INSERT INTO, and UNION queries in Cloud SQL.
You must complete a series of tasks within the allocated time period. Instead of following step-by-step instructions, you'll be given a scenario and a set of tasks - you figure out how to complete it on your own! An automated scoring system (shown on this page) will provide feedback on whether you have completed your tasks correctly.
other channels
_________________________________________________
Leave a LIKE ❤️ and SUBSCRIBE !!
________________________________________________________
Challenged labs links :--
Create and Manage Cloud Resources :
Perform Foundational Infrastructure Tasks in Google Cloud
Build and Secure Networks in Google Cloud
Deploy to Kubernetes in Google Cloud
Set Up and Configure a Cloud Environment in Google Cloud
Implement DevOps in Google Cloud
Ensure Access & Identity in Google Cloud
Build Interactive Apps with Google Assistant
Integrate with Machine Learning APIs
Build a Website on Google Cloud
Perform Foundational Data, ML, and AI Tasks in Google Cloud
Insights from Data with BigQuery
Create ML Models with BigQuery ML
Engineer Data in Google Cloud
________________________________________________
_________________________________________________
Overview
SQL (Structured Query Language) is a standard language for data operations that allows you to ask questions and get insights from structured datasets. It's commonly used in database management and allows you to perform tasks like transaction record writing into relational databases and petabyte-scale data analysis.
This lab serves as an introduction to SQL and is intended to prepare you for the many labs and quests in Qwiklabs on data science topics. This lab is divided into two parts: in the first half, you will learn fundamental SQL querying keywords, which you will run in the BigQuery console on a public dataset that contains information on London bikeshares.
In the second half, you will learn how to export subsets of the London bikeshare dataset into CSV files, which you will then upload to Cloud SQL. From there you will learn how to use Cloud SQL to create and manage databases and tables. Towards the end, you will get hands-on practice with additional SQL keywords that manipulate and edit data.
Objectives
In this lab, you will learn how to:
Distinguish databases from tables and projects.
Use the SELECT, FROM, and WHERE keywords to construct simple queries.
Identify the different components and hierarchies within the BigQuery console.
Load databases and tables into BigQuery.
Execute simple queries on tables.
Learn about the COUNT, GROUP BY, AS, and ORDER BY keywords.
Execute and chain the above commands to pull meaningful data from datasets.
Export a subset of data into a CSV file and store that file into a new Cloud Storage bucket.
Create a new Cloud SQL instance and load your exported CSV file as a new table.
Run CREATE DATABASE, CREATE TABLE, DELETE, INSERT INTO, and UNION queries in Cloud SQL.
Комментарии