Introduction to Cloud Data Analytics with Google BigQuery

Learn how a data warehouse centralizes data from multiple sources for reporting and analysis. Discover the extraction, cleaning, converting, and consolidation steps that prepare data for business intelligence.

Explore the three main types of a data warehouse: enterprise data warehouse, operational data storage, and data mart. See how centralized, subject-based data and real-time integration enable rapid reporting.

Explore data warehouse architectures, including staging areas and data marts, and learn how ETL processes from operational systems feed online analytical processing, reporting, and forecasting.

Explore data warehouse components, including process manager, load manager, and warehouse manager, gateways, and query manager, and apply data warehouse modeling with conceptual, logical, and physical models.

Compare databases and data warehouses to show how data warehouses store large data sets from multiple sources and enable statistical results for informed decisions.

Explore how Google's BigQuery enables fully managed, scalable interactive analytics on massive datasets, with web UI, command line, and REST API access, from data collection to transformation and business intelligence.

Explore BigQuery architecture, including columnar storage and full-scan queries, and learn cost optimization and security considerations with datasets, jobs, and access controls such as IAM.

Explore BigQuery features, including serverless architecture and multi-cloud analytics via standard APIs and multi-language client libraries, with public data sets, real-time insights, and SQL-based machine learning.

Leverage big data analytics with BigQuery to avoid slow server setups by providing scalable, managed, secure processing of time series and real-time data, enabling fast queries and extensive analytics.

Extract, transform, and load (ETL) processes collect data from multiple sources into a staging area, cleanse and normalize it, then load it into the data warehouse for analysis.

Explore the edl process in a data warehouse, using in-place transformations to speed queries, while enabling incremental loading, log replication, and fault-tolerant data integration across diverse sources.

Explore etl tools and their role in transforming data, illustrating how data warehousing solutions like Mark Logic, Oracle, and Amazon Redshift streamline integration, automation, and reporting.

Learn to perform business analytics securely on Google Cloud Platform with BigQuery, addressing performance challenges when analyzing larger datasets and highlighting security as a scalable solution.

Sign up for a Google account and start the Google Cloud Platform free trial with $300 credit for 12 months, then explore the security services.

Familiarize yourself with the Google Cloud Platform's BQ GUI by creating a new project, exploring the left pane with job history, datasets, and transfers, and preparing for live exercises.

Learn to create a new BigQuery project, set a project name and ID, and choose a budget. Open the project to see it contains public data and is currently empty.

Create a new dataset in BigQuery, name it, and upload a CSV from your local drive. BigQuery detects the schema, lets you preview data, and sets up for basic analysis.

Practice performing sql queries in google bigquery by loading a sales dataset, selecting columns such as region, and computing sums in milliseconds, showing faster results than Excel.

Learn how to group data by region to calculate total profit across regions using group by, view results, rename the output, and save analyses for future use.

Learn to join two datasets by a common order ID to create a single dataset, enabling analysis of orders, costs, and total profit across regions.

Import two datasets from Google Drive, delete previous data as needed, then link dispatch and accounts tables by order ID to enable combined analytics in BigQuery.

Join two datasets in BigQuery using a left join on a common key to produce a unified output. Save the results to Google Drive.

Create a Google Cloud Storage bucket, upload data to it, configure access and encryption, then load the data into a BigQuery dataset and table for analysis.

Compute the minimum and maximum total profit from the dataset, then analyze profit by item type using group by and sum, revealing fruits as minimum and cosmetics as maximum.

Explore item type and sales channel analysis in BigQuery, comparing online and offline sales, counting items, calculating profit, and grouping results to reveal revenue patterns.

Explore Google Cloud SDK, a secure toolkit with language-specific libraries that reduces boilerplate, enabling cloud storage management, querying datasets, tables, and entities from the command line, and deploying apps.

Install and configure the Google Cloud SDK on your machine, complete download and installer steps, sign in to access projects, and use commands like gcloud version, auth list, and info.

Learn to load local data into BigQuery via the cloud cli, create a dataset and table, upload files, and define schemas manually or automatically with the bq tool.

Learn to run sql queries with the bq cli tool using cloud sdk to query the baby names dataset, filter by gender, order by count, and limit to five.

Master how to create and manage cloud storage buckets with gsutil, upload and download data using the Google Cloud SDK, and inspect bucket contents.

Data Studio turns data into informative dashboards and reports with interactive visuals. Use 18 connectors, including Google Sheets and Google Analytics, and gallery templates to share dashboards.

Learn to load data into Data Studio from a project, create a blank report, select the sales record type, and add the dataset to generate analytics and a final report.

Analyze item type versus total profit in Google Data Studio, using drag-and-drop to visualize data and compare profits; the cosmetic item proves most profitable among fifty thousand records.

Compare online and offline sales by item type using the exploded view and tabulated data in Data Studio, adjust dimensions, and validate results across visuals.

Explore how to build an interactive data studio report with dashboards, region and item filters, and time-based insights on sales, profit, units, and demand using 2010–2017 data.

Load the dataset into Google Data Studio and analyze it graphically by adding dimensions such as total revenue and profit, then view a histogram by region to reveal insights.

Explore how to build and style reports in Data Studio using Google Map integration and bubble maps, display regional profits and margins with charts, scorecards, and histograms for sharing.

Add line graphs and control buttons to a cloud data analytics report in BigQuery, visualizing units sold and total revenue by order date with region and item filters.

Create and customize a multi-page sales report by adding scorecards and graphs, applying global and page-specific filters, and setting cross-page controls to display regional revenue and profit.

Apply formulas and date ranges in data studio to compute minimum and maximum profits by item type, visualize with a pie chart, and filter by region and date.