Google BigQuery : The 2024 Ultimate Guide for Data pros

Explore how BigQuery serves as a cloud-based SQL database that processes large data quickly. Learn about projects, datasets, tables, standard SQL, and features like billing, saved queries, and scheduled queries.

Set up a demo BigQuery account, claim $300 free credit, create your first project and dataset in the european union, and access public data sets for hands-on exercises.

Discover SQL basics, including tables, fields, and data types like integers and strings. Learn to join tables on common fields, use select from where, and apply optional aliases using as.

Learn to write queries with group by for aggregations such as sums, counts, and averages, use where and having clauses, and apply order by and limit in BigQuery.

Master SQL comparison operators including equals, greater than, less than, not equal, in, and between to filter data in where clauses and handle date ranges with practical examples and joins.

Explore sql joins, including inner joins, and left, right, and full outer joins, with two-table examples in BigQuery, and learn how to apply select, from, and on.

Master key functions in BigQuery, including casting between boolean, numeric, date, time, and timestamp types, and using string tools like concat, length, lower, upper, trim, replace, substring, and split.

Explore Google BigQuery exercises on the Austin crime table, using trim and length to clean descriptions, cast and split to extract dates, and date functions for formatting.

Master date truncation, format date, and parse date in BigQuery, and apply conditional expressions, subqueries with clauses, regexp_contains, and ranking for advanced data analysis.

Explore creating or replacing tables in BigQuery, including syntax and column naming, then build functions with declare variables and use them to filter data and categorize values.

Master best practices in Google BigQuery by using the with clause to simplify subqueries, monitor costs with Chrome extensions, and centralize dates with variables and comments for clean, shareable queries.

Explore core BigQuery concepts, including data types and modes, and learn to work with views and scheduled queries—creating, editing, scheduling, and managing destination tables with UTC timing and alerts.

Explore how partitioned and sharded tables in BigQuery divide data by date to boost performance. Learn how to inject data via csv, json, or Google Sheets.

Learn to insert and delete rows in BigQuery tables, export query results to Google Sheets and Google Cloud Storage, and visualize data with Looker Studio and charts.

Explore how to export BigQuery data using Cloud Functions in Google Cloud Platform, triggering Python code that processes CSV files in Cloud Storage and writes results back to BigQuery.

Explore nested tables in BigQuery, identify arrays within rows using record and repeated fields, and flatten them with unnest, with practical examples from GitHub and Bitcoin datasets.

Explore GA4 and GA360 analytics tables in BigQuery, focusing on sharded, nested structures and how to unnest event params, user properties, and items to extract revenue and e-commerce insights.

Explore Looker Studio's native BigQuery connection to build dashboards and reports with connectors, custom queries, and parameters for date-range filtering on the Austin crime table's top five crimes bar chart.

Explore data engineering with python for bigquery by connecting via a jupyter notebook, authenticating with a service account, running queries, and exporting data frames to bigquery.

Vote on a deeper second part via the PDF link to a Google form; once we reach 100 requests, I'll create a more detailed training and welcome questions.