Building data insights using Google BigQuery скачать в хорошем качестве

Building data insights using Google BigQuery

Join this channel to get access to perks:    / @fsethi   #techlead , #google #fabric #datascience #dataanalyst #gcp #bigquery Check out my website https://FSethi.com and LinkedIn   / farhana-sethi-1230a817   Building data insights using Google BigQuery can be a powerful way to analyze and visualize your data. Here's a step-by-step guide to help you get started: 1. Load Your Data Load your data into BigQuery from various sources such as CSV files, Google Sheets, or other databases. You can use the BigQuery web UI, the bq command-line tool, or the BigQuery API for this. 2. Clean and Prepare Your Data Clean and prepare your data by removing duplicates, handling missing values, and ensuring the data types are correct. You can use SQL queries to perform these tasks directly in BigQuery. 3. Analyze Your Data Analyze your data using SQL queries to uncover patterns, trends, and insights. Here are some common analysis techniques: Aggregations (e.g., SUM, AVG, COUNT) Filtering (e.g., WHERE clause) Joining tables (e.g., JOIN clause) Grouping (e.g., GROUP BY clause) Window functions (e.g., OVER clause) 4. Visualize Your Data Visualize your data to make the insights more understandable and actionable. You can use tools like Google Data Studio, Looker, or other BI tools that integrate with BigQuery. These tools allow you to create interactive dashboards and reports. 5. Build Machine Learning Models If you want to go a step further, you can use BigQuery ML to build and train machine learning models directly within BigQuery using SQL. This can help you make predictions and identify complex patterns in your data. 6. Automate and Schedule Queries Automate and schedule queries to run at regular intervals using BigQuery's scheduling features. This ensures that your data insights are always up-to-date. Example Query Here's an example SQL query to get you started with basic data analysis: sql SELECT product_category, COUNT(order_id) AS total_orders, SUM(order_amount) AS total_revenue FROM `your_project_id.your_dataset_id.your_table_id` WHERE order_date BETWEEN '2023-01-01' AND '2023-12-31' GROUP BY product_category ORDER BY total_revenue DESC; This query aggregates total orders and total revenue by product category for the year 2023. Documentation and Resources BigQuery Documentation https://cloud.google.com/bigquery/docs BigQuery SQL Reference https://cloud.google.com/bigquery/doc... Google Data Studio https://lookerstudio.google.com/u/0/n... Looker https://cloud.google.com/looker By following these steps, you can effectively build data insights using Google BigQuery. 🎥 MY YOUTUBE GEAR • Computer - https://www.amazon.com/Microsoft-Surf... • My Camera - https://www.amazon.com/Microphone-DEP... • Main Microphone - https://amzn.to/3t1n1aK • Azure Cognitive Services Speech Studio - Voice Farhana SethiNeural https://speech.microsoft.com/portal • Generative AI https://openai.com/product/dall-e-2 • ChatGPT customized AI model • Adobe Premiere Pro | Video Editing and Production Software - https://www.amazon.com/Adobe-Premiere... • Main Tripod - https://amzn.to/3pSUeDh Teleprompter - https://amzn.to/3hWGT8p