HomeGoogle BigQuery for MarketersModule 1 BigQuery Tutorial Google BigQuery for Marketing Reporting: Benefits and Use Cases Santiago Cardozo Marketing Manager at Porter March 19, 2026 Google BigQuery changes what is possible for marketing teams when it comes to reporting. Instead of pulling data manually from each platform, you centralize everything in one place and query it together. Here are the main benefits and the real use cases where BigQuery makes a difference. Consolidate Data From More Than 10 Sources Into One Table Most marketing teams run campaigns across multiple platforms: Meta Ads, Google Ads, TikTok Ads, LinkedIn Ads, Google Analytics

HomeGoogle BigQuery for MarketersModule 1 BigQuery Tutorial What Is an ETL Tool? Santiago Cardozo Marketing Manager at Porter March 19, 2026 ETL stands for Extract, Transform, Load. An ETL tool is software that automates those three steps: it pulls data from your source systems, cleans and formats it, and sends it to your destination. For marketing teams, the source systems are your ad platforms, CRM, and analytics tools. The destination is typically a data warehouse like Google BigQuery. The Three Steps of ETL Each letter in ETL represents one step in the data pipeline. Extract: The ETL tool connects to

HomeGoogle BigQuery for MarketersModule 1 BigQuery Tutorial What Is a Marketing Data Pipeline? Santiago Cardozo Marketing Manager at Porter March 19, 2026 A marketing data pipeline is the automated process that extracts your data from where it is created and delivers it to where you need to use it. Your marketing data starts in platforms like Meta Ads, Google Ads, Google Analytics, and Shopify. A marketing data pipeline takes that data, moves it, and loads it into a central location like Google BigQuery, where you can query and analyze it. Without a pipeline, you export data manually from each platform,

HomeGoogle BigQuery for MarketersModule 5 BigQuery Tutorial How to Connect E-Commerce Data to Google BigQuery Santiago Cardozo Marketing Manager at Porter March 19, 2026 Connecting your e-commerce platform to Google BigQuery lets you calculate true ROAS, track revenue by product and campaign, and understand which marketing channels drive actual purchases. Platform-reported conversion values are estimates. Your Shopify data is the ground truth. Here is how to connect your e-commerce data to BigQuery and build reports that show real marketing performance. Why E-Commerce Data Belongs in BigQuery Ad platforms attribute revenue using their own models. Meta uses a 7-day click, 1-day

HomeGoogle BigQuery for MarketersModule 5 BigQuery Tutorial How to Connect CRM Data to Google BigQuery for Marketing Reports Santiago Cardozo Marketing Manager at Porter March 19, 2026 Connecting your CRM to Google BigQuery closes the gap between marketing activity and business results. Instead of reporting on clicks and conversions, you report on leads, pipeline, and revenue. You see which campaigns generate customers, not just form submissions. Here is how to connect HubSpot or Salesforce to BigQuery and build a full-funnel marketing report. Why CRM Data Changes Your Marketing Reports Ad platforms report conversions based on their own attribution models. Meta

HomeGoogle BigQuery for MarketersModule 5 BigQuery Tutorial How to Create a Social Media Report in Google BigQuery Santiago Cardozo Marketing Manager at Porter March 19, 2026 A social media report in BigQuery consolidates your Meta Ads, TikTok Ads, and LinkedIn Ads data into one place. You see total reach, engagement, spend, and conversions across all social platforms in a single query, without switching between platform dashboards. Here is how to build it. What a BigQuery Social Media Report Covers A social media report in BigQuery typically includes: Reach and impressions: how many people saw your content across each platform. Engagement:

HomeGoogle BigQuery for MarketersModule 5 BigQuery Tutorial How to Create a PPC Report in Google BigQuery Santiago Cardozo Marketing Manager at Porter March 19, 2026 A PPC report in Google BigQuery gives you a single view of all your paid advertising performance across Meta Ads, Google Ads, TikTok, and LinkedIn. Instead of logging into each platform separately, you query all your ad data in one place and build a report that shows the full picture. Here is how to build it step by step. What a BigQuery PPC Report Includes A PPC report in BigQuery typically covers: Spend by platform:

HomeGoogle BigQuery for MarketersModule 5 BigQuery Tutorial How to Create a Multi-Client Marketing Report in BigQuery Santiago Cardozo Marketing Manager at Porter March 19, 2026 If you manage marketing for multiple clients, BigQuery lets you consolidate all client data into one database and build reports that work for every client from a single data source. Instead of building separate pipelines and dashboards for each client, you build one system that scales. Here is how to set it up. The Multi-Client Challenge Most marketing agencies manage 10, 20, or 50 clients. Each client has their own Meta Ads account, their own

HomeGoogle BigQuery for MarketersModule 4 BigQuery Tutorial SQL Joins in BigQuery: How to Combine Marketing Data Tables Santiago Cardozo Marketing Manager at Porter March 19, 2026 A JOIN is the SQL operation that merges two or more tables based on a shared column. In BigQuery, you use JOINs to combine your marketing data sources: Meta Ads with Google Ads, ad data with CRM data, campaign data with revenue data. Understanding JOINs is one of the most valuable SQL skills for marketing analysts. Here is how they work. The Concept: Venn Diagrams and Sets The easiest way to understand SQL JOINs

HomeGoogle BigQuery for MarketersModule 4 BigQuery Tutorial How to Create Custom Dimensions in BigQuery With SQL Santiago Cardozo Marketing Manager at Porter March 19, 2026 A custom dimension is a label you assign to rows in your BigQuery table based on conditions you define. Instead of reporting on raw campaign names, you group campaigns into categories, regions, product lines, or any other classification that makes sense for your reporting. The SQL tool for creating custom dimensions is CASE WHEN. What Is a Custom Dimension? Your ad platform gives you dimensions like campaign name, ad set name, country, and device type.

HomeGoogle BigQuery for MarketersModule 4 BigQuery Tutorial SQL for Marketers: BigQuery Basics You Need to Know Santiago Cardozo Marketing Manager at Porter March 19, 2026 SQL is the language you use to query data in Google BigQuery. You do not need a computer science background to use it. The core operators are simple, and once you understand them, you can extract exactly the marketing data you need. This guide covers the SQL fundamentals every marketer needs to work with data in BigQuery. Think of SQL Like a Google Sheet The easiest way to understand SQL is to compare it to

HomeGoogle BigQuery for MarketersModule 4 BigQuery Tutorial Data Blending in Google BigQuery: How to Combine Marketing Data Sources Santiago Cardozo Marketing Manager at Porter March 19, 2026 Data blending in BigQuery means combining multiple marketing data sources into one unified table. Instead of looking at Meta Ads in one report and Google Ads in another, you merge them into a single table that shows total spend, blended ROAS, cross-channel attribution, and real CPA across all platforms. Here is how it works and how to do it. What Data Blending Achieves When you blend Meta Ads, Google Ads, and Google Analytics

HomeGoogle BigQuery for MarketersModule 3 BigQuery Tutorial How to Send and Monitor Your Marketing Data in BigQuery Santiago Cardozo Marketing Manager at Porter March 19, 2026 After configuring your data sources, metrics, dimensions, date range, filters, write mode, and schedule in Porter Metrics, you are ready to send your data to BigQuery. This is the final step in setting up your marketing data pipeline. Here is what happens when you click “Send” and how to monitor your pipeline after it is live. Previewing Your Table Before Sending Before you send data to BigQuery, Porter shows you a preview of the

HomeGoogle BigQuery for MarketersModule 3 BigQuery Tutorial BigQuery Write Modes Explained: Overwrite, Append, and Update Santiago Cardozo Marketing Manager at Porter March 19, 2026 When Porter Metrics loads data into your BigQuery table, it follows a write mode that you set. The write mode controls what happens to the existing data in your table when new data arrives. There are three options: overwrite, append, and update. Choosing the right one depends on how you want to manage your data over time. Write Mode 1: Overwrite Overwrite deletes all existing rows in your table and replaces them with the new data

HomeGoogle BigQuery for MarketersModule 3 BigQuery Tutorial How to Schedule Your Marketing Data Pipeline to BigQuery Santiago Cardozo Marketing Manager at Porter March 19, 2026 Once your Porter Metrics query is configured, you set a schedule that tells Porter when to run. Every day at that time, Porter extracts fresh data from your marketing platforms and loads it into your BigQuery table automatically. Here is how to configure your schedule and what to consider when choosing your timing. Setting Your Schedule in Porter Metrics In your Porter query configuration, click “Schedule.” You see a text field where you type the

HomeGoogle BigQuery for MarketersModule 3 BigQuery Tutorial How to Set Date Ranges, Filters, and Sorting in BigQuery With Porter Metrics Santiago Cardozo Marketing Manager at Porter March 19, 2026 Before your marketing data lands in BigQuery, you control exactly what data goes in. Porter Metrics lets you set date ranges, apply filters, and configure sorting directly in the interface. No SQL required at this stage. Here is how each setting works. Setting Your Date Range The date range determines how far back Porter pulls data when it loads your table. By default, Porter uses the last 7 days. You change

HomeGoogle BigQuery for MarketersModule 3 BigQuery Tutorial How to Select Metrics and Dimensions for BigQuery With Porter Metrics Santiago Cardozo Marketing Manager at Porter March 19, 2026 When you send marketing data to BigQuery, you choose which metrics and dimensions to include. This determines what columns appear in your BigQuery table and what you can analyze once the data lands. Porter Metrics gives you two advantages here that you do not get when loading data manually: pre-calculated blended metrics and automatic custom calculations. Metrics vs Dimensions: The Difference Before selecting your data, it helps to understand the difference between metrics

HomeGoogle BigQuery for MarketersModule 3 BigQuery Tutorial How to Set Up Your BigQuery Project ID, Dataset, and Table Name Santiago Cardozo Marketing Manager at Porter March 19, 2026 After you connect your marketing accounts and authenticate BigQuery in Porter Metrics, the next step is selecting where your data will land. This means choosing your BigQuery Project ID, your dataset location, your dataset name, and your table name. Here is what each setting means and how to configure it correctly. Step 1: Select Your Project ID Your Project ID is the unique identifier for your Google Cloud project. It looks something