Getting Started with BigQuery SQL
If you or your team are unfamiliar with BigQuery SQL syntax (or new to writing SQL), you'll want to start with this free course from CIFL.
Building a Data Agency
How you can offer "data pipelines as a service" on Google BigQuery, following our end-to-end process
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Table of contents
Getting Started + FAQs
Getting Started + FAQs
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***ALL THE TEMPLATE LINKS***
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**GETTING HELP**
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The business of data, end-to-end
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Why'd we republish this course?
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Who is this for, and what will you learn?
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What is a data pipeline?
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[THROWBACK ALERT] WTF is ADP?
Additional (FREE) CIFL Courses
Additional (FREE) CIFL Courses
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Getting Started with BigQuery SQL
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Data Studio the Lazy Way
0.1 The Sales Flow
0.1 The Sales Flow
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0.1.1 Finding your niche
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0.1.2 Building an inbound content strategy
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0.1.3 Why we arrived at the sprint pricing model
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0.1.4 Why we publish pricing
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0.1.5 The initial sales call
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0.1.6 The roadmapping process
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0.1.7 Deal closing + contracts
0.2 Staffing & Resourcing
0.2 Staffing & Resourcing
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0.2.1 The sprint flow + roles
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0.2.2 Hiring reporting + data modeling analysts
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COMING SOON - Making a staffing plan & budget
1.1 Planning Development Sprints
1.1 Planning Development Sprints
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1.1.0 Meet the Tracking Plan
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1.1.1 Breaking the roadmap into a Tracking Plan
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1.1.2 Mapping our raw data source requirements
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1.1.3 What you'll do with data source schemas
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1.1.4 Mapping out data source schemas
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1.1.5 Populating key starter questions for reporting
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1.1.6 Scoping out each Site or Client
2.1 Data Feeds - Getting Started
2.1 Data Feeds - Getting Started
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2.1.1 Intro to data feeds
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2.1.2 BigQuery initial setup
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2.1.3 Setting up your BigQuery tables
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2.1.4 Pushing data from Sheets to BigQuery
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2.1.5 Supermetrics quickstart for beginners
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2.1.6 Pulling data from unsupported APIs into the Tracking Plan
2.2 Data Feeds - Stitch
2.2 Data Feeds - Stitch
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2.2.1 Stitch initial setup
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2.2.2 Pulling GA data using Stitch
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2.2.3 Pulling Adwords data using Stitch
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2.2.4 Pulling FB Ads data using Stitch
3.1 Intro to dbt
3.1 Intro to dbt
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3.1.1 Intro to dbt for SQL data modeling
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3.1.2 Planning your Data Models
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3.1.3 Creating your dbt project
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3.1.4 Connect your BigQuery database to dbt
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3.1.5 Managing your dbt project via Github
3.2 Data modeling with dbt
3.2 Data modeling with dbt
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3.2.1 Writing your 'processing' level SQL queries
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3.2.2 Writing your 'join' level SQL models
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3.2.3 Sidenote: on debugging dbt models
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3.2.4 Pro tip: standardizing URL structure
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3.2.5 Pro tip: using dbt macros
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3.2.6 Writing your 'admin' level SQL models
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3.2.7 Writing your 'math' and 'visualization' level SQL models
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COMING SOON - Data documentation in dbt
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COMING SOON - Data + schema testing in dbt
3.3 Productionalizing your dbt project
3.3 Productionalizing your dbt project
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3.3.1 Intro to productionalizing your pipeline
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3.3.2 Using dbt cloud to run your SQL models on a schedule
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3.3.3 Scheduling your data pipeline orchestrations
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3.3.4 Testing changes to your data pipeline
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3.3.5 QCing data using Supermetrics as a check
4.1 Visualizations in Data Studio
4.1 Visualizations in Data Studio
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4.1.1 The "PDA" reporting design framework
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4.1.2 Designing reports in the Tracking Plan
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4.1.3 Executing the reporting build
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4.1.4 Reviewing reporting
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4.1.5 Pulling data from BigQuery into Sheets
5.1 Sprint wrapup + review
5.1 Sprint wrapup + review
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5.1.1 Conditions for closing out a sprint
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5.1.2 Wiring up reporting with live data
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5.1.3 Sharing draft models + visualizations with clients
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5.1.4 Transitioning to support mode
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