Data Engineering with dbt
12 chapters
1. dbt for Data Transformation: Clean, Reliable Analytics
[Verse 1]
Your data's raw and messy, scattered all around
Tables full of chaos, insights can't be found
You need a transformation tool to make it clean
That's where dbt comes in, the best you've ever seen
[Chorus]
dbt makes it simple, Transform and Test
Models in SQL, giving you the best
Select Transform Load, but done the modern way
Clean reliable datasets, every single day
D-B-T, your analytics friend
Transform your data from beginning to end
[Verse 2]
Start with models folder, write your SQL code
Each file is a model, on the transformation road
Raw tables become staging, then into marts they flow
Documentation built-in, so everyone can know
[Chorus]
dbt makes it simple, Transform and Test
Models in SQL, giving you the best
Select Transform Load, but done the modern way
Clean reliable datasets, every single day
D-B-T, your analytics friend
Transform your data from beginning to end
[Bridge]
Tests keep data quality high
Unique not null, no need to cry
Freshness checks and custom rules
dbt gives you all the tools
Version control your data code
Dependencies in the right mode
[Verse 3]
Run dbt run to build your models
dbt test to catch the problems
Lineage graphs show data flow
From source to mart, watch it grow
[Chorus]
dbt makes it simple, Transform and Test
Models in SQL, giving you the best
Select Transform Load, but done the modern way
Clean reliable datasets, every single day
D-B-T, your analytics friend
Transform your data from beginning to end
[Outro]
Raw data in, insights out
That's what transformation's all about
With dbt your data's clean and bright
Analytics done right
2. What is Analytics Engineering?
[Verse 1]
Data flows like rivers through our systems every day
Engineers build pipelines, but there's more along the way
Analysts need clean data, but the raw stuff's quite a mess
There's a gap between the two that we need to address
[Chorus]
Analytics Engineering - the bridge between the streams
Transform and model data, make it ready for your dreams
A-E spells the magic, where the pipelines meet analysis
Clean it, test it, document it - that's our emphasis
[Verse 2]
Not just moving data, not just building charts and graphs
We're the ones who clean and shape, do the transformation math
SQL is our paintbrush, DBT our favorite tool
Making messy data tidy with a systematic rule
[Chorus]
Analytics Engineering - the bridge between the streams
Transform and model data, make it ready for your dreams
A-E spells the magic, where the pipelines meet analysis
Clean it, test it, document it - that's our emphasis
[Bridge]
We sit right in the middle of the modern data stack
Taking raw material, giving cleaned data back
Data quality guardian, making sure it's always right
Version control and testing keep our models burning bright
[Verse 3]
Dimensional modeling, creating tables that make sense
Business logic coded once, no more duplicate expense
Self-service analytics starts with data we prepare
Clean foundations that analysts can build on everywhere
[Chorus]
Analytics Engineering - the bridge between the streams
Transform and model data, make it ready for your dreams
A-E spells the magic, where the pipelines meet analysis
Clean it, test it, document it - that's our emphasis
[Outro]
From raw data chaos to insights crystal clear
Analytics Engineers make the pathway appear
Bridge the gap, make it right, that's our calling every day
A-E shows the modern way
3. Data Modeling Fundamentals
[Verse 1]
In the world of data there's a story to tell
About organizing information well
We start with tables, rows and columns neat
Making messy data clean and complete
Each row's a record, each column's a field
Building blocks that make our structure real
[Chorus]
Tables Relations Keys and More
That's what data modeling's for
Operational runs the day
Analytical shows the way
Tables Relations Keys and More
Building data from the floor
[Verse 2]
Primary keys make each row unique
Like a fingerprint for what we seek
Foreign keys connect our tables tight
Creating relationships that feel just right
One to many, many to one
Data connections getting things done
[Chorus]
Tables Relations Keys and More
That's what data modeling's for
Operational runs the day
Analytical shows the way
Tables Relations Keys and More
Building data from the floor
[Bridge]
Operational data keeps the business flowing
Day to day transactions always growing
But analytical takes a different view
Summarized and structured for insights new
OLTP for operations fast and lean
OLAP for analysis comprehensive scene
[Verse 3]
Normalization keeps things organized
Removing duplicates we've recognized
First normal form gets atomic fields
Second normal form dependency yields
Third normal form removes the rest
Now our data structure's at its best
[Chorus]
Tables Relations Keys and More
That's what data modeling's for
Operational runs the day
Analytical shows the way
Tables Relations Keys and More
Building data from the floor
[Outro]
From chaos comes order with a plan so bright
Data modeling makes everything right
Tables and relations working as a team
Building the foundation of the data dream
4. Dimensional Modeling Basics
[Verse 1]
In the world of data warehousing there's a name you need to know
Ralph Kimball showed us how to make our business insights flow
He said forget complex structures that are hard to understand
Build it simple, build it clear, with a well-designed plan
[Chorus]
Facts and dimensions, that's the key
Star schema makes it easy to see
Facts in the middle, dimensions around
Business intelligence can be found
Facts are numbers, dimensions describe
That's how we keep our data alive
[Verse 2]
Start with business questions, what do users want to know
Sales and inventory, how fast does revenue grow
Put the measurements in fact tables, every metric that you track
Quantities and dollars, never looking back
[Chorus]
Facts and dimensions, that's the key
Star schema makes it easy to see
Facts in the middle, dimensions around
Business intelligence can be found
Facts are numbers, dimensions describe
That's how we keep our data alive
[Verse 3]
Dimension tables hold the context, who and what and where and when
Customer and product data, again and again
Foreign keys connect them to the facts that matter most
Star pattern makes it simple from coast to coast
[Bridge]
Kimball taught us grain is crucial
Pick the lowest level that's useful
Additive facts work best of all
Semi-additive can sometimes fall
Build it right the first time through
Your analysts will thank you too
[Chorus]
Facts and dimensions, that's the key
Star schema makes it easy to see
Facts in the middle, dimensions around
Business intelligence can be found
Facts are numbers, dimensions describe
That's how we keep our data alive
[Outro]
So remember Ralph Kimball's way
Structure data for the business day
Stars will guide your warehouse design
Making analytics work just fine
5. Introduction to dbt
[Verse 1]
Raw data sits in tables, messy and unclear
Analysts need structure, that's where dbt appears
SQL transformations, building step by step
From source to insights, keeping data prepped
[Chorus]
dbt transforms, builds and tests
SQL plus version control at its best
Models flow from raw to clean
Analytics ready, living the dream
Transform, build, test, deploy
Data tools that teams enjoy
[Verse 2]
Write your models using simple SQL code
Define relationships, let the magic unfold
Dependencies mapped out, dbt knows the way
Run them in order, transform data today
[Chorus]
dbt transforms, builds and tests
SQL plus version control at its best
Models flow from raw to clean
Analytics ready, living the dream
Transform, build, test, deploy
Data tools that teams enjoy
[Bridge]
Version control keeps changes tracked
Git workflow, nothing gets hacked
Documentation auto-generated too
Lineage graphs show data flowing through
Tests ensure quality never fails
Fresh schemas, reliable trails
[Verse 3]
Staging models clean the source data first
Intermediate steps, quench analytics thirst
Mart models serve the final insights bright
Dashboard ready, everything's just right
[Chorus]
dbt transforms, builds and tests
SQL plus version control at its best
Models flow from raw to clean
Analytics ready, living the dream
Transform, build, test, deploy
Data tools that teams enjoy
[Outro]
From raw to refined, dbt leads the way
Modern data stack, transforming today
SQL you know, with power so much more
Analytics foundation, that's what dbt's for
6. Building dbt Models
[Verse 1]
In your data warehouse, raw tables await
Transform them with models, don't hesitate
Create a new file with dot S-Q-L
dbt will help your data story tell
Select and join, filter what you need
Clean messy columns, plant the seed
[Chorus]
Build it, test it, run it through
Models are the magic that transforms for you
Table, view, or incremental way
Materialization saves the day
dbt models, organized and clean
The best data pipeline you've ever seen
[Verse 2]
In your models folder, structure is key
Staging, marts, and intermediate, you see
Start with staging, clean the source
One table each, stay on course
Then intermediate joins them tight
Preparing data, getting it right
[Chorus]
Build it, test it, run it through
Models are the magic that transforms for you
Table, view, or incremental way
Materialization saves the day
dbt models, organized and clean
The best data pipeline you've ever seen
[Bridge]
Reference models with the ref function call
Dependencies tracked, connecting them all
Config block sets how it's stored
Table for speed, view for low cost scored
Incremental adds just what's new
Efficient updates, resources few
[Verse 3]
Write your SQL, transformation logic clear
Select columns, calculations appear
Where clauses filter, group by aggregates
Window functions calculate rates
Test your models, ensure they're right
Data quality shining bright
[Chorus]
Build it, test it, run it through
Models are the magic that transforms for you
Table, view, or incremental way
Materialization saves the day
dbt models, organized and clean
The best data pipeline you've ever seen
[Outro]
From raw to refined, your data flows
With dbt models, your knowledge grows
Build the pipeline, step by step
Quality data, promises kept
7. dbt Testing and Quality
[Verse 1]
Your data pipeline's flowing strong
But how do you know nothing's wrong?
Raw numbers tell a risky tale
Without some tests, your insights fail
DBT can be your guide
To check your data source and side
Before you trust what numbers show
You need to test and really know
[Chorus]
Test your schema, test your data
Make sure nothing's getting badder
Unique, not null, check every row
Quality first before you go
DBT tests will save the day
Keep the dirty data away
Schema tests and data tests
Quality control at its best
[Verse 2]
Schema tests are built right in
They check your columns, thick and thin
Unique means no duplicates found
Not null ensures your data's sound
Accepted values in a list
Makes sure no categories missed
Relationships between your tables
Keep your joins reliable and stable
[Chorus]
Test your schema, test your data
Make sure nothing's getting badder
Unique, not null, check every row
Quality first before you go
DBT tests will save the day
Keep the dirty data away
Schema tests and data tests
Quality control at its best
[Bridge]
Custom data tests you can write
SQL queries check if values are right
Revenue should never be negative
Dates should be consecutive
Write your logic, make it clear
Test the things that you most fear
Automated checks will run each time
Keep your analytics in line
[Verse 3]
In your YAML file define
Which columns need to pass the line
Configure tests for every model
Keep your data quality throttle
When tests fail the build will stop
No bad data at the top
Reliable analytics start
With testing as the beating heart
[Chorus]
Test your schema, test your data
Make sure nothing's getting badder
Unique, not null, check every row
Quality first before you go
DBT tests will save the day
Keep the dirty data away
Schema tests and data tests
Quality control at its best
[Outro]
Trust your dashboard, trust your metric
DBT tests make data authentic
Quality first from source to mart
Testing is the beating heart
8. Data Documentation and Lineage
[Verse 1]
When data flows through pipelines every day
Without proper docs, the knowledge fades away
Your team keeps asking where this dataset came from
Let's build a system where the answers never run
[Chorus]
Document and trace, every single place
Where your data's been and where it's gonna go
D-B-T docs make it crystal clear
Data lineage keeps the story here
Self-documenting code, institutional mode
Never lose the knowledge that your data team should know
[Verse 2]
Start with dbt to build your models right
Add descriptions so the purpose comes to light
Write source freshness tests and column definitions
Transform your raw data with clear intentions
[Chorus]
Document and trace, every single place
Where your data's been and where it's gonna go
D-B-T docs make it crystal clear
Data lineage keeps the story here
Self-documenting code, institutional mode
Never lose the knowledge that your data team should know
[Verse 3]
Watch the lineage graph show upstream and down
See which tables break when one system's down
Tag your models by the business domain
So when teammates leave, the wisdom will remain
[Bridge]
Schema files tell the story
YAML configs hold the glory
Every transformation documented
No more tribal knowledge fragmented
[Chorus]
Document and trace, every single place
Where your data's been and where it's gonna go
D-B-T docs make it crystal clear
Data lineage keeps the story here
Self-documenting code, institutional mode
Never lose the knowledge that your data team should know
[Outro]
When new engineers join your growing crew
They'll read the docs and know just what to do
Your data pipeline tells its own sweet tale
Documentation means you'll never fail
9. Alternative Data Modeling Approaches
[Verse 1]
Once upon a time we built our data neat and clean
Star schema was the king, dimensions ruled the scene
Fact tables in the center, dimensions all around
But modern data's bigger now, new methods can be found
[Chorus]
One Big Table, everything's together
OBT makes querying much better
Activity schema tells a story
Trade-offs matter, choose your glory
Model data your way, your way
Find the path that fits today
[Verse 2]
Traditional approach says normalize your design
Break it down to pieces, make relationships align
But joining takes forever when your dataset starts to grow
Sometimes flat and simple is the better way to go
[Chorus]
One Big Table, everything's together
OBT makes querying much better
Activity schema tells a story
Trade-offs matter, choose your glory
Model data your way, your way
Find the path that fits today
[Verse 3]
Activity schema thinks in terms of events and time
Every action logged becomes a record in the line
Who did what and when they did it, context all preserved
Better for analytics when behavior patterns are observed
[Bridge]
Storage space versus query speed
Flexibility versus what you need
Dimensional modeling's still around
But modern approaches break new ground
[Chorus]
One Big Table, everything's together
OBT makes querying much better
Activity schema tells a story
Trade-offs matter, choose your glory
Model data your way, your way
Find the path that fits today
[Outro]
No single method rules them all
Choose the right tool for your call
Data modeling's evolved and grown
Pick the approach to call your own
10. Semantic Layers and Metrics
[Verse 1]
Data scattered everywhere, numbers tell different tales
Sales team says we're up ten percent, but finance claims we fail
Marketing shows engagement high, while product team sees low
When everyone speaks different tongues, which numbers help us grow?
[Chorus]
Build the layer, semantic layer
One source of truth for all
Same language, same definitions
Breaking down the data walls
Metrics clear, logic shared
Now we're speaking as one
Semantic layer saves the day
Data chaos is done
[Verse 2]
Revenue means different things to every single team
Is it gross or net of refunds, subscription or one-time stream?
Customer count varies too, are trials included here?
Without consistent definitions, insights disappear
[Chorus]
Build the layer, semantic layer
One source of truth for all
Same language, same definitions
Breaking down the data walls
Metrics clear, logic shared
Now we're speaking as one
Semantic layer saves the day
Data chaos is done
[Bridge]
Centralized business logic, calculations uniform
Transform the raw data once, then serve it in standard form
No more reinventing wheels or building metrics twice
Governance and consistency, now that's our good advice
[Verse 3]
Dashboard shows the same results no matter who's the viewer
CEO and analyst both trust what they're seeing here
Time to market faster now, decisions crystal clear
When data speaks one language, success draws ever near
[Chorus]
Build the layer, semantic layer
One source of truth for all
Same language, same definitions
Breaking down the data walls
Metrics clear, logic shared
Now we're speaking as one
Semantic layer saves the day
Data chaos is done
[Outro]
One layer to rule them all
One truth to guide our way
Semantic metrics save us all
Hip hip hooray!
11. Reverse ETL Fundamentals
[Verse 1]
Data sits inside your warehouse walls
Analytics done but that's not all
Your sales team needs those insights too
But spreadsheets won't get them through
CRM is empty, marketing's blind
Without the gold that you worked to find
[Chorus]
Reverse ETL, flow it back
From warehouse to the business track
Extract Transform Load reversed around
Move insights where they can be found
Activate data, make it real
Reverse ETL seals the deal
[Verse 2]
Customer scores and lifetime value
Segmentation that's fresh and new
Push it to Salesforce, HubSpot too
Marketing platforms get their view
Real-time updates, not stale reports
Bridge the gap between data ports
[Chorus]
Reverse ETL, flow it back
From warehouse to the business track
Extract Transform Load reversed around
Move insights where they can be found
Activate data, make it real
Reverse ETL seals the deal
[Bridge]
Schedule the sync, map every field
Transform the format, make data yield
APIs connecting left and right
Operational systems come alive
No more silos, data flows
From analytics to action goes
[Verse 3]
Census, Hightouch, tools that shine
Turn your warehouse into pipeline
Batch or streaming, you decide
Keep your business teams supplied
Data activation is the key
Reverse ETL sets insights free
[Chorus]
Reverse ETL, flow it back
From warehouse to the business track
Extract Transform Load reversed around
Move insights where they can be found
Activate data, make it real
Reverse ETL seals the deal
[Outro]
Your warehouse gold deserves to flow
To every tool your teams should know
Reverse the river, change the game
Data activation is the name
12. Data Observability Basics
[Verse 1]
Sarah runs a data team, analytics everywhere
Charts and dashboards looking good, but something's in the air
Numbers seem a little off, reports don't match today
How can she be confident her data's not led astray
[Chorus]
Data observability, watch it Quality, Freshness, Anomaly
Q-F-A, that's the way
Monitor your pipeline every single day
Data observability, three pillars standing tall
Quality, Freshness, Anomaly - observe them all
[Verse 2]
Quality means your data's clean, no nulls where numbers go
Schema matches what you need, formats in a row
Completeness is the golden rule, missing fields spell doom
Accuracy and consistency keep your metrics in bloom
[Chorus]
Data observability, watch it Quality, Freshness, Anomaly
Q-F-A, that's the way
Monitor your pipeline every single day
Data observability, three pillars standing tall
Quality, Freshness, Anomaly - observe them all
[Verse 3]
Freshness tells you when data flows, timestamps never lie
If your batch was due at nine but came at half past five
Staleness kills your real-time dreams, delays will make you frown
Set your SLAs and alerts to catch when systems are down
[Chorus]
Data observability, watch it Quality, Freshness, Anomaly
Q-F-A, that's the way
Monitor your pipeline every single day
Data observability, three pillars standing tall
Quality, Freshness, Anomaly - observe them all
[Bridge]
Anomalies are patterns wrong, outliers in your stream
Statistical detection finds what's breaking your routine
Volume spikes and sudden drops, distributions gone astray
Early warning systems help you fix things right away
[Verse 4]
Lineage maps your data's path from source to final view
When something breaks you'll trace it back and know just what to do
Dashboards show your health in green, alerts will flash you red
Proactive monitoring means you'll catch bugs before they spread
[Final Chorus]
Data observability, watch it Quality, Freshness, Anomaly
Q-F-A, that's the way
Monitor your pipeline every single day
Data observability, three pillars standing tall
Quality, Freshness, Anomaly - observe them all
[Outro]
Sarah's team now sleeps at night, their pipelines running clean
Data observability makes them the best they've ever been
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