Why Data Engineering Exists (and Why Data Ingestion Comes First)
When people hear data engineering, they often think of tools: Spark, Airflow, Kafka, dbt, Snowflake.
But data engineering did not start with tools.
It started with problems.
Before Data Engineering: The Early Days of Software
In the early days, applications were simple:
- One application
- One database
- One team
If you wanted answers, you ran SQL directly on the database:
SELECT COUNT(*) FROM orders;
This worked because:
- Data was small
- Few users
- Few queries
There was no separate data engineering role.
What Changed? (The Real Problems)
As applications grew, several things happened at once:
1. Data Volume Exploded
- Thousands → millions → billions of rows
- Queries became slower
- Storage became expensive
2. More People Wanted Data
- Business teams
- Analysts
- Data scientists
- ML systems
All querying the same database.
3. Production Systems Started Breaking
Analytics queries:
- Locked tables
- Slowed down user requests
- Caused outages
At this point, companies learned a painful lesson:
Operational databases are not analytics systems.
The Birth of Data Engineering
To solve these problems, companies started separating concerns:
- Application databases → serve users
- Analytics systems → answer questions
But this separation created a new problem:
How does data move reliably between systems?
That question is the foundation of data engineering.
What Data Engineering Really Is
Data engineering is the discipline of:
Designing and building reliable systems that move, store, and prepare data so others can use it safely and correctly.
This includes:
- Data ingestion
- Storage design
- Transformations
- Orchestration
- Data quality
- Reliability and recovery
But everything starts with ingestion.
Why Data Ingestion Comes First
If ingestion is wrong:
- Data is missing
- Data is duplicated
- Data is incorrect
No transformation can fix that.
This is why experienced engineers say:
"Trust starts at ingestion."
The Simplest Ingestion Attempt (And Why It Failed)
The first approach teams tried was simple copying:
SELECT * FROM orders;
Run it every day. Store the result.
This worked briefly, then failed because:
- Tables grew too large
- Network and compute costs exploded
- Updates and deletes were lost
- No history existed
The system could not scale.
From Snapshots to Change-Based Thinking
Instead of repeatedly asking:
"What does the table look like now?"
Teams realized a better question was:
"What changed since last time?"
This shift in thinking led to Change Data Capture (CDC).
What Is Change Data Capture (CDC)?
CDC is a technique that captures:
- New rows (INSERT)
- Changed rows (UPDATE)
- Removed rows (DELETE)
Instead of copying full tables, CDC captures events.
This reduced:
- Data movement
- Load on databases
- Cost
And increased:
- Accuracy
- Freshness
- Scalability
How CDC Is Possible (Without Magic)
Databases already record every change internally using transaction logs:
| Database | Log Type |
|---|---|
| PostgreSQL | WAL |
| MySQL | Binlog |
| SQL Server | Transaction Log |
CDC tools read these logs and convert low-level operations into structured change events.
Snapshot + CDC: The Complete Ingestion Model
CDC cannot start from nothing.
The correct ingestion flow is:
- Take an initial snapshot (full copy)
- Store it safely
- Start reading database logs
- Continuously apply changes
Think of it as:
- Snapshot = starting state
- CDC = ongoing history
Both are required for correctness.
Why CDC Output Is Not Analytics-Ready
CDC produces events like:
{ "operation": "UPDATE", "order_id": 101, "before": {"amount": 500}, "after": {"amount": 600} }
Analytics systems expect clean tables.
A data engineer’s job is to:
- Apply events in order
- Handle duplicates
- Propagate deletes
- Rebuild correct table state
Common Failures That Created Modern Data Engineering
Many early systems failed because they:
- Ignored deletes
- Skipped initial snapshots
- Applied changes out of order
- Overwrote raw data
These failures shaped modern best practices.
The Core Lesson
Every modern data problem can be traced back to one mistake:
Treating ingestion as a copy problem instead of a systems problem.
Once data is ingested incorrectly, no amount of:
- SQL
- dbt models
- dashboards
- machine learning
can fully fix it.
This is why data engineering exists.
Not to run tools. Not to build pipelines.
But to guarantee that data arrives once, in order, and with history preserved.
Everything else like warehouses, transformations, analytics, AI is built on top of that foundation.