Introduction
In the previous article, we explored what data engineering is and why it matters. Now it's time to go deeper.
If you want to become a data engineer, there are three concepts you absolutely must understand:
- Data Pipelines
- ETL/ELT Processes
- Data Warehouses and Data Lakes
These are not just buzzwords. They are the foundation of everything you will build as a data engineer. In my years of consulting and training teams, I've seen engineers struggle not because they lack coding skills — but because they never truly grasped these fundamentals.
Let's fix that.
What Is a Data Pipeline?
A data pipeline is a series of steps that move data from one place to another.
That's it. Simple in concept. Complex in execution.
A Real-World Analogy
Think of a water pipeline:
- Water is collected from a source (river, reservoir)
- It passes through treatment plants (cleaning, filtering)
- It arrives at your tap ready to use
A data pipeline works the same way:
- Data is extracted from sources (databases, APIs, files)
- It passes through transformations (cleaning, formatting, aggregating)
- It arrives at a destination ready for analysis (warehouse, dashboard, ML model)
Why Pipelines Matter
Without pipelines, you'd be manually copying data between systems. Every. Single. Day.
Pipelines automate this process. They run on schedules, handle errors, and scale with your data volume.
ETL vs. ELT: What's the Difference?
You'll hear these acronyms constantly in data engineering. Let's break them down.
ETL: Extract, Transform, Load
The traditional approach:
- Extract — Pull data from source systems
- Transform — Clean, format, and process the data
- Load — Store the transformed data in the destination
ETL transforms data before it reaches the warehouse. This was standard when storage was expensive and compute was limited.
ELT: Extract, Load, Transform
The modern approach:
- Extract — Pull data from source systems
- Load — Store raw data in the destination first
- Transform — Process data inside the warehouse
ELT loads data first, then transforms it using the warehouse's processing power. This became popular with cloud platforms where storage is cheap and compute is scalable.
Which One Should You Use?
| Factor | ETL | ELT |
|---|---|---|
| Data Volume | Smaller datasets | Large-scale data |
| Infrastructure | On-premise systems | Cloud platforms |
| Transformation | Before loading | After loading |
| Flexibility | Less flexible | More flexible |
| Cost | Higher upfront processing | Pay-as-you-query |
In practice, most modern data teams use ELT with cloud warehouses like Snowflake, BigQuery, or Databricks.
Data Warehouses Explained
A data warehouse is a centralized repository designed for analytical queries.
Unlike transactional databases (which handle day-to-day operations), warehouses are optimized for:
- Aggregations
- Complex queries
- Historical analysis
- Reporting
Key Characteristics
- Structured data — Organized in tables with defined schemas
- Optimized for reads — Fast query performance
- Historical storage — Keeps data over time for trend analysis
- Single source of truth — Consolidates data from multiple systems
Popular Data Warehouses
| Platform | Type |
|---|---|
| Snowflake | Cloud-native |
| Google BigQuery | Cloud-native |
| Amazon Redshift | Cloud-native |
| Databricks SQL | Cloud-native / Lakehouse |
| Microsoft Synapse | Cloud-native |
Data Lakes Explained
A data lake is a storage repository that holds raw data in its native format.
Unlike warehouses, data lakes accept:
- Structured data (tables, CSVs)
- Semi-structured data (JSON, XML)
- Unstructured data (images, logs, videos)
Warehouse vs. Lake
| Aspect | Data Warehouse | Data Lake |
|---|---|---|
| Data Format | Structured | Any format |
| Schema | Defined before loading | Defined when reading |
| Use Case | Business reporting | Exploration, ML, archiving |
| Cost | Higher | Lower |
| Query Speed | Faster | Slower (without optimization) |
The Lakehouse: Best of Both Worlds
Recently, a hybrid architecture has emerged: the Data Lakehouse.
It combines:
- The flexibility of a data lake
- The performance and structure of a warehouse
Platforms like Databricks and Snowflake now support lakehouse architectures.
How These Concepts Connect
Here's how it all fits together:
[Source Systems]
↓
EXTRACT
↓
[Data Lake] ← Raw storage
↓
TRANSFORM
↓
[Data Warehouse] ← Cleaned, structured data
↓
[Dashboards / Reports / ML Models]
As a data engineer, your job is to design and maintain this flow.
Common Pipeline Patterns
Over the years, I've seen certain patterns repeat across organizations:
Batch Processing
- Data is collected and processed at scheduled intervals (hourly, daily)
- Best for: Reporting, historical analysis
- Tools: Apache Spark, dbt, Airflow
Stream Processing
- Data is processed in real-time as it arrives
- Best for: Fraud detection, live dashboards, IoT
- Tools: Apache Kafka, Apache Flink, Spark Streaming
Hybrid
- Combines batch and streaming for different use cases
- Most enterprise systems use this approach
Mistakes Beginners Make
From my experience training teams, here are common pitfalls:
- Skipping data validation — Always check data quality before loading
- Overcomplicating pipelines — Start simple, optimize later
- Ignoring idempotency — Pipelines should produce the same result if run multiple times
- No monitoring — If your pipeline fails silently, you'll find out the hard way
- Mixing concerns — Keep extraction, transformation, and loading as separate steps
What's Next?
You now understand the core building blocks:
- Pipelines move data
- ETL/ELT processes structure that movement
- Warehouses and lakes store the results
In the next article, we'll explore the tools and technologies that power modern data engineering — from orchestration frameworks to cloud platforms.
Series Overview
- Data Engineering Uncovered: What It Is and Why It Matters
- Pipelines, ETL, and Warehouses: The DNA of Data Engineering (You are here)
- Tools of the Trade: What Powers Modern Data Engineering
- The Math You Actually Need as a Data Engineer
- Building Your First Pipeline: From Concept to Execution
- Charting Your Path: Courses and Resources to Accelerate Your Journey
Questions about pipelines, ETL, or warehouses? Drop them in the comments.
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