Understanding Azure Data Engineering: Why So Many ETL Tools?

apoorvasogani — Thu, 25 Jun 2026 03:48:59 GMT

Understanding Azure Data Engineering: Why So Many ETL Tools?

When I started exploring , one question kept bothering me:

If Azure Data Factory, Databricks, Synapse Analytics, and Microsoft Fabric can all perform ETL/ELT operations, why do we need so many tools?

After digging deeper, here's the simplified understanding that helped me.

The traditional Azure workflow

Source Systems  (SQL Server, Oracle, SAP, APIs)
        |
        v
Azure Data Factory      (Orchestration & Data Movement)
        |
        v
Azure Data Lake Storage (Storage)
        |
        v
Azure Databricks        (Transformation Engine)
        |
        v
Synapse / Fabric        (Analytics & Reporting)
        |
        v
Power BI

Azure Data Factory (ADF) — the logistics manager

Extracts data
Copies data between systems
Schedules and orchestrates workflows

Example: Oracle → ADLS

ADF moves data but is not designed for heavy transformations on massive datasets.

⚙️ Azure Databricks — the factory floor

Cleans data
Joins large datasets
Runs Spark jobs
Handles big-data processing

Raw Sales Data  +  Customer Data  +  Inventory Data
                       |
                       v
                  Databricks
                       |
                       v
                 Curated Data

🏢 Synapse Analytics / Fabric Warehouse — the reporting layer

Optimized for BI queries
Supports dashboards and analytics
Serves Power BI efficiently

Business users typically consume data from here.

Another question: if Databricks can store data, why still ADLS?

The answer was a game changer — modern cloud architecture separates compute from storage.

Compute   ≠   Storage

Azure Databricks  =  Compute Engine
Azure Data Lake   =  Storage Layer

When Databricks creates Delta Tables, the actual data files are usually stored in ADLS:

ADLS/
 └── sales/
      ├── part-0001.parquet
      ├── part-0002.parquet
      └── _delta_log/

Benefits:

✅ Independent scaling of compute and storage
✅ Lower costs
✅ Data remains accessible even if Databricks is removed
✅ Other tools can access the same data

What is a Lakehouse?

A Lakehouse combines a Data Lake with Data Warehouse features. Using Delta Lake, we get:

ACID transactions
Schema enforcement
Time travel
Faster queries
Updates and deletes

ADLS                    = Physical Storage
Databricks + Delta Lake = Lakehouse

My key takeaway

A modern Azure Data Platform is not about choosing one tool — it's about understanding the role of each layer:

📦 ADLS	Stores data
🚚 ADF	Moves data
⚙️ Databricks	Transforms data
🏢 Warehouse	Serves analytics
📊 Power BI	Delivers insights

Once I understood the difference between Storage, Compute, Orchestration, and Analytics, the Azure data ecosystem started making much more sense.

#Azure #DataEngineering #Databricks #ADF #MicrosoftFabric #AzureSynapse #DeltaLake #DataAnalytics

Re: Understanding Azure Data Engineering: Why So Many ETL Tools?

Lodha_Jaydeep — Thu, 25 Jun 2026 13:36:19 GMT

Hi @apoorvasogani,

You have did the good overview, appriciate it.

Basically there are so many ETL tools in the Microsoft Azure eco system as the each layer has it's own job.

ADF (Low code): It use for moves or orchestrating your data from source to destination, source and destination varies according to the requirenment.

Fabric Equivalent: Data Pipelines

ADLS: It used for the storing purpose

Fabric Equivalent: Lake house/ Warehouse

Databricks/Synapse (Need Coding Expertise): Is purely spark base ETL tool (if you good at Python, R, SQL or scala....)

Fabric Equivalent: Pyspark Notebooks

Power BI: This is the reporting tool to show data to end users or busniness in form of the dashboards or reports.

So, the summary is Fabric having the combined features and functionalities form the diffrent ETL tools and services. And cover most of them which are used as indivdual tools or services.

So, if you are new and searching for tool/services should be use, you can go with fabric.

I hope this helps and I am able to clear your doubts. Please give some kudos or accept as solution if helps.

Thanks

topic Understanding Azure Data Engineering: Why So Many ETL Tools? in Data Engineering

Understanding Azure Data Engineering: Why So Many ETL Tools?

Understanding Azure Data Engineering: Why So Many ETL Tools?

The traditional Azure workflow

Azure Data Factory (ADF) — the logistics manager

⚙️ Azure Databricks — the factory floor

🏢 Synapse Analytics / Fabric Warehouse — the reporting layer

Another question: if Databricks can store data, why still ADLS?

What is a Lakehouse?

My key takeaway

Re: Understanding Azure Data Engineering: Why So Many ETL Tools?