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6 Ways to Ingest Data into Microsoft Fabric (And How to Choose)
- Tony Berry
- Apr 22
- 4 min read
Updated: 18 minutes ago
By Tony Berry
Data ingestion is a foundational component of any modern data architecture, enabling raw data to be collected, imported, and processed from a variety of source systems into a centralized lake or warehouse.
Microsoft Fabric provides several robust options for data ingestion - each with its own strengths, limitations, and ideal use cases. Whether your team is focused on building pipelines, accelerating ETL, or supporting analytics and AI workflows, choosing the right approach is critical.
Below is an overview of the six key ingestion methods available in Microsoft Fabric, with insights on when and why to use each.
1. Data Pipelines
Data Pipelines in Microsoft Fabric offer a code-free or low-code experience for orchestrating ETL workflows. They enable users to copy data from source to destination while also incorporating additional steps, such as preparing environments, executing T-SQL, and running notebooks.
Best for: Teams looking to automate and scale standard ETL processes with limited code requirements.
✅ Pros
Code-Free or Low-Code – Accessible to broader teams.
Workflow Automation – Supports scheduling and orchestration.
High Scalability – Capable of managing large volumes of data.
⚠️ Cons
Initial Setup – Requires some configuration.
Performance Ceiling – May not match code-rich options for extremely high-throughput.
Transformation Flexibility – More limited for advanced data shaping or normalization.
2. Dataflows Gen2
Dataflows Gen2 provides a visual, Power Query–based environment for data prep and transformation before ingestion. It’s designed for users who need custom, column-level transformations without writing code.
Best for: Analysts and data engineers who need an easy way to prep and shape source data visually.
✅ Pros
No-Code Interface – Great for data prep without engineering support.
Custom Transformations – Modify schemas, create calculated fields, and shape datasets.
Fabric Native – Fully integrated into the Fabric ecosystem.
⚠️ Cons
Source Limitations – Bound to supported connectors.
Less Suitable for Scale – Not optimized for massive or highly complex pipelines.
Flexibility Constraints – May not support advanced ingestion logic.
3. PySpark and Python Notebooks
For technically advanced teams, PySpark and Python notebooks offer unmatched flexibility and distributed processing capabilities. These notebooks are ideal for complex transformation pipelines, large datasets, and Spark-native workloads.
Best for: Teams with Spark/Python expertise working on custom, high-scale data processing tasks.
✅ Pros
High Performance – Leverages Spark’s distributed compute engine.
Custom Logic – Supports complex ingestion and transformation workflows.
Seamless Integration – Connects to other Fabric components for end-to-end pipelines.
⚠️ Cons
High Complexity – Requires PySpark or Python expertise.
Manual Management – Error handling, logging, and retries must be coded explicitly.
Setup Overhead – More effort required than GUI-based tools.
4. Copy Job (New)
The new Copy Job tool uses a visual assistant to move data between cloud-based sources and sinks. It’s a simplified option for users who want to ingest data quickly without building a full pipeline.
Best for: Users who need a fast, lightweight ingestion option with minimal setup.
✅ Pros
User-Friendly Setup – Copy Assistant simplifies configuration.
Connector Support – Works with a growing list of cloud sources.
Composable – Can be included in broader pipeline workflows.
⚠️ Cons
Gateway Restriction – On-premises to on-premises transfers require a shared gateway.
Throughput Limitations – May not match dedicated tools like the COPY statement.
Limited Connectors – Support for sources is still expanding.
5. COPY (Transact-SQL)
The COPY statement is a high-throughput, T-SQL–driven method for ingesting data from Azure storage into Fabric. It’s best suited for engineering teams who need full control over ingestion behavior via SQL.
Best for: Data teams already operating in a Transact-SQL environment and needing maximum performance.
✅ Pros
Top-Tier Performance – Delivers the highest available ingestion throughput.
Granular Control – Tune performance, map columns, and control ingestion behavior.
ETL/ELT Integration – Works seamlessly with existing T-SQL logic.
⚠️ Cons
Azure-Only Source Support – Currently limited to Azure storage accounts.
Code Requirement – Requires SQL fluency; not ideal for all users.
6. External Tools (e.g., Fivetran)
Fivetran offers a Managed Data Lake Service (MDLS) that automates ingestion and normalization into Fabric and OneLake. With 700+ connectors and prebuilt logic, it’s a strong option for teams that prioritize automation and governance.
Best for: Organizations seeking fast, governed ingestion from a wide variety of data sources—without building it all themselves.
✅ Pros
Fully Managed – Automates ingestion, normalization, compaction, and deduplication.
Extensive Connectors – 700+ prebuilt source integrations.
Fabric-Native – Supports OneLake and AI/analytics workloads.
Governance Ready – Converts raw data into optimized formats (Delta Lake or Apache Iceberg).
⚠️ Cons
Cost Consideration – Fivetran licensing adds to overall project cost.
Final Thoughts
Microsoft Fabric gives teams the flexibility to choose the right ingestion strategy based on technical maturity, scale, and existing architecture. Whether you're looking for no-code setup, full control via SQL or Spark, or fully managed ingestion, there’s an option designed to meet your needs.
Understanding the trade-offs of each method - and aligning them with your team’s strengths - sets the foundation for scalable, insight-ready data infrastructure.
Need help choosing the right data ingestion path?
At Interloop, we specialize in helping mid-market teams activate their data with clarity and confidence. Whether you're evaluating COPY vs. Pipelines, rolling out Fivetran, or just getting started with Microsoft Fabric - we can help you move from disconnected data to real-time insight.
Let’s get you from ingestion to action. Get looped in today.
