dataflow-gen2

Dataflow Gen2

When to Use

• Building data transformations without writing PySpark code in Microsoft Fabric
• Connecting to 150+ data sources using Power Query's no-code interface
• Setting up incremental refresh to load only new or changed data
• Loading transformed data to Lakehouse tables, Warehouse, or Azure SQL
• Deciding between Dataflow Gen2 and Spark Notebooks for a transformation task
• Preparing for Microsoft Fabric Data Engineer Associate (DP-700) exam

Core Jobs

1. Dataflow Gen2 Architecture

• Dataflow Gen2 = Power Query Online interface with staging layer in Microsoft Fabric
• 150+ data source connectors: files (CSV, Excel, JSON, Parquet), databases (SQL Server, Snowflake, Oracle), cloud services (SharePoint, Salesforce, REST APIs)
• Transformation engine: M language (Power Query formula language); visually authored
• Staging layer: intermediate OneLake storage during transformation (reduces source load)
• Final output written to configured data destinations

2. Staging Layer

• Staging = temporary OneLake storage where intermediate results land during transformations
• Automatically enabled by default; can be disabled per query
• Benefits: reduces repeated calls to source systems; enables parallel transformation steps
• Staging storage: always OneLake (cannot change to external storage)
• Important: staging is temporary; only the final data destination persists

3. Data Destinations

Destination	Use Case
Fabric Lakehouse (table)	Most common; Delta table in Lakehouse Tables/
Fabric Warehouse (table)	SQL-first teams; T-SQL accessible
Azure SQL Database	Hybrid scenarios; push to existing SQL workloads
Fabric KQL Database	Real-time analytics (Eventhouse)

• One Dataflow Gen2 can have multiple data destinations (one per query)
• Destination mode: Append (add rows), Replace (overwrite), Merge (upsert on key)

4. Incremental Refresh

• Only loads new or changed data since the last refresh (avoids full re-scan)
• Requirements:
  1. Source must have a reliable datetime column (e.g., ModifiedDate, EventTimestamp)
  2. Define RangeStart and RangeEnd parameters (datetime type) in Power Query
  3. Filter source query by: Table.SelectRows(Source, each [ModifiedDate] >= RangeStart and [ModifiedDate] < RangeEnd)
• Fabric manages the date window automatically on each refresh cycle
• Not supported for all connectors (check source compatibility)

5. Dataflow Gen2 vs Notebooks Decision

Criteria	Dataflow Gen2	Spark Notebook
User skill	Business analyst, no-code	Data engineer, PySpark
Transformation complexity	Low-medium (Power Query M)	High (arbitrary Python/Spark)
Scale	Medium datasets	Very large datasets (TB+)
Scheduling	Built-in refresh schedule	Manual or Pipeline trigger
ML/AI logic	Not supported	Full Python ecosystem
Best for	ETL from business systems	Large-scale data engineering

Decision rule: Choose Dataflow Gen2 for straightforward ETL from standard connectors; use Notebooks for complex transformations, ML, or very large data volumes.

6. Scheduling and Pipeline Integration

• Standalone schedule: configure refresh frequency (hourly, daily, weekly) in Fabric workspace
• Fabric Pipeline integration: add Dataflow Gen2 as a Dataflow activity in Pipeline
• Pipeline enables: sequential execution with other activities (Copy Data, Notebook), conditional logic, parameterization
• Dataflow activity in Pipeline can pass parameters to override connector settings

Key Concepts

• Power Query — visual data transformation engine; M language under the hood
• Staging — intermediate OneLake storage during transformation; reduces source load
• Data destination — final output target (Lakehouse table, Warehouse, SQL); persists after refresh
• RangeStart / RangeEnd — required datetime parameters for incremental refresh
• Dataflow Gen1 — Power BI-only Dataflow; no staging, fewer destinations; legacy, do not confuse with Gen2
• M language — Power Query formula language; functional, lazy-evaluated; authored visually

Checklist

• Dataflow Gen2 (not Gen1) selected when working in Microsoft Fabric?
• Staging enabled (default) to reduce repeated calls to source systems?
• Data destination configured for each output query (Lakehouse table, etc.)?
• Incremental refresh set up with RangeStart/RangeEnd parameters for large sources?
• Source connector supports the required authentication method (OAuth, key, managed identity)?
• Dataflow Gen2 activity added to Fabric Pipeline when orchestration is needed?
• Notebook used instead when transformation requires PySpark or Python libraries?

Output Format

• 🔴 Critical — using Dataflow Gen1 (Power BI Dataflows) expecting Fabric destinations
• 🔴 Critical — incremental refresh missing RangeStart/RangeEnd parameters (full refresh will run)
• 🟡 Warning — Dataflow Gen2 used for TB-scale data where Spark Notebook would perform better
• 🟡 Warning — no staging configured; source system queried multiple times per refresh
• 🟢 Suggestion — embed Dataflow Gen2 in Fabric Pipeline for orchestrated, conditional execution

Exam Tips

• Dataflow Gen2 ≠ Dataflow Gen1 — Gen2 has staging layer, more destinations (Lakehouse, Warehouse), and is Fabric-native; Gen1 is Power BI-only with fewer capabilities
• Staging must be OneLake — cannot configure external storage for staging; reduces load on source during transformation
• Incremental refresh requires RangeStart and RangeEnd parameters — must be datetime type; Power Query filters source by this range automatically on each run
• Dataflow Gen2 supports multiple data destinations — one per query in the same Dataflow; each can go to a different Lakehouse or table
• For complex PySpark transformations, use Notebooks — Dataflow Gen2 is Power Query (M), not Python/Spark
• Dataflow Gen2 as activity in Fabric Pipelines — enables orchestration with Copy Data, Notebook, and other pipeline activities; trigger on schedule or event