Microsoft Certified: Fabric Analytics Engineer AssociatePlan and implement data analytics solutions (10-15%)Medium
A data engineering team is designing a new data ingestion pipeline for a Microsoft Fabric Lakehouse. The source data consists of nightly batch files containing customer transaction records in CSV format, with an average file size of 500 MB. The team needs to ensure atomic, consistent, isolated, and durable (ACID) transactions when appending new data to an existing Delta table within the Lakehouse. Which ingestion method should they prioritize to meet these requirements efficiently?
- AUtilizing a Spark notebook to read the CSV files and write to the Delta table.
- BEmploying a custom C# application to stream data into the Lakehouse endpoint.
- CUsing Azure Data Factory Copy Data activity directly to the Lakehouse shortcut.
- DUploading files directly to the Lakehouse's 'Files' section via the Fabric portal.
Show answer & explanationAnswer & explanation
Correct answer: A. Utilizing a Spark notebook to read the CSV files and write to the Delta table.
Spark notebooks provide native support for Delta Lake, enabling ACID transactions, schema enforcement, and efficient upserts or appends to Delta tables within a Lakehouse. This method aligns perfectly with the requirement for ACID properties when ingesting data.
Why the other options are wrong
- B. Custom applications would require significant development to replicate Delta Lake's ACID capabilities and efficient append operations, making it less efficient than native Spark integration.
- C. Copy Data activity can move files but doesn't inherently provide ACID guarantees when appending to Delta tables; it's more for raw file transfer or basic table loads.
- D. Direct portal upload places files into the raw file storage but does not integrate them into a Delta table with ACID properties; it's a manual file management approach.
Delta Lake ACID Transactions
Delta Lake provides Atomicity, Consistency, Isolation, and Durability (ACID) properties to data lakes, ensuring reliable data transactions and preventing data corruption, especially during concurrent operations.
- Ensures data integrity for reads and writes.
- Supports schema evolution and enforcement.
- Enables time travel (versioning) of data.
Memory trick: Spark's ACID shield protects your Lakehouse data transactions.