Match each data storage format to its characteristic.
Drag a concept onto its matching description — or click a concept then click the description.
Columnar storage format optimized for analytics
Row-based format with schema embedded
Columnar format with high compression
ACID transactions on data lakes
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Parquet: Columnar storage format, efficient for analytics and compression
In Azure data engineering, Parquet and ORC are columnar formats ideal for analytics, while Avro is row-based with embedded schema, and JSON is a semi-structured text format. Common confusions involve mixing columnar and row-oriented characteristics.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Parquet: Columnar storage format, efficient for analytics and compression
Why this is correct
Parquet is a columnar storage format optimized for analytical workloads and provides efficient compression.
Avro: Row-based format with schema embedded in each file
Why this is correct
Avro is a row-oriented format that stores the schema along with the data, making it suitable for serialization.
JSON: Semi-structured, human-readable text format
Why this is correct
JSON is a text-based, semi-structured format that is easy to read and commonly used for data interchange.
ORC: Columnar format optimized for Hive and similar to Parquet
Why this is correct
ORC is a columnar storage format designed for Hive, offering high compression and performance for large datasets.
JSON: Columnar storage optimized for compression
Why it's wrong here
Incorrect — JSON is text-based, not columnar; columnar characteristics apply to Parquet or ORC.
ORC: Row-oriented format with schema in each file
Why it's wrong here
Incorrect — ORC is columnar, not row-oriented; the row-oriented format with embedded schema is Avro.
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