DP-203 · Question #281
You are designing an Azure Data Lake Storage solution that will transform raw JSON files for use in an analytical workload. You need to recommend a format for the transformed files. The solution…
The correct answer is D. Apache Parquet. Parquet, an open-source file format for Hadoop, stores nested data structures in a flat columnar Compared to a traditional approach where data is stored in a row-oriented approach, Parquet file format is more efficient in terms of storage and performance. It is especially good…
Question
You are designing an Azure Data Lake Storage solution that will transform raw JSON files for use in an analytical workload. You need to recommend a format for the transformed files. The solution must meet the following requirements:
- Contain information about the data types of each column in the files.
- Support querying a subset of columns in the files.
- Support read-heavy analytical workloads.
- Minimize the file size.
What should you recommend?
Options
- AJSON
- BCSV
- CApache Avro
- DApache Parquet
How the community answered
(53 responses)- A9% (5)
- B2% (1)
- C4% (2)
- D85% (45)
Explanation
Parquet, an open-source file format for Hadoop, stores nested data structures in a flat columnar Compared to a traditional approach where data is stored in a row-oriented approach, Parquet file format is more efficient in terms of storage and performance. It is especially good for queries that read particular columns from a ג €wide ג € (with many columns) table since only needed columns are read, and IO is minimized.
Topics
Community Discussion
No community discussion yet for this question.