Data Engineering Data Serialization Formats

Question 1

What is the primary advantage of Apache Parquet over CSV for analytical workloads?

Accepted Answer

It stores data in columnar format enabling faster analytical queries

Answer

It supports real-time data streaming

Answer

It is human-readable and easier to debug

Answer

It has no schema requirements

Question 2

Which serialization format uses binary encoding and requires a schema to serialize and deserialize data?

Accepted Answer

Apache Avro

Answer

JSON

Answer

CSV

Answer

XML

Question 3

What type of internal storage organization does ORC (Optimized Row Columnar) use?

Accepted Answer

A hybrid of row stripes containing columnar data

Answer

Pure row-based storage

Answer

Pure columnar storage

Answer

Key-value based storage

Question 4

Which serialization format is most appropriate when humans need to inspect and edit data directly without special tools?

Accepted Answer

JSON

Answer

Parquet

Answer

Avro

Answer

Protocol Buffers

Question 5

What does 'schema evolution' mean in the context of data serialization?

Accepted Answer

The ability to modify a schema over time while maintaining compatibility with existing data

Answer

Replacing an old schema with a completely new incompatible one

Answer

Automatically generating schemas from raw data

Answer

Converting schemas between different file formats

Question 6

Which format provides the best compression ratios for analytical data due to columnar storage and value encoding techniques?

Accepted Answer

Parquet

Answer

CSV

Answer

JSON

Answer

XML

Question 7

In Apache Avro, where is the writer's schema typically stored to make files self-describing?

Accepted Answer

In the file header of each Avro data file

Answer

In a separate .xsd file alongside the data

Answer

In a YAML configuration file on the server

Answer

In the application code only at runtime