Parquet format and columnar storage in Apache Spark - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the Parquet file format?

Parquet is a columnar storage file format designed for efficient data storage and retrieval. It stores data by columns instead of rows, which helps speed up queries and reduce storage space.

beginner

Why is columnar storage useful in data processing?

Columnar storage allows reading only the needed columns instead of the whole dataset. This reduces the amount of data read from disk, making queries faster and saving memory.

intermediate

How does Parquet format improve compression?

Since Parquet stores data by columns, similar data types are stored together. This makes compression more effective because similar values compress better than mixed data.

beginner

In Apache Spark, how do you read a Parquet file?

You can read a Parquet file in Spark using: spark.read.parquet('path/to/file'). This loads the data into a DataFrame for easy processing.

beginner

What is a real-life example of when columnar storage helps?

Imagine a store tracking sales data with many columns like date, product, price, and customer. If you only want to analyze prices, columnar storage lets you read just the price column quickly without loading all other data.

What is the main advantage of Parquet's columnar storage?

A Faster reading of specific columns

B Storing data as plain text

C Storing data row by row

D Using more disk space

Parquet stores data by columns, which allows faster reading of only the needed columns.

Which Apache Spark command reads a Parquet file?

A spark.read.csv('file')

B spark.read.text('file')

C spark.read.parquet('file')

D spark.load.json('file')

spark.read.parquet('file') is the correct way to read Parquet files in Spark.

Why does columnar storage improve compression?

A Because similar data types are stored together

B Because it stores data in rows

C Because it duplicates data

D Because it stores data as images

Storing similar data types together helps compression algorithms work better.

Which scenario benefits most from columnar storage?

A Storing images

B Reading all columns of a small dataset

C Writing data to a text file

D Reading only a few columns from a large dataset

Columnar storage shines when you read only some columns from large datasets.

Parquet files are best described as:

A Row-based text files

B Columnar binary files

C Uncompressed CSV files

D Image files

Parquet files store data in a columnar binary format for efficiency.

Explain how Parquet format uses columnar storage to improve data processing.

Think about how reading fewer columns helps speed and saves space.

Describe a real-world example where using Parquet and columnar storage would be helpful.

Consider a business analyzing sales or customer data.