According to the AWS Certified Data Engineer – Associate (DEA-C01) examination guide, understanding the different data storage formats such as .csv, .txt, and Parquet is crucial. This examination measures your ability to design, build, secure, and maintain analytics solutions that provide insight from data.
In the data world, different formats serve different purposes. Let’s dig deeper into these common data storage formats. We’ll start with .csv, then .txt and finally, Parquet.
CSV (.csv)
CSV or Comma-Separated Values is a very simple and common format for data storage. Each record in the table is one line in the text file. Each field value of a record is separated from the next by a character (the comma). A CSV file does not require a specific character encoding, it can be specified using the charset option.
Advantages:
- Excellent for small datasets and a quick and dirty import/export. It’s not very efficient for larger datasets.
- Can be read and written by most programming languages and spreadsheets.
Disadvantages:
- Does not support complex data types.
- Lacks standardization – Not all programs parse CSV in the same way.
Here’s an example of how a .csv file might look:
first_name,last_name,city
John,Doe,New York
Jane,Doe,Los Angeles
Text (.txt)
.txt format is a simple text file typically separated by tabs, spaces, or other delimiters. This data format does not have any predefined schema, unlike CSV or Parquet. TXT data format is excellent when all the data is of a single type and one wants to avoid the complexities of other formats.
Advantages:
- Can be read by virtually any software.
- Highly flexible.
Disadvantages:
- No standardization.
- Does not support complex data types.
Here’s an example of how a .txt file might look:
John Doe New York
Jane Doe Los Angeles
Parquet
Apache Parquet is a columnar storage format available to any project in the Hadoop ecosystem. Unlike CSV or JSON, Parquet files are binary files that contain metadata about their contents. When performing analytics, a columnar storage like Parquet is more efficient as it facilitates faster query processing.
Advantages:
- Efficient storage and high query speed.
- Schema evolution – fields can be added or removed from the schema over time.
- Supports complex nested data structures.
Disadvantages:
- Since it’s a binary format, it’s not human-readable.
- Not suitable for small datasets.
Note: Reading and writing Parquet files involves using libraries in Java, Python etc. Here’s a simple Python example using PyArrow to write Parquet data:
import pyarrow.parquet as pq
import pandas as pd
# creating a simple dataframe
data = pd.DataFrame({
'first_name': ['John', 'Jane'],
'last_name': ['Doe', 'Doe'],
'city': ['New York', 'Los Angeles']
})
# Writing dataframe to parquet
pq.write_table(data, 'data.parquet')
To summarize, the format you choose for data storage will largely depend on your specific needs in terms of compatibility, efficiency, and the complexity of the data involved. Therefore, a firm understanding of different data storage formats and their advantages and disadvantages will prove to be an asset for the AWS Certified Data Engineer – Associate (DEA-C01) exam and in your role as a data engineer.
Practice Test
Which is one of the most common formats for storing data?
- A. .png
- B. .csv
- C. .mp4
- D. .exe
Answer: B. .csv
Explanation: Comma separated values (.csv) is a very common data file format that uses plain text to store tabular data.
True or False: The .txt format supports both alphanumeric characters and special symbols.
Answer: True
Explanation: The .txt format can store any characters that can be typed on a standard keyboard, including both alphanumeric characters and special symbols.
Which data storage format is optimized for AWS Athena and Redshift Spectrum?
- A. Parquet
- B. .csv
- C. .txt
- D. .docx
Answer: A. Parquet
Explanation: The Apache Parquet format is a columnar storage file format that is optimized for AWS Athena and Redshift Spectrum.
Which of the following data storage format is often used for serialized object storing?
- A. .csv
- B. .json
- C. .txt
- D. .xls
Answer: B. .json
Explanation: .json (JavaScript Object Notation) format is often used for serialized storing of simple data structures and objects.
Which of these file formats can be used for storing hierarchical data?
- A. .txt
- B. .csv
- C. .json
- D. All of the above
Answer: C. .json
Explanation: .json format supports the storage of nested or hierarchical data unlike other formats.
True or False: CSV files do not support different data types in different columns.
Answer: False
Explanation: Despite the simplicity of .csv files, they can support different data types in different columns. However, all the values in a column must be of the same data type.
Which data format is often used where metadata is important along with data?
- A. Parquet
- B. .csv
- C. .txt
- D. .xml
Answer: D. .xml
Explanation: XML (eXtensible Markup Language) is designed to store and transport data with focus on what data is. It provides rich metadata features.
True or False: Parquet files are binary files that run efficiently on the major Hadoop ecosystem.
Answer: True
Explanation: Parquet is a columnar storage file format that is optimized for use with Hadoop ecosystem. It provides efficient data compression and encoding schemes.
Which data format is least human readable?
- A. .csv
- B. .txt
- C. .json
- D. Parquet
Answer: D. Parquet
Explanation: Parquet is a binary file format, which makes it less human-readable than the other text-based options.
Which of the following Storage formats are best suitable for storing huge volume of data ?
- A. .csv
- B. .txt
- C. Parquet
- D. Both: A & B
Answer: C. Parquet
Explanation: Parquet, a columnar storage file format that provides high data compression ratio and optimized query performance, is more suitable for stores of large data volume.
Interview Questions
What is the primary advantage of using .csv file format for data storage in AWS?
The primary advantage of using .csv file format for data storage in AWS is its simplicity and broad compatibility. Almost all data tools recognize and can work with CSV files, allowing for easy data import/export and manipulation.
How is .txt file format typically used in data storage scenarios?
.txt file format in data storage is typically used for storing unstructured data. It’s a simple and unformatted text that can be opened by nearly any software.
What makes Parquet suitable kind of data storage format on AWS?
Parquet is a columnar storage file format that is optimized for use with Amazon S3 due to its columnar nature. It’s highly efficient for analytical querying because it allows specific column-based access of data, offers high data compression, and promotes efficient use of resources.
What eye-catching quality does the .csv format entail for Data Engineers?
The separator character in .csv makes it easy to parse into individual data fields, allowing for quick and simple manipulation and analysis. This makes it a popular choice for data engineers.
How does AWS Glue interpret the .csv and .txt file formats when creating a DynamicFrame?
When AWS Glue encounters .txt or .csv files while creating a DynamicFrame, it interprets the data in these files as a table of data, where each line is a row and individual data fields are separated by a specific character (typically a comma for CSV, or a tab for TXT).
Could you elaborate on the significance of the Parquet format with respect to Big Data solutions on AWS?
Parquet format is integral to Big Data solutions because of its efficiency, compression and integration with advanced data processing frameworks like Apache Hadoop and Apache Spark on AWS. It is ideal for performing operations on Big Data due to its columnar nature.
Is .txt suitable for composing large datasets?
No, .txt format is not ideal for very large datasets as it lacks the structure, efficiency, and data-handling capabilities of other formats such as Parquet or CSV.
What data storage format is best for use with AWS Athena?
AWS Athena works best with columnar data storage formats, like Parquet and ORC, due to their efficiency and performance benefits in read-heavy environments.
Is it possible to store binary data in .txt or .csv format?
No, .txt and .csv are text-based formats and aren’t suitable for storing binary data. For binary data storage, formats like BSON or protocol buffers are more applicable.
How does columnar storage, like Parquet, enhance data querying performance?
Columnar storage allows for more efficient data compression and encoding, speeding up data querying performance. It’s particularly effective for read-heavy workloads as it requires reading only necessary columns from storage, reducing I/O significantly.
Can AWS Glue catalogue recognize Parquet file formats?
Yes, AWS Glue Catalog can recognize and work with Parquet files. It can catalog the metadata of Parquet files, which can then be directly queried using services like Amazon Athena and Amazon Redshift Spectrum.
Can we store images using CSV format?
No, CSV is a text-based format and is not intended or suitable for storing image data.
Are there advantages of saving data in CSV format rather than TXT when using AWS?
Yes, CSV files are generally more convenient to work with than TXT files when dealing with data in AWS. This is because CSV files have a structure that divides data into columns, which makes them easier to process and analyze.
Which AWS service is generally used to convert data from one format like JSON or CSV to another like Parquet?
AWS Glue can be used to convert data from one format to another. It can extract, transform, and load (ETL) data across various data stores.
What is the limitation of Parquet file format?
The primary limitation of Parquet is its lack of support for row-based operations. Since Parquet is a columnar format, it’s optimized for column-wise operations and does an excellent job conserving resources when processing these operations. However, it’s less efficient when it comes to executing row-wise operations.
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