7 Important File Formats in Data Engineering You Should Know
When and How to Use the 7 Most Important File Formats (As a Data Engineer)
File formats dictate how well data can be handled, stored, and analyzed, acting as the bridge that connects raw data to actionable insights.
The need for complex data processing pipelines and scalable analytics solutions is only going to grow, and every data engineer has to be aware of the wide range of file formats.
In this article, I’ve listed the seven most important file formats you should know as a data engineer.
1. CSV (Comma-Separated Values)
Key Attributes
- CSV files store tabular data in plain text format, with each line representing a row and fields separated by commas.
- It is simple and widely used for its compatibility with many applications.
- It lacks support for data types and can be inefficient for large datasets.
CSV files are commonly used for exchanging tabular data between different systems or applications. They are popular in scenarios where interoperability and simplicity are more important than advanced features.
CSV files are easily readable by both humans and machines. They are used for tasks such as importing and exporting data from databases, spreadsheets, and analytics tools. However, they might not be the best choice for large datasets due to their lack of data type information and inefficient storage.
Name, Age, City
John, 30, New York
Alice, 25, Los Angeles
Bob, 35, Chicago2. JSON (JavaScript Object Notation)
Key Attributes
- JSON is a lightweight data interchange format that is human-readable and easy for both humans and machines to understand.
- It is widely used for representing structured data, particularly in web applications and APIs.
- JSON supports nested structures and is often used for semi-structured data.
JSON is often used in web development, APIs, and configurations where data needs to be stored or transmitted in a structured format. It’s especially useful when dealing with semi-structured data.
JSON files store data as key-value pairs in a hierarchical structure. They are used for tasks such as storing application settings, exchanging data between web services, and serializing complex data structures in programming languages.
{
"name": "Nnamdi Samuel",
"age": 30,
"email": "Nnamdi@example.com",
"address": {
"street": "123 Main St",
"city": "Anytown",
"state": "CA",
"zipcode": "12345"
},
"phone_numbers": [
{
"type": "home",
"number": "555-1234"
},
{
"type": "work",
"number": "555-5678"
}
]
}3. Parquet
- Parquet is a columnar storage file format optimized for use with big data processing frameworks like Apache Hadoop, Apache Spark, and others.
- It stores data in a columnar fashion, which improves query performance by minimizing I/O operations and allowing for efficient column-wise compression.
- Parquet is well-suited for analytics workloads on large datasets.
Parquet is commonly used in big data processing frameworks like Apache Hadoop and Apache Spark. It’s ideal for analytical workloads that involve scanning large volumes of data.
Parquet files store data in a columnar format, which makes them efficient for analytics queries that typically access only a subset of columns. They are often used for data warehousing, data lakes, and analytical applications where performance and scalability are critical.
+-------+-----+------------+
| Name | Age | City |
+-------+-----+------------+
| John | 25 | New York |
| Emily | 30 | Los Angeles|
| David | 40 | Chicago |
+-------+-----+------------+4. Avro
Key Attributes
- Avro is a data serialization system that provides rich data structures, a compact binary format, and a schema definition language.
- It is designed to be fast and compact, making it suitable for data serialization and data exchange in distributed systems.
- Avro supports schema evolution, meaning data schemas can evolve without breaking compatibility.
Avro is used in distributed systems and data processing pipelines where schema evolution and efficient serialization are important.
Avro files store data along with its schema in a compact binary format. They are used for tasks such as data serialization in Apache Kafka, message passing between systems, and storing data in distributed file systems like Hadoop’s HDFS.
{
"name": "Nnamdi",
"age": 25,
"city": "Lagos"
}
{
"name": "Emily",
"age": 30,
"city": "Los Angeles"
}
{
"name": "David",
"age": 40,
"city": "Chicago"
}5. ORC (Optimized Row Columnar)
Key Attributes
- ORC is another columnar storage file format designed for Hadoop workloads.
- It provides efficient compression and encoding schemes to reduce storage space and improve query performance.
- ORC files support complex data types, predicate pushdown, and schema evolution.
ORC files are used in scenarios similar to Parquet, where columnar storage and efficient query performance are required.
ORC files store data in a columnar format with advanced compression techniques, making them suitable for analytics workloads. They are often used in conjunction with Apache Hive, Apache Impala, and other big data processing tools for data analysis and reporting.
Name | Age | City
---------------------
John | 25 | New York
Emily | 30 | Los Angeles
David | 40 | Chicago6. XML (Extensible Markup Language)
Key Attributes
- XML is a markup language that defines rules for encoding documents in a format that is both human-readable and machine-readable.
- It is used for representing structured data and is commonly used in web services and configuration files.
- XML is less efficient compared to JSON for data interchange due to its verbosity, but it is still widely used in certain domains.
XML is used in scenarios where data needs to be represented in a hierarchical structure and human readability is important.
XML files store data using markup tags in a tree-like structure. They are commonly used in web services, configuration files, and data interchange formats where data needs to be both machine-readable and human-readable.
<?xml version="1.0" encoding="UTF-8"?>
<catalog>
<book id="001">
<title>Harry Potter and the Sorcerer's Stone</title>
<author>J.K. Rowling</author>
<genre>Fantasy</genre>
<price>20.00</price>
<publish_date>1997-06-26</publish_date>
</book>
<book id="002">
<title>The Great Gatsby</title>
<author>F. Scott Fitzgerald</author>
<genre>Classic</genre>
<price>15.00</price>
<publish_date>1925-04-10</publish_date>
</book>
</catalog>7. Apache Thrift
Key Attributes
- Apache Thrift is a framework for scalable cross-language service development. It includes a serialization mechanism for data interchange that supports multiple programming languages.
- It is often used in distributed systems for communication between services and data serialization.
Apache Thrift is used in distributed systems for communication between services written in different programming languages.
Thrift defines a serialization mechanism that allows data to be exchanged efficiently between different components of a distributed system. It’s commonly used in microservices architectures, where services need to communicate with each other over a network.
namespace java example
struct Person {
1: required string name,
2: required i32 age,
3: optional string city
}
service PersonService {
bool addPerson(1: Person person),
list<Person> getPeople()
}There’s more to these file formats listed here.
From the simplicity of CSV to the efficiency of Parquet, each file format offers a unique set of features tailored to meet the diverse needs of data processing pipelines.
You can guarantee effective Big Data processing, analysis, and storage by making well-informed judgments based on your grasp of each file format’s unique properties and ideal applications.
