• Pradip
• 10 Dec, 2025
• 0 Comments
• 4 Mins Read

Configuration Data Formats in Data Engineering

Configuration Data Formats in Data Engineering: A Complete Guide

In modern data engineering ecosystems, configuration files play a crucial role in defining how systems behave, connect, scale, and interact with each other. Whether you’re maintaining ETL pipelines, scheduling workflows, deploying infrastructure, or integrating APIs—configuration data formats form the backbone of automation and reproducibility.

This blog explores the most widely used configuration data formats JSON, YAML, XML, TOML, INI, and more—explaining where each shines and how they compare.

Let’s dive deep into the formats every data engineer should master.

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What Are Configuration Data Formats?

Configuration data formats are structured text formats used to store settings, parameters, and environment-specific values that define how software systems operate.

They offer:

• Flexibility

• Portability

• Version control compatibility

• Separation of code & configuration (best practice)

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Why Configuration Formats Matter in Data Engineering

Configuration files impact every stage of data engineering:

• Pipeline Orchestration (e.g., Airflow, Dagster)

• Data Processing Frameworks (Spark, Flink)

• API Integrations

• Cloud Infrastructure (IaC) (Terraform, CloudFormation)

• Containerization & Deployment (Docker, Kubernetes)

• Metadata & Schema Definitions

Choosing the right configuration format directly affects readability, maintainability, automation, and performance.

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Commonly Used Configuration Data Formats

1. JSON (JavaScript Object Notation)

Why Data Engineers Use JSON

JSON is lightweight, easy to parse, supported across all programming languages, and widely used in:

• APIs

• Logging

• NoSQL databases (e.g., MongoDB)

• Cloud configurations

Pros

• Human-readable

• Universal language support

• Fast parsing

• Great for nested structures

Cons

• No comments allowed

• Verbose for large configs

Use Cases

• API request/response formats

• Spark configs (spark-submit --conf)

• Metadata files

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2. YAML (YAML Ain’t Markup Language)

Why YAML Is Popular in DevOps & Data Engineering

YAML is extremely human-friendly and supports comments, making it ideal for complex configuration needs.

Used in:

• Kubernetes manifests

• Airflow DAG configurations

• Docker Compose

• Ansible

Pros

• Very readable

• Supports comments

• Cleaner and shorter than JSON

Cons

• Indentation-sensitive (error-prone)

• Slower parsing

Use Cases

• Workflow orchestration

• Infrastructure automation

• Pipeline configuration

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3. XML (Extensible Markup Language)

Where XML Still Dominates

Though older, XML remains powerful in systems that require strict schema validation.

Widely used in:

• Enterprise systems

• Hadoop ecosystem (HDFS, Yarn configurations)

• SOAP APIs

Pros

• Rigid structure

• Validation using XSD

• Supports metadata-rich configs

Cons

• Verbose

• Harder to read compared to YAML/JSON

Use Cases

• Hadoop configuration files

• Legacy data platforms

• Configurations requiring strict schema compliance

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4. TOML (Tom’s Obvious, Minimal Language)

A Rising Star in Modern Data Stack

TOML is simple, readable, and ideal for Python-based tools (used in PyProject.toml).

Pros

• Cleaner than INI

• Supports complex data types

• Great readability

Cons

• Not as widely adopted as JSON/YAML

Use Cases

• Python application configs

• Tooling configurations (Poetry, Rust, Go projects)

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5. INI Files

Traditional but Effective

INI files are key-value based and commonly used in lightweight applications.

Pros

• Simple syntax

• Easy to maintain

Cons

• Limited nesting

• No standard specification

Use Cases

• Local system configs

• Application-level settings

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Side-by-Side Comparison of Configuration Formats

6. Comparison Table

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How to Choose the Right Configuration Format

1. Use JSON when:

• You need a lightweight, widely supported format

• You’re working with APIs or event-driven systems

2. Use YAML when:

• Configs are large and complex

• Files are part of DevOps workflows (K8s, Ansible)

3. Use XML when:

• You need strict schema validation

• Using legacy or enterprise frameworks

4. Use TOML when:

• Working with Python modern project tooling

5. Use INI when:

• Config needs are minimal and simple

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Best Practices for Configuration Management

1. Separate Configuration from Code

Keep configs external and environment-specific.

2. Use Version Control (Git)

Track changes and enable team collaboration.

3. Use Environment Variables for Secrets

Never store credentials in plain config files.

4. Validate Configuration Automatically

Use schema validation where possible (e.g., JSON schema).

5. Use Configuration Management Tools

Such as:

• HashiCorp Consul

• AWS Systems Manager Parameter Store

• Azure App Configuration

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Real-World Examples

Example 1: Airflow YAML Config

Example 2: Spark JSON Config

Example 3: Hadoop XML Config

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FAQs

1. Which configuration format is best for data engineering?

YAML and JSON are the most widely used due to readability and cross-platform compatibility.

2. Why do Kubernetes and Airflow prefer YAML?

Because YAML supports clean, hierarchical configuration with comments.

3. Is JSON faster than YAML?

Yes—JSON parsing is generally faster because it follows a simpler structure.

4. Can I convert between JSON, YAML, and XML?

Yes, tools like yq, jq, and various IDE plugins can convert formats easily.

5. Should secrets be stored in configuration files?

No. Always store secrets in environment variables or dedicated secret managers.

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Conclusion

Configuration data formats are foundational to data engineering success. Understanding when and where to use JSON, YAML, XML, TOML, or INI enables cleaner, scalable, and more maintainable data systems.

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