Introduction

In today’s data-driven world, the ability to process diverse data formats efficiently is a cornerstone of effective security analytics. The Amazon AWS REST API protocol in IBM QRadar enables seamless data ingestion from AWS services through robust and flexible file processors.

With the emergence of Netskope’s Direct-to-Cloud (DTC) Solution, vast volumes of event data are now exported directly into AWS S3 in CSV (Comma-Separated Values) format — offering scalability, cost-efficiency, and speed. However, while CSVs are widely used, they come with challenges such as missing headers, inconsistent structures, and complex field mappings.

To overcome these limitations, IBM QRadar introduces the Generic CSV File Processor — a major enhancement for the Amazon AWS REST API protocol, designed to make CSV ingestion from S3 effortless, reliable, and highly configurable.

What’s New: The Generic CSV File Processor

The Generic CSV processor introduces enhanced flexibility for ingesting and parsing CSV files directly from Amazon S3 buckets. It simplifies the entire ingestion pipeline while supporting multiple output options for seamless integration with existing QRadar workflows.

This new enhancement also supports different compressed file formats, enabling faster, more efficient data handling — even for very large datasets.

Supported File Compression Formats

The processor now automatically detects and processes compressed files, eliminating the need for manual decompression prior to ingestion. Supported formats include:

• .zst / .zstd (Zstandard) — A modern compression algorithm offering exceptional speed and compression efficiency.
• .gz / .gzip — The widely used Gzip format for compact file storage and transfer.

With automatic decompression, QRadar can seamlessly process large volumes of compressed event data, streamlining workflows and reducing preprocessing overhead.

Key Features and Configurable Options

The new Generic CSV processor adds several user-friendly configuration options to enhance parsing accuracy and control over event display:

1. Output Format
• Choose between two output display options:
• JSON — Ideal for structured downstream integrations.
• Name-Value Pair — Simplified view for quick analysis and readability.
2. CSV Delimiter
• Define the delimiter character used in your CSV files (e.g., comma ,, semicolon ;).
3. Suppress Empty Fields
• Automatically removes null or blank values from ingested data, ensuring cleaner datasets and more efficient processing.

Deep Dive: Real-World Use Cases

The Generic CSV Processor operates using QRadar’s modular ingestion framework. When configured, it connects to an AWS S3 bucket, scans for new CSV or compressed CSV files, decompresses them automatically, parses headers, and outputs structured event data.

To better understand how customers can benefit from this enhancement, let’s look at three common scenarios where the Generic CSV Processor delivers measurable value.

Use Case 1: A QRadar administrator wants to process data stored in Zstandard-compressed CSV files and display the output in JSON format.

Configuration Steps:

• Specify a valid Amazon S3 bucket and directory prefix.
• Select Event Format = Generic CSV.
• Choose Output Format = JSON.

Result:
QRadar automatically detects and decompresses the .zst file, extracts the CSV data, and displays events in a well-structured JSON format. The output includes detailed metadata and mapped headers, ensuring clarity and consistency.

Use Case 2: A QRadar administrator wants to process data stored in Zstandard-compressed CSV files but prefers output in Name-Value Pair format for quick readability.

Configuration Steps:

• Configure a valid Amazon S3 bucket.
• Select Event Format = Generic CSV.
• Choose Output Format = Name-Value Pair.

Result:
QRadar automatically decompresses and parses the CSV files, displaying event data in a clean, readable Name-Value Pair format — perfect for fast troubleshooting and manual inspection without sacrificing data accuracy.

Use Case 3: A QRadar administrator wants to process data stored in Zstandard-compressed CSV files and want to retain blank values in file to verify data completeness and ensure accurate mapping.

Configuration Steps:

• Configure a valid Amazon S3 bucket.
• Select Event Format = Generic CSV.
• Choose Suppress Empty Fields = No.

Result:
QRadar processes the compressed CSV and displays all headers, including those with blank values. This setup helps administrators verify data structures and field mappings without losing visibility into missing or incomplete fields.

Conclusion

The introduction of the Generic CSV Processor for the Amazon AWS REST API protocol marks a significant enhancement in QRadar’s data ingestion capabilities.
By supporting automatic decompression, flexible CSV parsing, and customizable output formats, this update simplifies event collection from S3 and improves operational efficiency.

This new feature delivers a smoother, faster, and more scalable data processing experience, allowing users to focus on analysis rather than data preparation.