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Logstash: what is it and how to use it for data processing?

Written by Henrique Canha | 28/04/25 11:20

Nowadays, organizations face the challenge of collecting, cleaning, transforming, and routing vast amounts of data from different sources. This is where Logstash, a core component of the Elastic Stack, becomes a critical solution.

Logstash is an open-source data processing pipeline that allows you to ingest data from multiple sources simultaneously, transform it on the fly, and send it to your desired destination - most commonly Elasticsearch. Its flexibility, extensibility, and strong integration with the Elastic Stack make it an ideal choice for modern data processing architectures.

What is Logstash, and how does it work?

Logstash acts as a real-time pipeline that ingests, parses, and transforms data before forwarding it for storage or analysis. It supports a wide variety of input sources, including logs, metrics, files, cloud services, databases, and more.

It works in three core stages:

  • Input: Collect data from various sources.

  • Filter: Transform and enrich the data using built-in or custom plugins.

  • Output: Forward the processed data to one or more destinations, such as Elasticsearch or another database.

With hundreds of plugins available, Logstash can adapt to several enterprise use cases. It supports structured and unstructured data and can handle complex parsing logic, making it ideal for logs, events, and time-series data.

Benefits of using Logstash for data processing

  • Centralized data collection: Aggregate data from different sources into a single pipeline, simplifying infrastructure and analysis.

  • On-the-fly transformation: Clean, enrich, and reformat data before it reaches its destination, ensuring consistency and usability.

  • Scalability: Designed to process large volumes of data efficiently, Logstash can scale horizontally to meet growing data needs.

  • Extensibility: Supports custom plugins and integrations, making it easy to adapt to unique data workflows and formats.

  • Tight integration with Elasticsearch: Optimised to work seamlessly with Elasticsearch, enabling real-time search and analytics right after ingestion.

Related article: Elastic Stack: How to integrate Elasticsearch, Logstash, and Kibana?

Logstash common use cases

Logstash is used in diverse industries and technical contexts. Common use cases include:

  • Log and event processing: Parse logs from servers, applications, containers, or network devices and forward them to Elasticsearch for analysis.

  • Security information and event management (SIEM): This process collects, filters, and routes security data to centralized platforms to support real-time threat detection.

  • Operational monitoring: Ingest performance metrics and infrastructure events to monitor system health and availability.

  • Business data pipelines: Transform raw data from CRMs, ERPs, or e-commerce platforms into structured formats for business intelligence.

Why Logstash is essential for your Elastic Stack strategy

While Elasticsearch and Kibana are essential for storing and visualizing data, Logstash provides the structure and transformation layer that ensures your data is clean, contextualized, and usable.

Without this processing layer, organizations risk feeding inconsistent or unstructured data into Elasticsearch, leading to inaccurate analysis or poor search performance.

Logstash ensures:

  • Data integrity and enrichment at ingestion

  • Improved performance of Elasticsearch through optimized data structures

  • Better data insights through consistent formatting

Partner with Syone for Logstash-powered data processing

At Syone, we help organisations design and implement intelligent data pipelines using Logstash, ensuring efficient data ingestion, transformation, and delivery.

As an Elite Elastic Partner and the only official Portuguese partner, our team provides:

  • Logstash pipeline architecture and plugin strategy

  • Integration with diverse data sources and systems

  • Security and compliance best practices for data ingestion

  • Ongoing monitoring, optimization, and support

Our experts tailor every solution to your business's specific requirements, ensuring reliable, scalable, and secure data processing.

Related article: OpenSearch vs Elasticsearch: What are the differences and how to choose?

Conclusion

Logstash plays a foundational role in building robust data pipelines. Its ability to handle high-throughput, multi-source data ingestion with real-time transformation makes it a core component of any modern data architecture.

With Syone’s expertise, your organization can fully leverage Logstash's capabilities to transform raw data into valuable, actionable insights.

Talk to one of our experts or learn more at https://www.syone.com/elastic.