Over the past decade, APMs, logs, and infrastructure monitoring tools have become the norm and table stakes expectation in any tech team where their software has a significant impact on revenue or customer experience.

A security engineer might use the same set of logs to look for any foreign attack, an engineer to check errors faced by users, and a business analyst to run some analytics.

While this sounds great, it becomes a challenge when different engineers have different de-facto tools to make the same analysis (e.g., Splunk SIEM by the Security team, Cloudwatch/New Relic by engineers, and Snowflake by analysts).

This is added to the complexity of constraints and requirements that different tools have—a security team needs complete access. In contrast, the levels of redaction or the specific logs required by various teams vary.

Observability pipelines act as an intersecting layer for integration.

An Observability Pipeline, also known as a Telemetry Pipeline, is a crucial component within modern technology stacks that manages, optimizes and analyzes telemetry data—logs, metrics, traces—from various sources. This system enables Security and DevOps teams to efficiently parse, route, and enrich data, facilitating informed decision-making to enhance system performance and maintain security within budgetary constraints. 

AI has transformed these pipelines into more cost-effective and efficient tools by incorporating AI-driven data optimization, intelligent routing, and anomaly detection.

Observability pipelines are increasingly integral to engineering teams' stacks, delivering three primary benefits:

• Flexibility & Vendor Independence: Observability pipelines prevent vendor lock-in by acting as a central hub for data from disparate sources. Teams can choose the most suitable tools for their needs, whether storing, analyzing, or visualizing data, without being tied to a single vendor.
• Cost Reduction: These pipelines optimize data flow, reducing the volume of low-value data and focusing on high-quality, actionable insights. This diminishes storage and processing costs and streamlines analysis, making observability financially sustainable even as data volumes grow.
• PII Redactions at Source: Observability pipelines minimize the risk of sensitive data leaks by implementing routing and filtering logic at the source. With configurable UIs or code to set up these rules, pipelines ensure compliance with data protection regulations by effectively managing data Privacy.

Observability pipeline tools elevate the game by not just showing that there's an issue (like traditional monitoring) but by digging into the 'why' and 'how' behind the scenes. It's the difference between seeing smoke and understanding what's burning and how to extinguish it effectively. With software systems becoming increasingly complex and distributed, this level of insight isn't just nice to have; it's crucial.

When selecting an observability pipeline tool, consider these essential features:

• Compatibility Across Environments: Choose a tool that integrates seamlessly with various setups, whether cloud, on-premises, or hybrid, ensuring comprehensive visibility across your entire network.
• Holistic Visibility: Opt for tools that provide a 360-degree view of your systems, offering deep insights into network health and performance.
• Efficient Data Processing: Efficiency in managing and processing large data volumes is crucial. Look for tools that optimize data storage and retrieval to keep operational costs under control.
• Robust Security and Compliance: Essential features should include robust mechanisms to protect sensitive data and ensure compliance with relevant regulations.
• Smart Alerting and Anomaly Detection: Advanced alerting capabilities and anomaly detection using AI and machine learning can help pinpoint critical issues before they escalate, allowing for proactive management.
• Intuitive Dashboards and Customization: The best tools provide customizable, easy-to-navigate dashboards that display real-time data, making them invaluable for quick decision-making.

Moreover, observability pipelines elevate traditional monitoring by providing insights into what is happening and why—offering a deeper understanding of the underlying causes. This enables engineering teams to:

• Detect Problems in Real-Time: Identify and address issues as they occur, often before they impact users.
• Receive Intelligent Alerts: Focus on significant alerts that matter, reducing noise and enhancing productivity.
• Gain Predictive Insights: Use advanced analytics to foresee and mitigate potential issues proactively.

In this section, we will cover some popular tools and they are listed below: 

1. Cribl
2. Vector.dev
3. Nimbus.dev
4. Edge Delta
5. Sensu
6. Dynatrace Open Pipeline
7. Logstash
8. Fluentd