Key Takeaways

• Observability pipelines are essential for managing the ever-growing volume of telemetry data (logs, metrics, traces) efficiently, enabling optimal security, performance, and stability within budget constraints.
• They address challenges such as data overload, legacy architectures, rising costs, compliance and security risks, noisy data, and the need for dedicated resources.
• Observo.ai's AI-powered Observability Pipeline offers solutions like data optimization and reduction, smart routing, anomaly detection, data enrichment, a searchable observability data lake, and sensitive data discovery, significantly reducing costs and improving incident resolution times.

Introduction

An Observability Pipeline is a transformative tool for managing complex system data, optimizing security, and enhancing performance within budget. Observo.ai revolutionizes this with AI, slashing costs and streamlining data analysis to empower Security and DevOps teams.

Q: What is an Observability Pipeline?

‍A: An Observability Pipeline, sometimes called a Telemetry Pipeline is a sophisticated system designed to manage, optimize, and analyze telemetry data (like logs, metrics, traces) from various sources. It helps Security and DevOps teams efficiently parse, route, and enrich data, enabling them to make informed decisions, improve system performance, and maintain security within budgetary constraints. Observo.ai elevates this concept with AI-driven enhancements that significantly reduce costs and improve operational efficiency.

Overview

Observability is the practice of asking questions about the inner workings of a system or application based on the data it produces. It involves collecting, monitoring, and analyzing various data sources (logs, metrics, traces, etc.) to comprehensively understand how the system behaves, its performance, and potential security threats. Another important practice is telemetry, which involves collection and transmission of data from remote sources to a central location for monitoring, analysis and decision making. Logs, metrics, events and traces are known as the four pillars of Observability.

The telemetry data collected and analyzed by Security and DevOps teams in their observability efforts is growing at an unrelenting pace – for some organizations, as much as 35% year over year. That means that costs to store, index, and process data are doubling in a little more than 2 years. Some of our larger customers spend tens of millions of dollars a year just to store and process this data.

An observability pipeline or a telemetry pipeline can help Security and DevOps teams get control over their telemetry data such as security event logs, application logs, metrics, traces et al. It allows them to choose the best tools to analyze and store this data for optimal security, performance, and stability within budget requirements. Observability pipelines parse and shape data into the right format, route it to the right SIEM and Observability tools, optimize it by reducing low-value data and enriching it with more context, and empower these teams to make optimal choices while dramatically reducing costs.

Challenges of Observability

• Data Overload: Security and DevOps teams need help to keep pace with the growth of telemetry data used for observability efforts. This leads them to make sub-optimal choices about what data to analyze, how heavily to sample, and how long to retain data for later security investigations and compliance. Budget is often the culprit driving decisions about how much data to analyze, but it can impair enterprise security, performance, and stability if these teams lack a complete view of their environment.
• Legacy Architectures: Traditional architectures, relying on static, rule-based methods and indexing for telemetry data processing and querying, struggle to adapt to the soaring data volumes and dynamic nature of modern systems. As the scale of data expands, these static methods fail to keep pace, endangering real-time analysis and troubleshooting. Log data constantly changes with new releases and services. Static systems need constant tuning to stay on top of this change which can be time-consuming and difficult without seasoned professionals at the helm.
• Rising Costs: As telemetry data volumes grow, so do the costs of storing, processing, and indexing this data. Many customers report that storage and compute costs are the same or more than their SIEM and log analytics license costs. Because budgets remain flat or decrease, rising costs force decisions about which data can be analyzed and stored - jeopardizing security, stability, and compliance goals. 
• Compliance and Security Risks: As telemetry data grows, it becomes increasingly challenging to keep private identifiable information (IPI) secure. Recent reports suggest that data breaches from observability systems have increased by 48% in just the last two years. Manual efforts to mask this data rely on in-depth knowledge of data schemas to try to protect PII. Unfortunately, those efforts fall short. PII like social security numbers, credit card numbers, and personal contact information is often found in open text fields, not just the fields you would expect. This leaves organizations vulnerable to data breaches in new and troubling ways and makes compliance efforts even more challenging.
• Noisy Data Overwhelming Useful Signal: About 80% of log data has zero analytical value, yet most teams are paying to analyze all of it. This adds to the cost challenges we’ve mentioned and limits the flexibility of getting a comprehensive and holistic view of observability into core systems. All of this noise also makes SIEM and Observability systems work much harder. It’s easy to find a needle in a really small haystack.  If that haystack gets really big you might need more people to help you find that one important needle. The same is true for SIEM and log management tools. Too much data requires much more CPU power to index and search through it and costs 80% more than it should to store it.
• Lack of Dedicated Resources: Most of our customers deploy large teams to tackle these challenges before using Observo. They develop an intimate knowledge of the telemetry data and tools designed to optimize observability. This draws them away from working on proactive efforts to improve security, performance, reliability, and stability and other projects that bring a lot of value to their organization. The most skilled and knowledgeable of these teams also leave over time. If the systems are heavily reliant on their expertise, this puts the strength of observability in jeopardy.

The Observo.ai Observability Pipeline

Observo.ai has developed an AI-powered Observability pipeline to address these challenges. Our customers have reduced observability costs by 50% or more by optimizing telemetry data such as Security event logs, application logs, metrics, and others, and by routing data to the most cost-effective destination for storage and analysis. By optimizing and enriching data with AI-generated sentiment analysis, our customers have cut the time to identify and resolve incidents by more than 40%. Built in Rust, Observo.ai's Observability pipelines are extremely fast and designed to handle the most demanding workloads. Here are some of the key ways our solution addresses your biggest observability challenges.

• Data Optimization and Reduction: We have found that only 20% of log data has value. Our Smart Summarizer can reduce the volume of data types such as VPC Flow Logs, Firewall logs, OS, CDN, DNS, Network devices, Cloud infrastructure and Application logs by more than 80%. Teams can ingest more quality data while reducing their overall ingest and reduce storage and compute costs. Many customers reduce their total observability costs by 50% or more.
• Smart Routing: Observo.ai’s observability pipeline transforms data from any source to any destination, giving you complete control over your data. We deliver data in the right format to the tool or storage location that makes the most sense. This helps customers avoid vendor lock-in by giving them choices about how to store, index, and analyze their data.
• Anomaly Detection : The Observo.ai observability pipeline learns what is normal for any given data type. The Observo.ai Sentiment Engine identifies anomalies and can integrate with common alert/ticketing systems like ServiceNow, PagerDuty, and Jira for real-time alerting. Customers have lowered mean time to resolve (MTTR) incidents by 40% or more.
• Data Enrichment: Observo enriches data to add context. Observo.ai’s models assign “sentiment” based on pattern recognition, or add 3rd party data like Geo-IP and threat intel. Sentiment dashboards add valuable insights and help reduce alert fatigue. By adding context, teams achieve faster, more precise searches and eliminate false alarms that can mask real ones.
• Searchable, Full-Fidelity, Low-cost Observability Data Lake: The Observo.ai observability pipeline helps you create a full-fidelity observability data lake in low-cost cloud storage. We store data in Parquet file format making it highly compressed and searchable. You can use natural language queries, so you don’t need to be a data scientist to retrieve insights from your observability stack. Storing this data in your SIEM or log management tool can cost as much as a hundred times more than in an Observo.ai data lake.  This helps you retain more data, for longer periods of time, spend less money, and be a lot more flexible.
• Sensitive Data Discovery: Observo.ai proactively detects sensitive and classified information in telemetry data flowing through the Observability pipeline, allowing you to secure it through obfuscation or hashing wherever it sits. Observo.ai uses pattern recognition to discover all sensitive data, even if it’s not where you’d expect it to be or in fields designated for PII.

Use Cases

There are numerous use cases for Observability pipelines and how they can help organizations solve challenges. Primarily, they are a combination of the challenges mentioned above. Here are some examples that we have seen with organizations of various sizes.

• Get data from Splunk forwarder, optimize the data and send to Splunk. Route the raw data in optimized parquet schema to data lake on AWS S3
• Ingest Cisco Firewall events and Windows event logs from Kafka topic. Send the optimized data to Azure Sentinel and full fidelity data to a Snowflake data lake
• Collect logs from OpenTelemetry agent, reduce the noise and send the optimized data to Datadog
• Receive data from Cribl Logstream, reduce the data volume, mask PII data and route it to Exabeam. A full fidelity copy in JSON format is sent to an Azure Blob Storage data lake
• Ingest VPC Flow logs and CloudTrail events from AWS Kinesis, reduce the noise and send optimized data to Elasticsearch

Conclusion

An observability pipeline is a critical tool for cutting costs, managing data growth, and giving you choices about what data to analyze, which tools to use, and how to store it. An AI-powered observability pipeline elevates observability with much deeper data optimization, and automated pipeline building, and makes it much easier for anyone in your organization to derive value without having to be an expert in the underlying analytics tools and data types. Observo.ai helps you break free from static, rules-based pipelines that fail to keep pace with the ever-changing nature of your data. Observo.ai helps you automate observability with a pipeline that constantly learns and evolves with your data. 

Learn More

For more information on how you can save 50% or more on your SIEM and observability costs with the AI-powered Observability Pipeline, Read the Observo.ai White paper, Elevating Observability with AI.