In today’s data-driven world, understanding and measuring what is happening within and between disparate IT systems is paramount. Modern distributed application systems utilizing complex architectures with microservices and cloud-based infrastructure require a thorough understanding of interactions and dependencies between different system components. Observability allows engineering teams to gain insights into a system’s behavior, performance, and health status, allowing for more efficient monitoring, troubleshooting, and optimization.

The term “observability” can be traced back to control theory, which measures how well a system’s internal states can be inferred from knowledge of its external outputs. Today, this concept is applied to modern software development in the form of microservices, serverless computing, and container technologies. To get to the root cause of issues and improve system performance, observability relies on three critical types of telemetry data – logs, metrics, and traces. Alerting provides intelligence from these critical data points.

• Logging: Recording events, activities, and messages generated by a system. Logs provide a historical record of what has happened, aiding in post-event analysis.
• Metrics: Quantitative measurements representing various aspects of a system’s performance, such as response time, throughput, or error rates.
• Tracing: Monitoring and recording the flow of requests as they traverse through different system components. Tracing helps identify bottlenecks and understand the dependencies between various processes and services.
• Alerting: Setting up notifications or alerts based on predefined conditions or thresholds. This allows teams to be notified of issues in real time and take corrective action promptly.

Software Observability vs Data Observability

Software Observability was born out of necessity with the advent of public Cloud technology services introduced by AWS in the mid-2000s. The need to understand the state of the infrastructure components is vital. The fundamental concept is straightforward: in deploying elements of infrastructure such as databases, servers, and API endpoints within a cloud environment, it is imperative to possess a comprehensive awareness of their operational status. This encompasses metrics like the database’s memory usage, the server’s CPU utilization, or the latency exhibited by an API endpoint. As the scale of the infrastructure expands, the necessity for vigilant monitoring intensifies.

To correlate observability back to control theory, the thought is that continuous measurement of sufficient data points from systems makes it possible to infer its internal state as time progresses. This allows for better predictability of system usage performance and enhances the ability to resolve issues when they arise proactively.

Data Observability, on the other hand, differs from Software Observability in that its goal is to answer what information we need to reconstruct a useful picture of our data. This type of observability draws from four main pillars: metrics, metadata, lineage, and logs. More importantly, data lineage is the cornerstone of the four pillars. Data lineage involves comprehending, documenting, and visually representing the data journey from its sources to consumption. Lineage encompasses tracking all transformations the data undergoes throughout the data pipeline, interpreting the changes made, and providing insights into the reasons behind these changes.

• Metrics within data observability can be defined as the internal characteristics of data that provide insights into the performance, health, and behavior of data systems and processes.
• Metadata, commonly known as “data about data,” shows the external characteristics of data to help understand its origin, structure, format, and meaning, enhancing the overall understanding and usability of the primary data.
• Logs serve as a chronological trail of actions and occurrences within a system, providing valuable information for monitoring, troubleshooting, and auditing purposes. Together, these four pillars unify data observability practices and ensure data quality.

Observability Governance

Given the general nature of data security, observability governance is also essential. Observability governance refers to the practices, policies, and processes organizations use to manage and govern observability within their systems. This includes ensuring that observability tools and methods align with business goals, security requirements, and stringent compliance standards.

Security and Data Privacy within Observability Pipelines

Ensuring governance practices adhere to data privacy regulations and security standards and align with business objectives is crucial to data security. When done correctly, access control and associated permissions must be tailored by engineers to align with business functions and roles. Businesses must define who can access different data types, set permission thresholds, and implement role-based access control (RBAC) mechanisms to handle sensitive information appropriately. This is particularly important in financial services, healthcare, and government sectors, where strict compliance requirements exist.

Data Retention Policies

A good rule of thumb is to never store data longer than necessary for the purpose for which the data has been collected or used. Legal requirements, compliance standards, or internal policies may influence this. Retention schedules can aid in this effort as they establish guidelines about how long essential data must be retained for future use or reference and when and how the data can be destroyed when it is no longer needed. Businesses must be able to trace who accessed data, when, and for what purpose. Audit logs and monitoring play an essential role in achieving auditability.

Standardization and Continuous Improvement

Standardized practices for implementing observability across different teams and projects allow business units to speak the same language. This includes defining common metrics, logging formats, and tracing standards akin to the OpenTelemetry observability framework. Teams who follow these best practices will help maximize organizational buy-in while minimizing risks. When following an iterative approach, continuous improvement is the foundation of success. This may involve regular reviews, feedback loops, and updates to governance policies based on evolving business needs.

Key Benefits of Observability

The benefits far outweigh the downfalls of implementing an observability strategy. With the right partner at your side, the benefits can be a force multiplier and enhance every element of the data lifecycle. By implementing observability within your organization, some key benefits emerge, such as:

• Data Quality Assurance: Ensure the quality of your data by monitoring for anomalies, errors, and inconsistencies. This is crucial for maintaining accurate and reliable data and making informed business decisions.
• Proactive Issue Detection: Continuous monitoring of data pipelines and processes enables early detection of data drift, schema changes, and pipeline failures. This proactive approach allows organizations to address problems before they impact downstream systems.
• Enhanced Collaboration: Data observability facilitates collaboration between Data Engineers, DevOps, SRE, and Security teams. Shared visibility into data quality and performance metrics fosters better communication and collaboration.
• Faster Troubleshooting: When data issues arise, observability tools provide insights into the root causes of problems. This accelerates the troubleshooting process, reducing downtime and minimizing the impact on business operations.
• Increased Trust in Data: When organizations can confidently monitor and ensure the quality of their data, it builds trust among users and the customers they serve. Reliable data leads to more confident decision-making and greater confidence in business intelligence and analytics. Moreover, compliance can be seen as a business advantage centered around trust.

Most importantly, cost optimization is identifying and addressing inefficiencies in data processing that can result in cost savings. Data observability helps organizations optimize resource utilization, minimize unnecessary data movements, and reduce operational costs. This is a critical component of a robust data management strategy, contributing to improved data quality, reliability, and the ability to derive valuable insights from data-driven processes. Furthermore, observability allows you to optimize your bottom line, a winning business strategy.

Most Common Observability Use Cases

Observability within Security Information Event Management (SIEM) systems refers to monitoring, analyzing, and gaining insights into security-related events and activities within an organization’s IT environment. SIEM systems play a crucial role in cybersecurity by collecting and correlating log and event data from various sources to detect and respond to security events and incidents. This allows Security, DevOps, and Site Reliability Engineers to understand what’s happening within their technology environments and proactively act accordingly.

Conversely, Observability within Application Performance Monitoring (APM) systems refers to the capability to gain insights into applications’ performance, behavior, and health. APM systems are designed to monitor and analyze various aspects of an application’s execution, allowing developers and operation teams to identify issues, optimize performance, and ensure a positive user experience. This enables software developers to diagnose application performance issues rapidly, point DevOps teams to the problem, apply fixes, and minimize downtime.

How Do You Make a System Observable?

Observability helps teams detect and diagnose issues, optimize performance, and ensure the reliability and stability of a system. It is fundamental to building and maintaining robust and scalable software systems. The observability pipeline serves as a strategic control layer between diverse data sources, enabling users to efficiently ingest data in any format from any source and direct it to any destination for consumption, leading to improved performance and decreased application and infrastructure costs. An effective way to make a system observable is to build a highly flexible observability pipeline.

Enter Datavolo, a dataflow infrastructure pipeline purposely built for complex observability requirements. With the ability to ingest structured, unstructured, programmatic, and sensory data, there’s no stopping how you can utilize this technology to solve the most significant challenges your business faces today. Furthermore, Datavolo is uniquely positioned to excel within the Generative AI and LLM space, specifically regarding multimodal data ingestion and intelligence. Datavolo is building a cloud-native solution centered around Apache NiFi, which is purpose-built to fuel this revolutionary technology. We are thrilled about this once-in-a-lifetime opportunity to spearhead meaningful change in a data-driven world and can’t wait to take this journey with you.

We can’t wait to see what you build! Contact Datavolo today.