Enterprise Observability Platform: What it takes to build one?

Observability

In the digital age, where services are delivered through complex and distributed systems, maintaining operational efficiency and stability is paramount. Observability – not just a buzzword but a crucial framework – enables businesses to understand and monitor their systems’ internal states from the external outputs they produce during runtime. An effective observability platform helps organisations detect unexpected behaviour, understand intricate system dependencies, and improve decision-making through actionable insights.

Why Any Enterprise Needs an Observability Platform

The necessity for an observability platform in any enterprise primarily stems from the need to ensure high availability, performance optimization, and anomaly detection in increasingly complex IT environments. With the shift towards microservices, serverless architectures, and cloud-native technologies, traditional monitoring tools often fall short. Observability fills this gap by providing deeper insights into system performance and health through metrics, logs, and traces. This holistic view allows enterprises to:

• Proactively address issues before they affect the user experience.
• Optimise resources to reduce costs and improve efficiency.
• Scale systems effectively in response to real-time data.

Choices for Enterprises: Off-the-Shelf vs. Custom Solutions

When considering the implementation of an observability platform, enterprises face a crucial decision: opting for a pre-built, off-the-shelf solution or developing a bespoke system tailored to their specific needs.

Off-the-Shelf Solutions

These are comprehensive, ready-to-deploy software that may offer ease of integration, standardisation, and immediate deployment. Examples include platforms like Datadog, New Relic, or Splunk.

Custom Solutions

Building a custom observability platform allows for greater flexibility, optimization of resources, and fine-tuning of features to meet the unique operational dynamics of the enterprise.

Comparison of both approaches

Key Aspects	Off The Shelf	Custom built
Cost	Mostly subscription based and can generally be expensive over a period of time.	High initial costs but lower longer term expenses.
Customization	Limited to what is provided by the platform vendor.	Highly customizable as per enterprise needs. Within an enterprise varying requirements of different teams can be met.
Vendor lock-in	Dependency on vendor and their platform	Independent of vendors or can be designed to swap out vendor components
Integration	Generally higher 3rd party support is available.	Need to build integrations as per requirements
Monitoring coverage	Standard out of the box monitoring available, but may require additional PS (professional services) contracts for custom integration	Built grounds up for enterprise specific needs and hence legacy challenges can be easily solved with forward/backward compatibility
Security	Mostly covered by the vendor	Full control in what and how you want to implement security guard rails.

Custom Observability Platform Composition

Let’s now look into what goes into building a custom observability platform.

Observability has transcended its original confines of mere resource monitoring to become a comprehensive discipline that encompasses monitoring, evaluation, alerting, and the strategic redirection of insights and data across an organisation. Modern observability platforms do not just watch over systems but actively participate in a feedback loop that channels critical alerts and data streams to various downstream systems. These systems handle archival for long-term analysis, immediate operational adjustments, and provide feedback that fuels continuous improvement and innovation. 

With such an ever-expanding scope, the agility to quickly rearrange and orchestrate these complex pipelines becomes paramount. This necessitates exploring custom solutions that can flexibly adapt to evolving business needs and technological landscapes, ensuring that the observability infrastructure remains both robust and dynamic. 

Custom observability solutions empower enterprises to design and modify their data flows with precision, enhancing efficiency and responsiveness across their operations. The below illustration represents building blocks that compose a flexible, resilient and configurable observability solution.

At a very high level the Observability platform can be decomposed into 3 layers – INGESTION, INSPECTION & INSIGHTS (INCIDENT).

INGESTION

This is the phase where everything that is needed to be monitored is setup, instrumented and a framework is used to configure and manage this. To enable the ingestion effectively, a choice of a versatile ingestion framework such as CRIBL is key. This starting part of the Observability pipeline is made up of:

1. Agent Framework: The Observability platform should allow for easy deployment of agents across your infrastructure, with minimal overhead. Validate compatibility of operating systems while selecting your agent framework. Effective management of monitoring agents is crucial for scalability and reliability in enterprise environments. The Observability agent framework must provide a centralised way to manage agent configurations, ensuring that all agents are consistently configured according to the latest policies and requirements.  It needs to monitor the health and performance of the agents themselves, providing alerts if agents fail or deviate from expected performance thresholds. It also facilitates updates and patches to agents without significant downtime.

2. Monitoring/Instrumentation: Agents can generate several types of monitoring data such as:

• OS Metrics: Operating System metrics provide foundational visibility into the health and performance of both physical and virtual machines. They typically include CPU usage, memory utilisation, disk I/O, network statistics, and more.
• Metric and Log Ingestion: Evaluate tools for their efficiency in ingesting diverse metrics and logs at scale. Consider technologies like Fluentd or Logstash.
• Remote Script Execution: Custom scripts are often used to capture specific metrics or logs that standard agents might not cover. These can range from performance metrics of custom applications to security audits. Your observability agent framework should be able to ingest the output from custom scripts executed on hosts. It must be able to parse, format, and transform this data before it’s sent to downstream analytics or other monitoring systems.
• Service Monitoring: Service monitoring involves tracking the availability and performance of critical applications and services. This includes web servers, databases, and application backends.
• Log Ingestion: Logs provide detailed insights into the operations of systems and applications, capturing everything from user activities to error messages. The system must support aggregating logs from various sources, parsing and extracting valuable data from each log entry. This can include converting unstructured logs into structured data that is easier to analyse.

INSPECTION

This is the phase where the ingested data needs to be looked into for transformation. Before directly evaluating the incoming data against alert rules, data needs to be cleaned up for being suitable to derive insights. The following functions need to be supported in your Observability platform to prepare data insights in this phase:

1. Data Normalisation and Enrichment: Once the data is ingested, the platform needs to normalise metric names, tags, and timestamps across different sources, ensuring consistency. It can also enrich the metrics with additional context, like tagging with location data or adding configuration states. Typical enrichments will include annotation with additional labels like environments (production, staging etc), identifying resources against different enterprise groups (eventually based on these labels the monitoring will be limited to a group for their own infrastructure/resources).
2. Data Reduction and Optimization: Another key consideration is to manage volume and storage costs when the ingestion data volumes are very high (especially when monitoring logs). Your platform needs to be able to filter, sample, or reduce log verbosity dynamically based on the current needs and policies, prioritising important data while minimising noise.
3. Data Privacy: Before sending data/metrics to downstream systems or alert management systems, it is important to mask PII (personally identifiable information). This is also done in this insights phase where masking or removal of PII is undertaken.

INSIGHTS

The “Insights” phase in an observability platform is where data turns into actionable intelligence. This phase not only involves the visualisation and real-time analysis of metrics, logs, and events but also encompasses the strategic use of this data to predict, alert, and respond to potential issues before they impact business operations. Tools like Grafana play a central role in this phase, providing powerful visualisation capabilities that help teams understand their operations at a glance. 

Here’s an expanded look into how this phase functions, with a focus on time series data storage, event evaluation, alerting, and notification systems.

Visualisation: Every Observability platform needs an intuitive visualisation tool for monitoring and observing ingested data.They should support a variety of data sources, including most common ones like Prometheus, InfluxDB, Elasticsearch, and others, which are commonly used for storing time series data (this is the ideal data type for observability information).

These platforms must allow users to create dashboards with panels that display data in various formats such as graphs, charts, tables, and more. These dashboards must be highly customizable and configurable to update in real-time, providing a live overview of system performance and health.

Time Series Data Storage: Time series databases (TSDBs) like Prometheus and InfluxDB specialise in handling sequential data points indexed in time order. This is ideal for observability data which is inherently temporal. These databases efficiently store, retrieve, and process time-stamped data, enabling rapid query responses and real-time analysis. They allow users to track changes over time, recognize patterns, and evaluate metrics against historical data to detect anomalies.

Event Evaluation and Threshold Breaching

1. Metric Analysis: Time series data needs to be continuously analysed to monitor trends and sudden changes in operational metrics. If a metric exceeds or falls below predefined thresholds, it can indicate an anomaly or potential issue that needs attention.
2. Anomaly Detection: Advanced algorithms and machine learning models can be applied to predict anomalies by learning from historical data. This helps in proactively identifying issues before they escalate.
3. Alerting Mechanisms
   • Threshold-based Alerts: The platform must provide alerting features where users can set conditions based on metric thresholds. When these conditions are met, an alert is triggered.
   • Complex Alerting Rules: For more nuanced scenarios, the platform must support creating complex alerting rules that can evaluate multiple queries or the results of scripted conditions to determine if an alert should be sent.

Notification Systems

Once an alert is triggered, it’s crucial that the right people are notified promptly so that corrective action can be taken. Here are a few notification mechanisms commonly integrated into observability platforms:

1. Email and SMS: These are the most straightforward channels. Grafana can be configured to send email or SMS notifications directly to the stakeholders or on-call engineers.
2. Webhooks: Many systems support receiving generic HTTP callbacks, known as webhooks. These are versatile and can be used to integrate with custom APIs, trigger additional workflows, or even integrate with other platforms.
3. PagerDuty: A popular incident management solution that provides on-call scheduling, automated escalations, and incident tracking. Grafana can send alerts to PagerDuty, which then handles notifying the appropriate personnel based on the current on-call schedules and escalation policies.
4. Slack and Other Messaging Platforms: Real-time messaging platforms like Slack can be integrated to receive notifications, allowing immediate visibility and team collaboration around incidents.

Orchestration

The core capability of any effective observability platform should centrally hinge on orchestration. Such a platform must not only manage its internal functions but also expertly integrate and synchronise a wide array of external tools, platforms, and services. This orchestration is crucial for creating a seamless workflow that addresses the multifaceted needs of data processing. For instance, an advanced observability platform should effortlessly direct SNMP data to specific monitoring systems, simultaneously dispatch copies of data for archival purposes, and maintain a version for data replay and analysis. This capability ensures that data flows are not merely managed but optimised across the entire ecosystem, enabling comprehensive visibility and actionable insights. The platform’s ability to interconnect diverse operations and technologies transforms raw data into a structured, insightful toolset that powers decision-making and operational efficiency at every level of the organisation.

Engineering with Less Manpower

Building with fewer resources can be streamlined by choosing platforms designed to handle significant portions of the backend logic needed for data management:

Platforms like CRIBL offer powerful capabilities to manage and transform streaming data from multiple sources, simplifying complex transformations and routing without extensive coding.

Consideration for Storage, Archival, and Data Accessibility

Choosing the right storage solutions involves not only capacity planning but also considering data retention policies, access speeds, and compliance with regulatory requirements. Effective strategies might include:

Hybrid Storage Solutions: Using high-performance storage for recent data and more cost-effective solutions for older, less frequently accessed data.

Data Archival Systems: Implementing automated policies for data lifecycle management ensures that data is archived in a way that remains accessible but does not incur high costs.

Conclusion: Leveraging Expertise for Custom Solutions

While there are numerous tools and components available to build an observability platform, integrating them into a cohesive system that addresses specific enterprise needs can be challenging. Invisibl Cloud, with its proven expertise in orchestrating custom observability solutions, can guide enterprises through this complex landscape. By leveraging tailored solutions, enterprises can achieve not only deep system insights but also maintain scalability, flexibility, and cost-efficiency.

This blog is intended to guide enterprises through the decision-making process involved in selecting, designing, and implementing a custom observability platform. By understanding the components, evaluating the right tools, and engaging with experienced partners, businesses can enhance their operational intelligence and maintain robust digital environments.