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Observability Implementation Strategy for Digital Service Providers (DSPs)

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Enhancing productivity with end-to-end (E2E) visibility in cloud-native applications

Discover an effective observability implementation strategy that can help DSPs overcome implementation challenges and achieve efficient observability.

As digital service providers (DSPs) transition to multi-layered microservices architecture and cloud-native applications, traditional monitoring tools have presented several limitations. Unified analysis with scattered monitoring tools is difficult to achieve. Challenges in correlating and isolating issues hinder DSPs from meeting SLA/SLO requirements.

To overcome these challenges, DSPs need to go beyond traditional monitoring and make their digital business more observable, making it easier to understand, manage, and troubleshoot. According to Gartner, observability is defined as “the attribute of software and systems that allows them to be ‘seen’ and allows questions about their behavior to be answered.” Implementing efficient end-to-end observability provides immediate value to the DSP ecosystem by gaining crucial insights into the performance of complex cloud-native environments. Having unified visibility across the ecosystem enables powerful analysis by bringing together logs, metrics, events, and traces in a single stack.

Key strategy for an efficient observability implementation

While leading DSPs have begun implementing observability strategies, many face numerous implementation challenges that prevent them from realizing the full benefits. The following observability implementation strategy can help DSPs address these challenges and achieve efficient observability.

Key strategy for DSPs for an efficient Observability implementation
Fig: Key strategy for DSPs for an efficient Observability implementation

  1. Build observability pipeline based on OpenTelemetry

    Challenges in observability pipeline

    Lack of standardization of telemetry data leads to increased complexity in maintaining instrumentation. The usage of different agents to collect logs, traces, and metrics creates issues with data portability and results in a vendor lock-in scenario. Additionally, a tighter coupling of collected data with destinations forces teams to use scattered toolsets with drawbacks.

Challenges in observability pipeline

Key recommendations

  • Construct observability pipeline based on OpenTelemetry standards: Unified data collection using OpenTelemetry standards decouples the data sources from the destinations, making the observability data easily consumable.
  • OpenTelemetry eases instrumentation for DSPs by providing a single, vendor-agnostic instrumentation library per language and supports automatic and manual instrumentation. It establishes a de-facto standard for adding observability to cloud-native applications. Additionally, as OpenTelemetry gains adoption, more frameworks will include out-of-the-box instrumentation.
  • Monitor metrics that truly matter: Start by monitoring key metrics that have direct implications on operations and business. Establish a baseline list of metrics and optimize it based on observability learnings. Focus only on the data sources that provide real value to eliminate any capacity issues.
  • Ensure standard and structured log management practices in the logging guidelines: Clearly define logging guidelines that cover key parameters such as when to log, log name, log format, and log details like correlation ID, flow ID, event ID, and transaction ID. It is essential to log critical data that helps DSPs troubleshoot performance issues, resolve user experience problems, or monitor security-related events. These log levels can also be made configurable to control the verbosity of logs and obtain sufficient information as needed.
  1. Promote observability as a culture across the organization

    Challenges

    In traditional monitoring, visibility is not a consideration during the design or development phase. As a result, the DevOps team is only aware of issues when services fail or are on the verge of failure in predictable ways.

    Key suggestions

    • Promote observability as a culture across the organization: Observability as a culture refers to the degree to which a company values the ability to inspect and understand systems, their workload, and their behavior.
    • Ensure Observability Driven Development (ODD) throughout the software development life cycle:
      • In the design phase, determine what to measure based on quality of service (QoS) and key performance indicators (KPIs) to be met. Also, identify appropriate places to add instrumentation.
      • The development phase requires standardizing the context and consistently including sufficient context in all instrumentation data. It is also important to maintain the right balance of instrumentation to avoid overwhelming analysis.
      • In the build and deployment phase, implement observability as part of the continuous deployment process. Detect unusual behavior at an early stage through automation.
      • Lastly, in the run phase, foster continuous feedback of observability learnings from the operations and development teams for continuous improvement.
  2. Adopt best practices for data management, security, and governance

    Challenges

    Overlogging leads to a situation where log storage capacity is quickly consumed. Lack of retention policies results in rapid exhaustion of storage capacity, leading to increased costs and operational issues. Additionally, inadequate role-based access and GDPR non-compliance often result in severe security breaches and penalties.

    Key recommendations

    • Centralize and correlate all data sources. Don’t analyze in silos: A single pane of view helps connect the dots between captured logs, events, traces, and metrics, providing the complete story of what’s happening at any given time. Logs from disparate sources can be collected, parsed, and stored in a central repository with indexing.
    • Create a flexible data retention policy: Clearly define the duration of retention for various types of data (e.g., regulatory data, machine state data, etc.). Follow the 3-2-1 rule for storage and backup. Ideally, there should be three copies of the data, stored on two different media, with at least one stored off-site or in the cloud. Log storage should work as a cyclic buffer that deletes the oldest data first when the storage limit is reached.
    • Implement security policies for collected data: Role-based access control should be implemented for accessing stored data. Ensure sensitive data is anonymized or encrypted.
    • Use stored data (logs) to identify automation opportunities: Logging should be seen as an enabler for automation, not just troubleshooting. Capture where issues are introduced and what are the sources of those issues to identify automated fixes.
  3. Select the observability platform that fits the organization’s long-term needs

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Observability Platform

Key parameters an observability platform must have:

  • Full-stack monitoring (cloud, business, user, applications, infrastructure, network)
  • Supports OpenTelemetry (e.g., Elastic, NewRelic, etc.)
  • Supports intelligence and AIOps (e.g., Elastic, Dynatrace, AppDynamics, MoogSoft, etc.)
  • Ability to correlate metrics, traces, logs, and events to business outcomes
  • Real-time analysis (aggregation and visualization)
  1. AI-powered intelligence for proactive observability at scale. Adopt an AIOps strategy.

Key parameters an observability platform must have

AIOps is becoming an embedded capability of observability. Gartner predicts that the exclusive use of AIOps and digital experience monitoring tools to monitor applications and infrastructure will rise from 5% in 2018 to 30% in 2023.

DSPs should prioritize the development of an AIOps strategy. An AI-powered observability tool combined with an AIOps strategy for observability at scale can simplify the demands of an increasingly complex ecosystem.

Benefits achieved by a leading DSP in Europe after observability implementation

  • Increased productivity by 40% with better workflows for debugging and performance optimization.
  • 30% improvement in system availability: Significant enhancements in incident detection and resolution time, increasing reliability to deliver on SLAs and SLOs.
  • Improved customer experience: Better compliance with business, IT, and infrastructure metrics. Enabled by gaining valuable insights into the performance of DSP’s complex cloud-native environment.

*The images used in this article are sourced from the original article on IT Chronicles.

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