Best Telemetry Pipelines

Guide to Telemetry Pipelines

Telemetry pipelines are systems designed to collect, process, and transport telemetry data from various sources to a centralized location for analysis and monitoring. Telemetry data typically includes logs, metrics, traces, and events generated by applications, infrastructure, and network components. These pipelines are essential for gaining insights into system performance, reliability, and user behavior, and are especially critical in modern, distributed environments such as cloud-native and microservices architectures.

A typical telemetry pipeline involves several stages: data collection, transformation, and export. Data is first collected from various sources using agents, SDKs, or APIs. It is then transformed or enriched to standardize formats, add metadata, or filter out unnecessary information. Finally, the processed data is exported to observability platforms, data lakes, or monitoring tools for storage and analysis. Common tools and technologies used in telemetry pipelines include OpenTelemetry, Fluentd, Logstash, and various cloud-native services like AWS CloudWatch or Azure Monitor.

Implementing an effective telemetry pipeline requires thoughtful design to ensure scalability, reliability, and minimal performance overhead. It must handle high volumes of data in real time, ensure data integrity, and support secure transmission. Additionally, telemetry pipelines must be flexible enough to evolve with changing application architectures and business needs. As organizations increasingly rely on telemetry for decision-making and automation, robust pipelines become a foundational part of their digital operations strategy.

Telemetry Pipelines Features

• Multi-source Ingestion: Telemetry pipelines are designed to collect data from a wide range of sources across an entire IT ecosystem. These sources include application code, servers, virtual machines, containerized environments like Kubernetes, databases, network devices, cloud infrastructure, and third-party services. This allows teams to consolidate data from disparate systems and gain a unified view of their environments for better observability and faster troubleshooting.
• Support for Standard Protocols and Formats: To ensure interoperability and flexibility, telemetry pipelines support numerous industry-standard protocols and data formats. These include OpenTelemetry, Fluentd, Prometheus, StatsD, Syslog, and others. This compatibility makes it easier to integrate various systems and tools without needing custom collectors or complex adapters, ultimately simplifying the telemetry collection process.
• Data Parsing: Raw telemetry data is often messy, unstructured, or only semi-structured, making it difficult to use effectively. Telemetry pipelines can parse this data into structured formats such as JSON, making it easier to analyze, store, and visualize. This structured approach also facilitates more advanced processing and querying in downstream systems.
• Data Transformation: Once parsed, telemetry data can be transformed in-flight to suit analytical or operational needs. For instance, pipelines can rename or standardize attribute names, convert timestamps, or aggregate numerical values. These transformations help normalize data from diverse sources, enabling consistent processing and correlation across systems.
• Data Enrichment: Telemetry pipelines enhance the value of data by adding contextual information during processing. For example, metadata such as region, hostname, environment type (e.g., dev or production), service tags, or deployment versions can be attached to each data record. This added context is vital for filtering, grouping, and understanding the behavior of systems in specific environments or regions.
• Anomaly Detection and Pre-processing: Advanced telemetry pipelines can detect anomalies, outliers, or predefined patterns in real time. This early detection capability allows teams to identify performance degradation or security threats before they escalate. Pre-processing the data in this way reduces the noise and ensures that only relevant, actionable data is forwarded to monitoring systems.
• Data Sampling: To manage the volume of telemetry data in high-traffic systems, pipelines can apply sampling techniques. This involves selectively retaining only a subset of traces or logs based on defined criteria or probabilities. Sampling reduces storage costs and performance overhead while still preserving critical insights for analysis.
• Rate Limiting: Rate limiting ensures that telemetry data does not overwhelm the processing or storage capacities of downstream systems. Pipelines can throttle data flow, either globally or by source, to maintain system stability and avoid performance bottlenecks, especially during traffic spikes or outages.
• Data Filtering: Telemetry pipelines offer powerful filtering capabilities that let users discard unnecessary or irrelevant data. For instance, verbose debug logs from production environments can be filtered out, reducing noise and conserving resources. Filtering can also help teams comply with data minimization policies.
• Conditional Routing: Data can be routed to different destinations based on rules defined in the pipeline configuration. For example, high-priority error logs might be sent to a real-time alerting platform, while regular access logs are routed to a long-term storage solution. This flexibility allows organizations to optimize their observability stack for both performance and cost.
• Tag-based Routing: Routing decisions can also be made based on metadata or tags attached to telemetry data. For example, telemetry from staging environments might be routed to a separate analytics backend than production data, enabling teams to test changes without polluting production dasards.
• Metric Aggregation: To reduce the volume of telemetry data and improve efficiency, pipelines can aggregate raw metrics over time intervals before exporting them. This is especially useful for high-frequency metrics like CPU or memory usage, where detailed granularity is less important than overall trends.
• Log Batching and Compression: Pipelines can group multiple log records into batches and compress them before sending them to the destination. This reduces the number of transmissions and conserves bandwidth, which is particularly beneficial in distributed environments or when sending data across the internet.
• Multi-Destination Export: Telemetry pipelines can send processed data to multiple backends simultaneously. This allows organizations to feed different systems based on use case—such as exporting metrics to Prometheus for performance monitoring, logs to Elasticsearch for search and analysis, and traces to Jaeger or Zipkin for distributed tracing.
• Format Conversion for Export: To ensure compatibility with destination systems, telemetry pipelines can convert data into the required output formats. This enables seamless integration with a variety of platforms and tools, avoiding the need for additional format converters or custom integrations.
• Retry and Delivery Guarantees: To avoid data loss, telemetry pipelines include mechanisms like retry logic and delivery guarantees. Whether the downstream system is temporarily unavailable or there is a network failure, the pipeline can buffer data and attempt redelivery, adhering to at-least-once or exactly-once delivery semantics as needed.
• Plugin Architecture: Many telemetry tools, such as Fluent Bit or Logstash, use a plugin-based architecture. This design allows users to plug in additional functionality—such as custom parsers, output destinations, or processing filters—without modifying the core software. It also fosters a rich ecosystem of community-developed plugins.
• Custom Script Support: To meet unique business or technical requirements, some telemetry pipelines allow users to write custom scripts or processors in supported languages like Lua or JavaScript. These scripts can perform custom transformations, filtering, or enrichment based on logic specific to an organization’s needs.
• Data Redaction and Masking: Telemetry pipelines help ensure compliance with privacy regulations by redacting or masking sensitive data before export. For example, personally identifiable information (PII), passwords, or API tokens can be automatically removed or obfuscated to prevent unauthorized access or accidental exposure.
• Authentication and Encryption: To secure data in transit and protect telemetry sources and destinations, pipelines support encryption protocols such as TLS and require authentication credentials. This ensures that data cannot be intercepted or tampered with as it moves through the pipeline.
• Self-Monitoring: Telemetry pipelines often expose their own operational metrics, such as throughput, latency, dropped message count, and buffer usage. This self-monitoring capability enables teams to detect issues in the pipeline itself, such as bottlenecks, backpressure, or component failures.
• Health Checks and Alerting: Integrated health checks and alerting capabilities notify operators when parts of the telemetry pipeline are underperforming, misconfigured, or failing altogether. This proactive monitoring ensures that telemetry collection is reliable and uninterrupted.
• Horizontal Scaling: Modern telemetry pipelines are designed to scale horizontally, meaning more processing nodes can be added to handle increasing data volumes. This is essential for enterprise environments with distributed systems and microservices that generate massive telemetry traffic.
• Resilience and Failover: Fault-tolerant designs enable telemetry pipelines to continue functioning even when certain components fail. Mechanisms like message queues, data buffers, and redundant paths ensure that telemetry data is preserved and delivered even under adverse conditions.
• Short-Term Buffering: To handle temporary surges in data or interruptions in connectivity, pipelines often include in-memory or disk-based buffers. These buffers store telemetry data temporarily, preventing data loss while the system catches up or recovers.
• Retention Policy Enforcement: Pipelines can apply policies that determine how long data should be retained, particularly in local queues or temporary storage. These policies help manage resource usage and comply with organizational data governance strategies.
• Structured Output for ML Pipelines: Telemetry pipelines that normalize and enrich data enable downstream integration with machine learning pipelines. Structured telemetry can be fed into ML models for predictive analytics, such as identifying performance anomalies or forecasting resource usage.
• Real-Time Stream Processing Compatibility: Pipelines can integrate with stream processing platforms like Apache Kafka, Apache Flink, or Spark . This allows for real-time analytics, filtering, and even ML inference at the edge, offering faster insights and more dynamic automation.

What Types of Telemetry Pipelines Are There?

• Logs Pipeline: A logs pipeline is responsible for collecting textual or semi-structured data generated by applications, operating systems, and infrastructure components. These logs often contain valuable information for troubleshooting issues, performing audits, and gaining insight into application behavior. A logs pipeline typically includes stages such as parsing, formatting, enriching, and routing the log data to appropriate storage or analysis destinations. This type of pipeline must handle high volumes of data and support various formats like JSON, XML, or plain text.
• Metrics Pipeline: A metrics pipeline gathers quantitative data that represents the health and performance of systems. This can include CPU usage, memory consumption, request latency, error rates, and more. Metrics are often aggregated over defined time intervals, making them ideal for quick visualization and real-time alerting. Because metrics are numerical and time-series-based, they can be stored efficiently and are well-suited for triggering thresholds and observing trends over time.
• Traces Pipeline: Traces pipelines focus on collecting and analyzing distributed traces that follow individual requests as they pass through various services in a system. Each trace consists of multiple spans, where each span represents a segment of the request lifecycle. These pipelines are critical for diagnosing latency issues, understanding inter-service dependencies, and providing deep visibility into microservice interactions. They require a data structure that can represent parent-child relationships and timing information to reconstruct the full request path.
• Push-Based Pipeline: In a push-based telemetry pipeline, data is sent automatically from the client (such as an agent, application, or device) to the telemetry backend. This model is efficient for near real-time data collection and enables systems to transmit information as soon as it is generated. Push-based pipelines often include features such as buffering and retry logic to handle intermittent connectivity or system overloads.
• Pull-Based Pipeline: A pull-based pipeline works by having the central system periodically request or fetch telemetry data from clients. This approach provides greater control over data collection intervals and can be more predictable in terms of data volume. While it may introduce some latency, it's beneficial in environments where security, scheduling, or resource limitations require centralized control of data collection activities.
• Stream Processing Pipeline: Stream processing pipelines operate on data in motion, processing each event or data point as it arrives. These pipelines are essential for real-time monitoring, quick decision-making, and immediate alerting. They are designed to handle high-throughput, low-latency workloads and can perform transformations, enrichments, and filtering on-the-fly without waiting for a batch to complete.
• Batch Processing Pipeline: Batch processing pipelines collect data over a defined period and then process it as a group. This method is optimal for workloads that do not require immediate insight and can afford to wait for scheduled analysis runs. Batch pipelines are resource-efficient and well-suited for reporting, data warehousing, historical analysis, and machine learning preprocessing tasks.
• Agent-Based Pipeline: In this setup, software agents are installed on hosts or within applications to collect and forward telemetry data. These agents can perform preprocessing tasks such as filtering, aggregation, and buffering before sending the data to a central destination. Agent-based pipelines are flexible and provide granular control but require installation and maintenance on each monitored system.
• Sidecar-Based Pipeline: Common in containerized environments, sidecar pipelines involve running a companion process alongside the primary application container. The sidecar is responsible for telemetry tasks like collecting metrics, logging, and tracing. This pattern promotes modularity and isolation, allowing telemetry concerns to be handled independently of the application’s core logic.
• DaemonSet or Node-Level Pipeline: This pipeline type deploys a telemetry component on each node of a system rather than within each application or container. It collects data from all services on that node, reducing duplication of telemetry agents and conserving resources. This approach is common in orchestrated environments like Kubernetes and offers a balanced solution between granularity and efficiency.
• Monitoring Pipeline: A monitoring pipeline focuses on providing visibility into system health and performance in real time. It is optimized for fast data delivery and low-latency alerting. This type of pipeline emphasizes stability, uptime, and the immediate detection of failures or anomalies through real-time dasards and alerts.
• Analytics Pipeline: Analytics pipelines are designed for deep, long-term analysis of telemetry data. They often support complex queries, trend analysis, and integration with business intelligence tools. These pipelines may not operate in real time but are essential for capacity planning, optimization strategies, and strategic decision-making.
• Security Telemetry Pipeline: This pipeline is tailored for the collection and analysis of data related to security events and threats. It gathers logs and events from sources like firewalls, intrusion detection systems, and authentication services. Enrichment with context such as user identity, IP address, and location is common. Security pipelines support compliance, threat hunting, and forensic investigations.
• Managed Pipeline: Managed pipelines are provided as a service and abstract away the complexities of deployment, scaling, and maintenance. They are easy to set up and integrate with, making them ideal for teams that prioritize ease of use and operational simplicity. However, they may have limitations in terms of customization and fine-grained control.
• Custom or Self-Hosted Pipeline: These pipelines are built and maintained in-house, offering full control over telemetry processing, data flow, and storage. They can be tailored to meet specific organizational needs or compliance requirements. While they offer high flexibility, they also demand significant resources for configuration, scaling, and ongoing support.
• Centralized Pipeline: A centralized telemetry pipeline consolidates all data into a single backend system for processing, storage, and analysis. This simplifies management and correlation of data across sources but can become a bottleneck or a single point of failure if not properly scaled and secured.
• Distributed Pipeline: In a distributed pipeline, telemetry data is processed and sometimes stored closer to the source, either by region or by service. This reduces latency, improves fault tolerance, and scales better for global or edge-based systems. Distributed pipelines are more complex to manage but offer superior resilience and scalability.

Benefits of Telemetry Pipelines

• Centralized Data Collection: Telemetry pipelines allow for the centralized gathering of logs, metrics, and traces from multiple sources across an organization’s infrastructure. This eliminates data silos, ensuring that all relevant information is collected in a single place. By centralizing data, teams can streamline troubleshooting and gain a holistic view of system behavior across applications, services, and environments.
• Scalability and Flexibility: Modern telemetry pipelines are built to scale with the needs of growing systems. Whether you're dealing with a handful of microservices or thousands, pipelines can be configured to handle increasing volumes of telemetry data without significant performance degradation. Flexible configurations also allow for custom processing rules, format transformations, and routing logic tailored to specific business and technical needs.
• Real-Time Processing and Monitoring: Telemetry pipelines enable real-time or near-real-time processing of data, allowing teams to detect and respond to incidents as they happen. This is particularly valuable for alerting, anomaly detection, and auto-remediation workflows, where timely information can prevent minor issues from escalating into major outages.
• Data Enrichment and Normalization: Pipelines often include functionality for enriching telemetry data with additional context—such as host metadata, application tags, or geolocation info. They also standardize data from different sources into a common schema, making it easier to analyze and correlate information across heterogeneous systems. This normalization simplifies querying and improves the effectiveness of dasards and visualizations.
• Improved Observability and Insight: With telemetry pipelines feeding data into observability platforms, organizations can achieve deeper insights into system health, performance trends, and user behavior. This enhanced observability allows for proactive maintenance, capacity planning, and continuous improvement of applications and infrastructure.
• Reduced Operational Overhead: Automating the flow of telemetry data reduces the need for manual data handling, custom scripts, or ad hoc solutions for log forwarding and metrics collection. This frees up engineering resources and minimizes the risk of human error in telemetry-related tasks, allowing teams to focus on higher-value activities.
• Cost Optimization: By enabling fine-grained control over what data is collected, stored, or forwarded to downstream systems, telemetry pipelines help manage and optimize costs. For example, they can filter out noisy or low-value data before it hits expensive logging or analytics platforms. Compression and batching capabilities also reduce bandwidth and storage usage.
• Enhanced Security and Compliance: Telemetry pipelines can enforce security and compliance policies during data transit. This may include masking sensitive information (like PII), encrypting data streams, or ensuring logs are retained in accordance with regulatory requirements. These capabilities help ensure that observability practices don't compromise organizational or legal standards.
• Custom Routing and Multi-Destination Support: A powerful advantage of telemetry pipelines is the ability to route different types of data to different destinations. For example, logs might be sent to a log analysis tool, while metrics go to a time-series database, and traces to a dedicated tracing system. This granularity supports specialized tools while maintaining coherence in overall observability strategies.
• Resilience and Fault Tolerance: Many telemetry pipelines are designed to be fault-tolerant, with built-in buffering, retries, and failover mechanisms. This ensures that data is not lost even if parts of the pipeline or destination services are temporarily unavailable. This reliability is crucial for post-incident forensics and long-term trend analysis.
• Developer and Operations Empowerment: By making telemetry data more accessible and actionable, pipelines empower both developers and operations teams. Developers can use telemetry to debug and optimize code, while ops teams rely on it for system monitoring, incident response, and infrastructure tuning. The shared visibility fosters collaboration and accelerates root cause analysis.
• Support for Modern DevOps and SRE Practices: Telemetry pipelines are integral to modern DevOps and Site Reliability Engineering (SRE) practices. They support CI/CD pipelines by providing performance baselines and regression detection, contribute to service-level objective (SLO) tracking, and underpin automated health checks and chaos engineering experiments.
• Historical Analysis and Trend Forecasting: By archiving telemetry data, pipelines enable long-term analysis that supports business intelligence, trend forecasting, and strategic decision-making. This retrospective view helps organizations understand system evolution, plan for scaling, and identify persistent issues that require architectural changes.
• Tool Agnosticism and Interoperability: Well-designed telemetry pipelines are vendor-agnostic, allowing integration with a wide variety of source and destination systems. This future-proofs observability strategies by making it easier to adopt new tools or switch providers without having to redesign the data ingestion process.

Who Uses Telemetry Pipelines?

• Site Reliability Engineers (SREs): SREs are responsible for ensuring system reliability, uptime, and performance. They rely heavily on telemetry data to monitor system health, identify and resolve outages, track service-level indicators (SLIs), and maintain service-level objectives (SLOs). Real-time metrics, logs, and traces help them detect issues before they become incidents.
• DevOps Engineers: DevOps engineers automate and streamline the software development and deployment lifecycle. They use telemetry to monitor CI/CD pipelines, observe the behavior of applications in different environments, and ensure that new deployments don’t degrade performance or availability. This includes gathering data from containers, orchestrators like Kubernetes, and infrastructure tools.
• Developers / Software Engineers: These are the engineers who write and maintain application code. They use telemetry to understand how their code behaves in production, debug issues, optimize performance, and assess feature usage. Traces and logs are especially valuable during debugging and root cause analysis.
• Data Engineers: Data engineers manage data pipelines and infrastructure, often handling large volumes of structured and unstructured data. They use telemetry to monitor data pipeline performance, detect bottlenecks or data loss, and ensure timely delivery of data. Metrics on throughput, error rates, and latency are particularly important.
• Security Engineers / Analysts: Security professionals are responsible for monitoring and protecting systems from threats and vulnerabilities. They rely on telemetry from systems, applications, and networks to detect suspicious behavior, investigate security incidents, and audit access patterns. Logs and events are essential for forensic analysis and compliance.
• IT Operations / Infrastructure Engineers: These users manage the underlying infrastructure that supports applications and services, including servers, storage, and networks. They track resource utilization, capacity planning, and hardware failures. Metrics from operating systems, hypervisors, and network devices help ensure that infrastructure meets demand and operates within safe thresholds.
• Product Managers: Product managers define and prioritize features and functionality in a product roadmap. They use user behavior telemetry (often via analytics platforms) to evaluate how features are used, identify popular or underused functionality, and make data-driven product decisions. They also monitor KPIs like user retention, engagement, and conversion rates.
• Business Analysts: These users analyze business data to drive strategic decisions and performance improvements. They often rely on higher-level aggregations of telemetry data to derive insights about customer behavior, operational efficiency, or system performance trends that impact business outcomes.
• Quality Assurance (QA) Engineers: QA engineers ensure that software meets quality standards before and after release. They monitor automated test results, track test coverage and failure rates, and use post-deployment telemetry to detect regressions or anomalies that weren’t caught during testing.
• Customer Support / Technical Support Engineers: These roles assist customers in troubleshooting and resolving issues with software or services. They use telemetry to gather diagnostic data, investigate reported problems, and correlate issues with logs, events, or user sessions. This helps them provide faster, more accurate support.
• Network Engineers: Network engineers design, implement, and maintain networking infrastructure. They analyze telemetry data from switches, routers, and firewalls to monitor bandwidth usage, detect anomalies, identify packet loss, and troubleshoot latency issues across the network.
• Compliance Officers / Auditors: These professionals ensure that systems comply with regulatory and internal governance requirements. They use logs and audit trails to verify system activity, enforce data access policies, and maintain historical records for compliance with standards such as GDPR, HIPAA, or SOC 2.
• Cloud Architects / Cloud Engineers: These engineers design and optimize cloud-based architectures and services. They use telemetry to track the health, performance, and cost of cloud resources. Observability data helps with scaling, cost optimization, and architectural decisions across multi-cloud or hybrid environments.
• Machine Learning Engineers / Data Scientists: These users build and maintain models and data-driven applications. They monitor model performance (e.g., accuracy, drift, latency) and data pipelines. Telemetry helps ensure that models in production continue to perform as expected, and it aids in troubleshooting data quality issues.
• Executives / C-Level Stakeholders (e.g., CTO, CIO): These are high-level decision-makers responsible for strategic direction and resource allocation. They often consume dasards or summarized reports derived from telemetry pipelines to assess the health and progress of technical initiatives, business impact, and risk levels. Their interest is typically in trends, anomalies, and key performance indicators.

How Much Do Telemetry Pipelines Cost?

The cost of telemetry pipelines can vary widely depending on several factors, including the scale of data collection, the complexity of processing, and the level of reliability and performance required. For small-scale or basic telemetry setups, expenses may remain relatively low, especially if utilizing open source tools or in-house infrastructure. However, as the volume of data increases and the need for real-time analysis, high availability, and scalability grows, so do the associated costs. These can include cloud storage fees, data transfer charges, compute resources, and maintenance of the system.

Additionally, operational costs play a significant role in the total expense of running telemetry pipelines. Organizations often need to invest in engineering expertise for pipeline development, integration, and ongoing monitoring. Security, compliance, and redundancy features may further add to the costs, especially in regulated industries. Ultimately, while a basic telemetry pipeline might be implemented on a modest budget, a robust, enterprise-grade system could represent a substantial financial commitment, depending on the requirements and scale of operations.

What Software Can Integrate With Telemetry Pipelines?

A wide range of software can integrate with telemetry pipelines, depending on the purpose and architecture of the system. Monitoring and observability tools such as Prometheus, Grafana, Datadog, and New Relic commonly integrate with telemetry pipelines to collect, visualize, and analyze metrics, logs, and traces. Application performance monitoring (APM) platforms also rely on telemetry to track performance bottlenecks and detect anomalies in real time. In addition, logging frameworks like Fluentd, Logstash, and OpenTelemetry Collectors often serve as intermediaries, ingesting telemetry data from various sources and forwarding it to storage or analysis backends.

Cloud platforms, including AWS, Azure, and Google Cloud, offer native telemetry integration, enabling seamless collection and processing of system and application-level telemetry. DevOps and CI/CD tools such as Jenkins, GitLab, and CircleCI may also hook into telemetry pipelines to provide visibility into deployment health, build performance, and operational metrics. Security information and event management (SIEM) systems, like Splunk and Elastic Security, leverage telemetry data to detect threats and perform forensic analysis.

Custom applications can integrate through APIs, SDKs, or agents provided by telemetry frameworks. These applications might emit their own logs, metrics, or traces into the pipeline to support debugging, performance tuning, and business analytics. The ability to integrate effectively often depends on adherence to open standards, such as those defined by OpenTelemetry, which facilitate consistent data formats and interoperability between tools.

Telemetry Pipelines Trends

• Shift from Monolithic to Distributed Architectures: Modern software systems are moving away from monolithic architectures and embracing distributed models such as microservices and serverless. This transformation increases system complexity and introduces challenges in maintaining observability across multiple services, containers, and cloud environments. As a result, telemetry pipelines have become more sophisticated, needing to aggregate and correlate data from various components to provide a coherent view of system health, performance, and behavior.
• Adoption of OpenTelemetry: OpenTelemetry is emerging as the universal standard for collecting telemetry data, encompassing traces, metrics, and logs in one unified framework. Its rise is driven by the need for consistent instrumentation and seamless integration with diverse observability platforms. By providing vendor-agnostic APIs and SDKs, OpenTelemetry allows organizations to avoid lock-in and simplifies the process of gathering telemetry data across different layers of the tech stack, making observability easier to implement and maintain.
• Emphasis on Real-Time Processing: Organizations increasingly demand real-time insights to accelerate incident response and ensure optimal performance. Telemetry pipelines are evolving to support architectures, leveraging tools such as Apache Kafka, Apache Flink, and Amazon Kinesis. These technologies enable data to be ingested, transformed, and analyzed in-flight, allowing for immediate detection of anomalies, alerting, and action. As digital systems operate with greater velocity, real-time telemetry is no longer a luxury—it’s a necessity.
• Edge Telemetry and Decentralized Processing: With the rise of edge computing and IoT devices, telemetry is no longer limited to centralized data centers or cloud environments. Data needs to be collected, processed, and acted upon at the edge to minimize latency, reduce bandwidth usage, and ensure reliability in disconnected environments. Telemetry pipelines are adapting to support lightweight agents and edge-native processing frameworks, enabling local analytics while syncing with central observability platforms when needed.
• Increasing Use of AI/ML for Anomaly Detection: As telemetry pipelines generate massive volumes of high-dimensional data, traditional rule-based alerting becomes insufficient. To address this, AI and machine learning models are being integrated into observability systems to automatically detect patterns, spot anomalies, and predict system failures. These models rely on enriched, structured telemetry data and require pipelines that support preprocessing, feature extraction, and real-time inference. The fusion of telemetry and machine intelligence is enabling more proactive and intelligent operations.
• Explosion in Data Volume and Cardinality: The granularity of modern telemetry data is skyrocketing, with every microservice, container, pod, and API endpoint generating its own logs, metrics, and traces. This results in extremely high data volumes and cardinality, which can overwhelm traditional storage and querying systems. Telemetry pipelines must be optimized to manage this scale efficiently, leading to advancements in time-series databases, downsampling techniques, and more intelligent data indexing strategies.
• Cost Optimization and Data Sampling: With the exponential growth in telemetry data, organizations are seeking ways to control observability costs without compromising insight. Telemetry pipelines are incorporating dynamic sampling, aggregation, and data retention policies to reduce storage and compute overhead. Techniques like head-based and tail-based sampling for tracing ensure that the most relevant data is preserved. Additionally, cost-aware observability strategies are being built into telemetry pipelines to balance fidelity and affordability.
• Unified Observability Platforms: There's a growing movement toward integrating logs, metrics, traces, and alerting into centralized observability platforms. Instead of juggling multiple tools, teams are adopting all-in-one solutions like Datadog, New Relic, Grafana Cloud, and Splunk, which provide a unified interface for visualizing and correlating telemetry data. This integration streamlines root cause analysis, shortens mean time to resolution (MTTR), and reduces operational overhead by simplifying the observability stack.
• Growing Role of Telemetry in DevOps and SRE: Telemetry is at the heart of modern DevOps and Site Reliability Engineering (SRE) practices. Teams rely on telemetry data to define and measure Service Level Indicators (SLIs) and Service Level Objectives (SLOs), which inform operational goals and reliability targets. Telemetry pipelines support real-time feedback loops in CI/CD workflows, enabling practices like automated rollbacks, canary deployments, and progressive delivery. This tight integration of observability with engineering workflows improves reliability and developer confidence.
• Rise of Infrastructure as Code (IaC) for Observability: Observability is increasingly being defined as code, just like infrastructure and application deployment. Telemetry pipelines and their configurations are being codified using tools like Terraform, Helm, and Ansible. This shift toward Infrastructure as Code (IaC) ensures repeatability, version control, and consistency across environments. It also enables telemetry configurations to be tested, reviewed, and deployed alongside application code, fostering better collaboration between development and operations teams.
• Enhanced Security and Data Governance: Telemetry data often contains sensitive information, such as user identifiers, IP addresses, and payload content. As privacy regulations like GDPR and HIPAA tighten, telemetry pipelines are evolving to include built-in capabilities for data masking, redaction, encryption, and role-based access controls. Ensuring the secure collection, transmission, and storage of telemetry data is no longer optional—it’s a critical part of compliance and trust in digital systems.
• Multi-Cloud and Hybrid Cloud Support: Enterprises are increasingly operating in multi-cloud and hybrid cloud environments, making telemetry collection more complex. Telemetry pipelines must be able to collect and unify data across AWS, Azure, GCP, and on-prem systems. This requires normalization of telemetry formats, resilient data transport mechanisms, and cross-cloud correlation capabilities. The ability to observe and manage systems seamlessly across varied infrastructures is now essential for enterprise-grade observability.
• Use of eBPF for Kernel-Level Observability: eBPF (Extended Berkeley Packet Filter) is gaining traction as a powerful technology for kernel-level observability with minimal overhead. It allows for the safe and efficient collection of system telemetry without modifying application code. Tools like Cilium and Pixie use eBPF to capture granular performance and security data in real time. Telemetry pipelines are being extended to include eBPF-based sources, offering deeper visibility into system behavior.
• Vendor-Neutral and Open Source Tools on the Rise: Organizations are increasingly embracing vendor-neutral and open source telemetry tools to avoid lock-in, reduce costs, and maintain control. Projects like Prometheus, Grafana, Loki, Tempo, Fluent Bit, and Jaeger are widely adopted due to their flexibility, community support, and integration capabilities. These tools enable customized, extensible telemetry pipelines that can be tailored to specific needs and scaled independently.
• Observability for Business Metrics: Beyond system performance, telemetry is being used to monitor business-centric metrics such as user engagement, transaction rates, and revenue impact. By integrating telemetry with analytics tools and product dasards, organizations can gain visibility into how system behavior affects customer experience and business outcomes. This fusion of observability and business intelligence empowers teams to make data-driven decisions that go beyond uptime and latency.

How To Select the Right Telemetry Pipeline

Selecting the right telemetry pipelines involves understanding the specific needs of your systems and applications, as well as the goals of your observability strategy. Start by identifying the types of data you need to collect, such as logs, metrics, traces, or events. Each data type serves a different purpose, so it’s important to choose pipelines that can handle the volume, velocity, and variety of that data effectively.

Consider the tools and technologies already in your environment. If you’re using cloud-native services, it might make sense to choose pipelines that integrate easily with your cloud provider’s ecosystem. Similarly, if you rely heavily on open source tools, look for solutions that support open standards like OpenTelemetry to ensure flexibility and avoid vendor lock-in.

Scalability and reliability are also critical factors. The pipeline should be able to scale with your infrastructure and handle spikes in data without losing performance. It should also support data buffering, retries, and backpressure to minimize the risk of data loss during outages or congestion.

Think about how you plan to process and analyze the data. Some pipelines offer built-in transformation capabilities, allowing you to enrich, filter, or redact data before it reaches the backend systems. This can help reduce storage costs and improve the quality of insights.

Security and compliance requirements should not be overlooked. Ensure the pipeline can encrypt data in transit and at rest, and that it aligns with your organization’s data governance policies.

Finally, evaluate the cost and complexity of managing the pipeline. Managed services may offer convenience, but they can be more expensive. On the other hand, self-hosted options provide greater control but require more maintenance. The right choice depends on your team’s expertise, budget, and long-term strategy.

On this page you will find available tools to compare telemetry pipelines prices, features, integrations and more for you to choose the best software.