Leveraging Observability Pipelines to Maximize Telemetry Data Value

    Leveraging Observability Pipelines to Maximize Telemetry Data Value

    The cost of managing data is rising, and it is made even more difficult by unforeseen software failures and constantly changing security risks.

    The development of cloud computing and the preference for data-driven decision-making have steadily caused investments in observability to rise over time. Telemetry data is becoming increasingly understood to be essential for the upkeep of a company’s infrastructure and for assisting the business and security teams in making wise decisions. Ventana Research claims that more than half of organizations intend to increase their investment in observability technology through 2026 to hasten the value creation from telemetry data.

    It is not sufficient to increase investment in observability technology. Organizations must deal with the increasing data volume and complexity to realize the full potential of telemetry data. Organizations must establish effective strategies teams will enable them to gain insights, accelerate time to value, and maintain regulatory compliance.

    This process could be difficult and overwhelming! The cost of managing all this data is rising, and it is made even more difficult by unforeseen software failures and constantly changing security risks. This forces companies to select which software components to instrument and monitor. A new method of data management is needed to solve this problem.

    What is telemetry data?         

    A relatively old science, telemetry is exploding with new applications. Consumers are embracing a wide range of uses for the Internet of Things (IoT), which is now widely used and has a wealth of sensors to monitor everything from home security to biometric data. However, businesses stand to gain even more.

    Telemetry data provides insights into machines and provides managers with a live view of performance metrics. Teams can achieve efficiencies that lower risk and downtime while boosting revenue with tools that combine artificial intelligence and machine learning (AI/ML) with the untapped data stored within operations and processes. Telemetry is the process of recording and reporting remote data from equipment using digitally connected sensors.

    According to IDC, the Internet of Things (IoT), made possible by these sensors, is anticipated to generate nearly 80 billion zettabytes (ZB) of data by 2025. There is an enormous amount of unstructured data produced by telemetry sensors.

    Companies organize the data by cleaning, converting, contextualizing, and connecting it to determine what is important and its structure per KPIs.

    Making Prompt Moves to Maximize Telemetry Data

    Businesses can act right to maximize the use of their telemetry data. They must first adopt a visibility-first strategy rather than a cost-first one. They ought to evaluate the software that they have instrumented.

    This will reveal telemetry gaps, so organizations can develop a strategy to instrument all software. They must then develop a plan for handling their telemetry data, paying close attention to how and why it will be stored and examined.

    Observability Pipelines to Manage Limited Resources

    Developers must meet more demands in today’s hectic business environment than ever. The size of engineering teams hasn’t increased in line with their increased responsibilities. Due to this, the number of full-stack engineers in charge of technologies from the front to the back end has increased. Due to a lack of time and resources, developers find instrumenting and monitoring software challenging.

    By using various processors like sampling, throttling, filtering, and parsing, observability pipelines assist in controlling the amount of telemetry data and only forward valuable data to the downstream systems. The remaining information can be discarded or kept on a cheap system like Amazon S3. This lowers costs and frees resources to analyze pertinent data and identify problems.

    Engineers can use observability pipelines to combine data from various tools. Many teams use multiple platforms or free software programs like Jaeger or Prometheus. It is challenging to identify problems and gauge the health of applications due to the fragmented data. Observability pipelines combine all of this data to make it simpler to take action.

    It’s crucial to keep in mind that actual people are working on these technical difficulties. Working long hours to meet software development demands puts engineers at risk of burnout. Pressure is increased by constantly switching between tools and manually moving data.

    Additionally, many platforms, observability tools, and data sources require that data. Adopting open standards can reduce the difficulty of integration. Engineers can change tools without extensive testing and integration. Engineers can concentrate on providing business value through improved control and simple integration.

    Increasing Data Usability

    Teams from different organizations want access to the data, but they have trouble using it well. This inability to use the data causes a challenge: Telemetry data is unstructured, has a variety of formats that make it challenging to use, requires time-consuming data preparation, and may result in compliance violations due to sensitive data in logs.

    Most estimates place the percentage of unstructured data between 80% and 90%, which makes it challenging to store and analyze because it doesn’t follow conventional data models. Before reaching its destination, observability pipelines make sense of unstructured data. Data processors accomplish this by shaping and transforming data to make it more usable. These processors include sophisticated parsers that locate and extract pertinent data, converting the data into usable formats that facilitate manipulation and analysis.

    The benefit of carrying out these operations within the pipeline is the ability to prepare the same data for use in various downstream use cases. For example, one team might need data optimized for visualization and trend analysis, while another might need complete data for threat analysis. Separate data streams are unnecessary because these transformations can be managed from a single control point.

    Observability pipelines can help businesses adhere to data compliance regulations. Before the data reaches a SIEM or audit platform, teams can scrub, mask, or redact PII data based on a specified key structure.

    Developers can increase the value of telemetry through observability pipelines by putting in place a comprehensive and well-thought-out system that efficiently gathers, processes, and uses telemetry data. Here is a detailed breakdown of the procedures:

    Define Specific Goals and Objectives: Before using telemetry data, developers should specify the precise goals and objectives they hope to achieve. This includes enhancing application performance, quickly locating and fixing problems, allocating resources optimally, or learning more about user behavior. Clear objectives will guide the design and implementation of the observability pipeline.

    Instrumentation: Developers should instrument the application and infrastructure components to collect pertinent telemetry data. The main metrics, logs, and traces that reveal information about the system’s performance, behavior, and health should be identified. This requires configuring logging tools, integrating telemetry libraries or frameworks into the application code, and utilizing distributed tracing for end-to-end visibility.

    Centralized Data Collection: A centralized data collection system is important for combining telemetry data from various sources. Developers can use tools like log aggregators, metrics collectors, or distributed tracing platforms to gather and store data in one place. This strategy guarantees simple accessibility and makes efficient analysis possible.

    Data Normalization and Enrichment: To maximize the value of telemetry data, it’s essential to normalize and enrich the collected data. This process entails formatting data consistently, establishing timestamp standards, and including contextual data like tags or metadata. Data normalization makes it simpler to analyze and correlate data among system components.

    Real-time Processing and Analysis: To quickly gain insightful knowledge from telemetry data, observability pipelines should include real-time processing and analysis capabilities. Developers can use event-driven architectures or stream processing frameworks to process incoming data streams, find patterns, perform aggregations, and produce useful metrics and statistics.

    Also Read: 10 Essential APIs for Web Development: Empowering Projects with Seamless Integration

    Visualization and Alerting: Developers should use efficient visualization and alerting mechanisms to make telemetry data easier to consume by displaying important metrics, trends, and anomalies in real-time active monitoring platforms or customized dashboards. Setting up alerts based on predetermined thresholds or anomaly detection algorithms also aids in the early detection and resolution of problems.

    Telemetry value can be increased with the help of observability pipelines if organizations take the time to plan and execute a comprehensive system that incorporates instrumentation, centralized data collection, data normalization, real-time processing, visualization, alerting, intelligent analysis, and continuous improvement.

    Companies will encounter difficulties processing an enormous amount of telemetry data to take meaningful action as they devote more resources to developing observability practices. Observability pipelines give businesses a competitive edge by prioritizing crucial data to help them make smart decisions faster.

    Using a comprehensive approach, developers can get better equipped to gain deep insights into their systems, improve performance, find problems faster, and make data-driven decisions for ongoing improvement.


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