The observability data fabric is a new paradigm in the cloud technology space. It unifies several aspects of data analysis, including metrics and logs, into a single platform. It enables organizations to gain better control over their IT infrastructure by providing topological visibility. This makes it easier for teams to identify weak points in the system, and proactively address any performance issues or security vulnerabilities.
Topology is the roadmap that depicts the connection and relationships between the components in your IT ecosystem. This can include the layout and connections of network devices, the interactions between different software components, and the flow of data through the system. This map is essential for understanding how the system works, and for troubleshooting and monitoring the system.
However, in today’s world, this map has become increasingly complex. The IT environment is constantly changing, with new components being added, updated, or removed. This can make it difficult to keep track of the relationships between different components and to understand how the system is working. Additionally, the sheer scale of modern IT environments can make it difficult to visualize and comprehend the relationships between all the different components.
Old approaches for understanding topology are no longer sufficient for dealing with the complexity and fast-paced change of modern IT environments. Traditional monitoring and troubleshooting tools, for example, may not be able to keep up with the speed and scale of today’s systems. This is where topology-first workflows and observability data fabric become important. These tools provide a new way to understand the underlying structure and relationships of complex environments and make it possible to monitor and troubleshoot the system in a more efficient and effective way.
In this blog post, we will explore the concept of topology-based observability data fabric in greater detail, examining its benefits and how data teams can leverage it to ensure data quality and drive better decision-making.
What is an Observability Data Fabric?
Topology is the study of the layout and connections of a system. It is often used to understand how different components are connected and how they interact. In network topology, for example, the layout and connections of network devices can give insight into how data flows through the system and where potential bottlenecks or failures may occur. This is important because it allows for better monitoring and troubleshooting of the system.
An observability data fabric, on the other hand, is a tool that sits at the intersection of distributed systems, observability, and data flow management. It is used to understand the underlying structure and relationships of complex environments.
Topology-first workflows are a game changer for observability data fabrics, providing a way to understand the underlying structure and relationships of complex environments in a more efficient and effective way. This leads to better monitoring and troubleshooting of the system, and ultimately to a better understanding of the system as a whole.
The 5 Stages of the Observability Maturity Model
You can measure and enhance your business’s ability to achieve full-stack observability and beyond with the topology-based observability maturity model by LOGIQ. It is composed of the following 5 essential levels/stages:
Level 0- Monitoring:
Monitoring provides you with basic information regarding the health and status of individual components in your infrastructure, as well as warning you if one breaks down. Monitoring essentially gives insights into performance, capacity, availability, events, and alerts.
An agent on a server tracks its usage and the data from the agent is collected and displayed usually as a performance graph over time. If the device stops functioning properly, it sends out an alert to the admin, so that they can repair, update, or replace it until it meets standard operating requirements.
Whilst monitoring ensures that everything is in working order, it can’t resolve to answer why a certain issue occurred, this is where observability comes in.
Level 1- Observability:
Observability is the degree to which an internal system’s states may be deduced from external sources. Metrics, logs, and traces have traditionally been used as the three pillars of observable data.
An example of data observability is the use of distributed tracing, which tracks the flow of requests and responses through a distributed system. This can help organizations understand how different components of their systems are interacting, and identify bottlenecks or other issues that may be impacting performance.
Level 2- Full-stack Observability:
Full-stack observability is real-time monitoring of the full stack supporting applications, computing, storage, services, and networks.
The contextualization of events, logs, metrics, and traces from across the stack of the application and infrastructure enables enterprises to discover the root cause of problems.
For example, topology-based correlation helps to gather a range of data streams, revealing previously hidden interconnections and providing more in-depth insights for root cause analysis.
Level 3- Intelligent Observability:
With intelligent observability or AIOps, advanced data analysis and machine learning techniques provide rapid insights into the performance and behavior of systems and applications. End users detect and resolve issues significantly faster compared to the other stages as the system learns patterns and correlations and surfaces potential bottlenecks and issues.
For example, the algorithm might detect a sudden increase in error messages or a pattern of slow response time. Using this information, AIOps will prioritize critical issues and take action to fix them.
IT Operations, DevOps, and site reliability engineers improve their efficiency by reviewing IT data and observability telemetry with AIOps. Therefore, Intelligent observability helps you to recognize digital service issues in real-time and resolve them before business activities or customers are affected.
Level 4- Federated Observability:
Federated observability enables the democratization of observability. Data is made available for consumers on demand. It ensures business agility at the lowest possible cost. Workflows, consumption models, cost management, and other factors lie at the forefront at this stage. It’s the stage where data reigns supreme and data consumers take the center stage leveraging data on-demand to solve a wide variety of security, operational, and business problems and make critical business decisions.
A topology-based observability data fabric implementation allows enterprises to achieve observability data federation. This is essential to determine solutions to business problems such as improving customer experience and reducing the cost of cloud infrastructure.
5 Use Cases for topology-based Observability Data Fabric
LOGIQ.AI facilitates debugging and data flow visualizations for technical as well as federated use cases, some of them are as follows:
- Unifying, Correlating, and Visualizing: With LOGIQ.AI, you can have full-stack visibility of your IT environment in real-time, through a single pane of glass. It integrates quickly with other data sources, combining siloed telemetry with time-series topology data, so you can see cause and effect across your stack.
- Finding Root Cause: LOGIQ helps you troubleshoot problems in your increasingly complex platforms in record time, reducing Mean Time to Recovery (MTTR). It tracks dependencies and configuration changes over time, providing the context you need to find the root cause of problems.
- Reducing Alert Noise: LOGIQ’s topology-powered Observability helps you save time and money by correlating events. It understands the upstream changes that cause downstream problems, logically grouping related alerts and prioritizing the ones that need your attention.
- Determining Business Impact: Data Fabric observability helps you understand how IT issues affect your customers, services, and lines of business. It shows you the consequences of failures, from dependent components that are impacted to the business services that falter, so you can determine the priority to fix an issue and who needs to be involved.
- Preventing Problems Proactively: LOGIQ combines Topology-Powered Observability and Artificial Intelligence Operations (AIOps) to give you the innovative new capabilities you need to eliminate problems before they impact your users. It uses advanced monitors and AI-powered anomaly detection to spot anomalies in your environment before they become costly problems.
Data Observability pillars with LOGIQ
The LOGIQ.AI platform combines topology, traces, telemetry, logs, and time into a single data fabric to provide unprecedented insight into your IT landscape. This is made possible by a sophisticated database integration that links objects together in terms of their relationships with each other and the timestamps on their telemetries. This allows LOGIQ.AI to capture every millisecond of change from any source, providing complete insight into your entire IT infrastructure.
The topology dimension in LOGIQ.AI’s data fabric provides a map of the relationships between the components in your IT environment. This can include the layout and connections of network devices, the interactions between different software components, and the flow of data through the system. This map is essential for understanding how the system works, and for troubleshooting and monitoring the system.
Traces, telemetry, and logs are key metrics for understanding the performance and behavior of a system. LOGIQ.AI’s data fabric captures traces, telemetry, and logs from every source and correlates them with the topology of the system, providing a detailed picture of how the system is working.
Finally, the time dimension in LOGIQ.AI’s data fabric enables the visualization of your stack both historically and in real-time. This allows you to understand how the system has evolved over time, and how it is currently functioning, providing a comprehensive view of the system.
LOGIQ.AI’s topology-powered observability data fabric is a tool that provides complete insight into your IT landscape by ingesting telemetry data from all of your IT systems. This includes metrics, events, and logs from monitoring, provisioning, deployment, and configuration management tools.
Furthermore, LOGIQ supports hundreds of integrations to popular technologies, making it easy to connect to the tools and systems that you are already using. This combination of telemetry data and integration support makes LOGIQ a versatile and comprehensive observability solution that can help you understand and troubleshoot your IT environment with ease.
In a Glimpse:
- LOGIQ.AI’s topology-based observability data fabric provides complete insight into IT landscapes by ingesting telemetry data from all systems, including metrics, events, and logs.
- The 5 stages of LOGIQ’s Observability Maturity Models include Monitoring, Observability, Full-stack Observability, Intelligent Observation, and Federated Observation.
- With its combination of telemetry data and integration support with popular technologies such as distributed tracing or AIOps (Artificial Intelligence Operations), LOGIQ is a comprehensive observability solution that helps to understand complex environments in an efficient way.
- LOGIQ unifies, correlates, and visualizes the stack in real-time to help find the root cause of problems quickly with reduced MTTR (Mean Time To Recovery).
- AIOps are used for advanced data analysis & machine learning techniques to provide rapid insights into the performance & behavior of systems & applications.
- Data Fabric allows enterprises to achieve federated observability which ensures business agility at the lowest possible cost while providing on-demand access for consumers.