Implementing interlinking in blogs

.gitignore

@@ -19,3 +19,7 @@ public/**/feed.xml
 app/tag-data.json
 # Sentry Config File
 .env.sentry-build-plugin
+
+# Interlinking data files (large, not for VCS)
+interlinking.csv.bak
+scripts/data/interlinking.csv

data/blog/6-silent-traps-inside-cloudWatch-that-can-hurt-your-observability.mdx

@@ -41,7 +41,7 @@ These limits mean large-scale dashboards or high-resolution queries can quickly
 In fact, the CloudWatch API will return errors like *Too many datapoints requested* if 
 you overshoot these bounds. 
 By contrast, open-source time-series databases [Prometheus, ClickHouse] that back platforms 
-like Grafana and SigNoz are designed for *high throughput* and 
+like Grafana and [SigNoz](https://signoz.io/docs/introduction/) are designed for *high throughput* and 
 can be *scaled horizontally without fixed query TPS* [Transaction per second] limits. 
 
 
@@ -160,12 +160,12 @@ you must manually open the Logs Insights console and construct a query.
 In contrast, platforms like SigNoz unify the three pillars together. 
 SigNoz provides a single pane to view metrics, traces, and logs together. 
 You can click on a trace span and see the logs for that operation immediately, or jump from an 
-error log to the trace and related metrics.
+[error log](https://signoz.io/guides/error-log/) to the trace and related metrics.
 
 ## High-Volume Trace Performance
 
 Let’s explore how trace performance holds up under high volumes of traces across CloudWatch 
-and modern observability platforms. AWS X-Ray, the distributed tracing tool integrated 
+and modern observability platforms. AWS X-Ray, the distributed [tracing tool](https://signoz.io/blog/distributed-tracing-tools/) integrated 
 with CloudWatch, is AWS’s native solution for this. 
 But it comes with some well-known limitations, let’s take a closer look.
 
@@ -243,5 +243,5 @@ Here’s a quick comparison chart for you.
 
 CloudWatch works for many use cases, especially if you're deep into AWS. But as your systems scale 
 and span across environments, you need something that handles all signals well and brings 
-everything together in one place. That’s when it’s time to rethink your observability stack 
-and explore options that truly fit your needs.
+everything together in one place. That’s when it’s time to rethink your [observability stack](https://signoz.io/guides/observability-stack/) 
+and explore options that truly fit your needs.

data/blog/7-takeaways-prometheus-conference-2019.mdx

@@ -167,7 +167,7 @@ Below is a snapshot of how Gitlab currently does its capacity planning. Any reso
 
 **Prometheus and Jaeger work well together**
 
-Gautham from GrafanaLabs gave a good talk on. I couldn't get into the details of it, but the broad takeaway from the talk is that Prometheus and Jaegar work quite well, and should be explored in more detail. Though not many people currently use Jaegar or distributed tracing, I think this will soon become very important.
+Gautham from GrafanaLabs gave a good talk on. I couldn't get into the details of it, but the broad takeaway from the talk is that Prometheus and Jaegar work quite well, and should be explored in more detail. Though not many people currently use Jaegar or [distributed tracing](https://signoz.io/distributed-tracing/), I think this will soon become very important.
 
 Overall, I think it was a great conference with lots of interesting discussions around Prometheus. I definitely want to attend this conference in person, next time around.
 

data/blog/alert-fatigue.mdx

@@ -270,7 +270,7 @@ To address alert fatigue effectively, you need to quantify and track it:
 
 ## Leveraging SigNoz for Effective Alert Management
 
-SigNoz is a powerful observability platform designed to provide comprehensive insights into the performance and health of your applications and infrastructure. It combines metrics, logs, and traces into a unified view, enabling organizations to monitor, troubleshoot, and optimize their systems more effectively. SigNoz reduces alert noise and fatigue by providing a sophisticated alerting system that delivers real-time notifications for system anomalies. It allows the creation of precise Alert Rules using Query Builder, PromQL, or Clickhouse Queries, ensuring relevant and actionable alerts. This approach minimizes false positives and reduces cognitive overload, making alert handling more efficient and manageable.
+[SigNoz](https://signoz.io/docs/introduction/) is a powerful observability platform designed to provide comprehensive insights into the performance and health of your applications and infrastructure. It combines metrics, logs, and traces into a unified view, enabling organizations to monitor, troubleshoot, and optimize their systems more effectively. SigNoz reduces alert noise and fatigue by providing a sophisticated alerting system that delivers real-time notifications for system anomalies. It allows the creation of precise Alert Rules using [Query Builder](https://signoz.io/blog/query-builder-v5/), PromQL, or Clickhouse Queries, ensuring relevant and actionable alerts. This approach minimizes false positives and reduces cognitive overload, making alert handling more efficient and manageable.
 
 <GetStartedSigNoz />
 
@@ -289,7 +289,7 @@ Addressing alert fatigue requires more than just technological solutions; it dem
 - Causes include excessive alerts, poor prioritization, and human cognitive limitations.
 - Prevention strategies involve intelligent systems, training, and cultural shifts.
 - Automation and AI play crucial roles in modern alert management.
-- Continuous monitoring and improvement are essential to combat alert fatigue effectively.
+- [Continuous monitoring](https://signoz.io/comparisons/continuous-monitoring-tools/) and improvement are essential to combat alert fatigue effectively.
 
 ## FAQs
 

data/blog/angular-graphql.mdx

@@ -576,7 +576,7 @@ Likewise, Angular is also a widely adopted front-end web framework. In the 2021
 
 Once you build your application and deploy it to production, monitoring it for performance issues becomes critical. Mostly, in today’s digital ecosystem, applications have distributed architecture with lots of components. It gets difficult for engineering teams to monitor their app’s performance across different components.
 
-A full-stack APM solution like [SigNoz](https://signoz.io/) can help you monitor your Angular applications for performance and troubleshooting. It uses OpenTelemetry to [instrument application](https://signoz.io/docs/instrumentation/) code to generate monitoring data. SigNoz is open-source, so you can try it out directly from its GitHub repo:
+A full-stack APM solution like [SigNoz](https://signoz.io/) can help you monitor your Angular applications for performance and troubleshooting. It uses [OpenTelemetry](https://signoz.io/blog/what-is-opentelemetry/) to [instrument application](https://signoz.io/docs/instrumentation/) code to generate monitoring data. SigNoz is open-source, so you can try it out directly from its GitHub repo:
 
 [![SigNoz GitHub repo](/img/blog/common/signoz_github.webp)](https://github.com/SigNoz/signoz)
 

data/blog/api-monitoring-complete-guide.mdx

@@ -173,7 +173,7 @@ Aligning API metrics and business KPIs is one of the principal ways to make data
 
 ## Top API Monitoring Tools
 
-Here’s a list of 5 API monitoring tools that you can use:
+Here’s a list of 5 [API monitoring tools](https://signoz.io/blog/api-monitoring-tools/#key-api-metrics-to-monitor) that you can use:
 
 ### Signoz
 
@@ -211,7 +211,7 @@ Graphite’s UI may not be great, but it provides integration with Grafana to bu
 ## What should a good tool offer?
 
 - **Alerting:**
-    Ability to alert when the API check fails to minimize alert fatigue and reduce false positives. Support for multiple alert strategies based on run count, time range, etc.
+    Ability to alert when the API check fails to minimize [alert fatigue](https://signoz.io/blog/alert-fatigue/) and reduce false positives. Support for multiple alert strategies based on run count, time range, etc.
 
 - **Ability to analyze response data:**
     For effective API monitoring, it's essential to extend alert capabilities beyond simple connectivity or HTTP errors to include customizable criteria based on response headers and body content. This entails the ability to identify specific header names/values and parse standard formats like JSON to verify the correctness of field values against expected results. Such precision in monitoring allows for targeted validation of both API availability and data integrity, catering to the nuanced needs of a technical audience.

data/blog/api-monitoring-tools.mdx

@@ -42,7 +42,7 @@ Here’s a curated list of API monitoring tools — a mix of open-source and com
 
 ### 🔍 Full-Stack Observability & APM
 
-These tools go beyond APIs — they offer logs, traces, and infrastructure monitoring.
+These tools go beyond APIs — they offer logs, traces, and [infrastructure monitoring](https://signoz.io/guides/infrastructure-monitoring/).
 
 1. [**Datadog**](#datadog) – Cloud-native observability with 500+ integrations. Strong dashboards, alerts, and tracing support.
 2. [**New Relic**](#new-relic) – End-to-end visibility with APM, browser monitoring, and ML-powered insights.
@@ -74,14 +74,14 @@ For teams heavily invested in cloud-native stacks.
 
 ## SigNoz
 
-Open-source alternative to Datadog built for developers. It provides unified observability with logs, metrics, and traces, and natively supports OpenTelemetry, making it a great choice for API monitoring.
+Open-source [alternative to Datadog](https://signoz.io/blog/datadog-alternatives/) built for developers. It provides unified observability with logs, metrics, and traces, and natively supports OpenTelemetry, making it a great choice for API monitoring.
 
 <Figure src="/img/blog/2025/04/api-monitoring-tools-image.webp" alt="API Monitoring using SigNoz" caption="API Monitoring using SigNoz" />
 
 ### Pros
 
 - Unified observability (logs + metrics + traces), ideal for full-stack API monitoring
-- OpenTelemetry-native — automatic instrumentation for APIs
+- [OpenTelemetry](https://signoz.io/blog/what-is-opentelemetry/)-native — automatic instrumentation for APIs
 - Self-hostable, giving you control over your API telemetry
 - Real-time monitoring and alerting of API performance metrics like latency, request rates, and error rates
 
@@ -111,7 +111,7 @@ An open-source monitoring system designed for collecting and querying time-serie
 
 ### Pros
 
-- Great for collecting API metrics like request rates, latencies, and error rates
+- Great for collecting [API metrics](https://signoz.io/blog/api-monitoring-complete-guide/) like request rates, latencies, and error rates
 - Excellent for time-series data, ideal for monitoring API performance over time
 - Works well with exporters and custom metrics, so you can easily integrate API monitoring
 - Rich querying capabilities with PromQL
@@ -165,7 +165,7 @@ Cloud-based observability platform offering full-stack monitoring, including API
 
 - Easy integration for monitoring APIs with automatic instrumentation
 - Real-time monitoring of API requests, response times, and error rates
-- Built-in distributed tracing to track API calls across services
+- Built-in [distributed tracing](https://signoz.io/blog/distributed-tracing/) to track API calls across services
 - Advanced alerting, anomaly detection, and root cause analysis
 
 ### Cons
@@ -301,7 +301,7 @@ A full-stack observability platform that provides monitoring for cloud-native ap
 
 ### Pros
 
-- Unified observability for **logs, metrics, and traces**, great for **full-stack API monitoring**
+- [Unified observability](https://signoz.io/unified-observability/) for **logs, metrics, and traces**, great for **full-stack API monitoring**
 - Supports **cloud-native applications** and microservices architectures
 - Offers real-time API monitoring with **custom metrics** and **alerting**
 - Provides **log management** capabilities alongside API monitoring, useful for troubleshooting API failures
@@ -499,7 +499,7 @@ As a developer, I understand you don’t want another bloated dashboard—you wa
 
 If you're building modern services, **observability > ping checks**.
 
-Tools like **SigNoz** and **Prometheus** give you low-level visibility and full trace context. For production-grade monitoring that’s also OpenTelemetry-native, **SigNoz** hits that sweet spot—open source, dev-first, and purpose-built for debugging distributed systems.
+Tools like **[SigNoz](https://signoz.io/docs/introduction/)** and **Prometheus** give you low-level visibility and full trace context. For production-grade monitoring that’s also OpenTelemetry-native, **SigNoz** hits that sweet spot—open source, dev-first, and purpose-built for debugging distributed systems.
 
 > Debug faster, monitor smarter, and don’t wait till users rage-tweet at you.
 > 

data/blog/apm-tools.mdx

@@ -68,7 +68,7 @@ A few essential APM benefits in solving performance issues are as follows:
 
 ### SigNoz
 
-**[SigNoz](https://signoz.io/)** is a full-stack open-source APM and observability tool. It provides a unified UI for application metrics and traces so that there is no need to switch between different tools like Jaeger and Prometheus. It also provides infrastructure metrics like CPU Load Average, CPU Utilization, System Memory Usage.
+**[SigNoz](https://signoz.io/)** is a full-stack open-source [APM and observability](https://signoz.io/guides/apm-vs-observability/) tool. It provides a unified UI for application metrics and traces so that there is no need to switch between different tools like Jaeger and Prometheus. It also provides infrastructure metrics like CPU Load Average, CPU Utilization, System Memory Usage.
 
 Using SigNoz, you can track things like:
 
@@ -241,7 +241,7 @@ Pricing starts at \$31 per host per month if billed annually. It also has an on-
 
 It was initially developed at SoundCloud in 2012 before being released as an open-source project. It was the second project to graduate from CNCF after Kubernetes. Prometheus can only be used to capture metrics, and nothing else.
 
-Prometheus monitoring stack includes the following components:
+[Prometheus monitoring](https://signoz.io/guides/what-is-prometheus-for-monitoring/) stack includes the following components:
 
 - Prometheus server
 - Client Libraries & Exporters
@@ -305,7 +305,7 @@ Some of the key features of the Lightstep APM tool includes:
 
 <a href = "https://zipkin.io/" rel="noopener noreferrer nofollow" target="_blank" ><b>Zipkin</b></a> is an open-source APM tool used for distributed tracing. Zipkin captures timing data need to troubleshoot latency problems in service architectures. In distributed systems, it's a challenge to trace user requests across different services. If a request fails or takes too long, distributed tracing helps to identify the events that caused it.
 
-Zipikin was initially developed at Twitter and drew inspiration from Google's Dapper. Unique identifiers called Trace ID are attached to each request which then identifies that request across services.
+Zipikin was initially developed at Twitter and drew inspiration from Google's Dapper. Unique identifiers called [Trace ID](https://signoz.io/comparisons/opentelemetry-trace-id-vs-span-id/) are attached to each request which then identifies that request across services.
 
 Zipkin's architecture includes:
 
@@ -417,7 +417,7 @@ Some of the key features of the Elastic APM tool includes:
 Some of the key features of the Pinpoint APM tool includes:
 
 - Application topology at a glance
-- Real-time application monitoring
+- [Real-time application monitoring](https://signoz.io/application-performance-monitoring/)
 - Code-level visibility to every transaction
 - APM agents which require minimal changes to code
 - Minimal impact on performance
@@ -444,7 +444,7 @@ Some of the key features of the Apache Skywalking APM tool includes:
 - Root cause analysis with code profiling
 - Service topology map analysis
 - Slow services and endpoint detection
-- Distributed tracing and context propagation
+- Distributed tracing and [context propagation](https://signoz.io/blog/opentelemetry-context-propagation/)
 
 Skywalking also supports the collection of telemetry data in multiple formats.
 
@@ -465,7 +465,7 @@ Skywalking also supports the collection of telemetry data in multiple formats.
 - Language supported: .Net, Go, Java, Node.js, PHP, Python and Ruby
 - Application service topology maps
 - Identify the root cause of performance issues
-- Distributed tracing, host and IT infrastructure monitoring with dozens of integrations
+- Distributed tracing, host and IT [infrastructure monitoring](https://signoz.io/guides/infrastructure-monitoring/) with dozens of integrations
 
 <figure data-zoomable align='center'>
     <img className="box-shadowed-image"
@@ -521,7 +521,7 @@ Some of the key features of AWS X-Ray includes:
 <a href = "https://stackify.com/retrace/" rel="noopener noreferrer nofollow" target="_blank" ><b>Stackify Retrace</b></a> is an APM tool that integrates code profiling, error tracking, application logs and more. Some of the key features of the Stackify Retrace includes:
 
 - Language support: .NET, PHP, Node.js, Ruby, Python, or Java stack
-- Centralized logging and error-tracking
+- [Centralized logging](https://signoz.io/blog/centralized-logging/) and error-tracking
 - Application and server metrics
 - Identify bottlenecks in your tech stack by seeing top web requests, slow web requests, SQL query performance