docs: update broken link paths

Author: BLMgithub

README.md

@@ -64,6 +64,8 @@ By leveraging the Polars Rust engine (Lazy API), the system achieves near-optima
 | 40M Snapshot (8GB / 4 vCPU) |
 | :---: |
 | ![engine-performance-8gb](assets/screenshots/engine-performance-8gb-4cpu.png) |
+> Benchmark data: [`40m_stats_log.csv`](assets/benchmarks/polars/)
+> Dataset : [`Dataset Information`](data/)
 
 | Metric | Data | 
 |:---|:---|
@@ -72,6 +74,7 @@ By leveraging the Polars Rust engine (Lazy API), the system achieves near-optima
 | Efficiency (Processing) | ~307k Rows / Second |
 | Total Runtime (Wall-Clock) | 130 Seconds |
 
+
 *   **Maximized Memory Density:** The **Primitive Integer Pipeline** allows a ~5.34GB analytical model to process within the 8GB RAM limit by shrinking join-key overhead by ~16x.
 *   **Near-Linear Performance Scaling:** The engine saturates available vCPUs, yielding high throughput during streaming execution.
 *   **Zero-Idle Economics:** 100% serverless execution ensures zero billable time during idle periods.

assets/benchmarks/polars/README.md

@@ -1,6 +1,6 @@
 # Measurement Methodology
 
-This section details the methodology used to capture the memory metrics in the [`GCP Stress-Test Metrics (Scaling Efficiency)`](../../../README.md#gcp-stress-test-metrics-scaling-efficiency)
+This section details the methodology used to capture the memory metrics in the [`GCP Stress-Test Metrics (Scaling Efficiency)`](../../../README.md###gcp-stress-test-metrics-scaling-efficiency)
 
 The telemetry logger below was added to the orchestrator for a specific benchmarking run. 
 

data/README.md

@@ -3,7 +3,7 @@
 This directory serves as the local state provider for the pipeline when executing in a non-cloud environment. It mimics the structure of the Google Cloud Storage (GCS) buckets.
 
 ## Synthetic Dataset
-To replicate the high-volume environment described in the [GCP Stress-Test Metrics (Scaling Efficiency)](/README.md#gcp-stress-test-metrics-scaling-efficiency) section, you can download the 40M-row synthetic dataset here: [**Kaggle Dataset Link**](https://www.kaggle.com/datasets/melvidabryan/e-commerce-synthetic-dataset)
+To replicate the high-volume environment described in the [GCP Stress-Test Metrics (Scaling Efficiency)](/README.md###GCP-Stress-Test-Metrics) section, you can download the 40M-row synthetic dataset here: [**Kaggle Dataset Link**](https://www.kaggle.com/datasets/melvidabryan/e-commerce-synthetic-dataset)
 
 > *Note: This upload contains the **Contracted Version** of the dataset. The original "Raw" state, totaling approximately ~26GB of unrefined CSVs was omitted to prioritize transfer efficiency.*