Tracking and measuring website traffic beyond search analytics is crucial for optimizing performance, understanding visitor behavior, and enhancing security. AWS offers powerful tools like Elastic Load Balancing (ELB), AWS Web Application Firewall (WAF), and Amazon Athena for comprehensive traffic analysis. This guide will explore how to set up and utilize these AWS services to monitor and measure web statistics beyond traditional search analytics.

Setting Up AWS ELB and WAF

  1. Elastic Load Balancing (ELB):
    • Configure AWS Elastic Load Balancer to distribute incoming traffic across multiple targets such as EC2 instances or containers.
    • ELB automatically scales with your application and generates detailed access logs capturing client IP addresses, request paths, and response codes.
  2. AWS WAF on ELB:
    • Deploy AWS Web Application Firewall (WAF) to safeguard your applications running on ELB against common web exploits.
    • Define rules within WAF to filter web traffic based on criteria like IP addresses, HTTP headers, and request methods.
    • Enable logging within WAF to capture comprehensive information about requests that match your specified rules.

Storing WAF Logs in Amazon S3

  1. Storing WAF Logs in Amazon S3:
    • Configure WAF to log all filtered requests into an Amazon S3 bucket.
    • WAF logs are stored in Apache Parquet format, optimized for cost-effective storage and subsequent analysis using Amazon Athena.

Analyzing Traffic with Amazon Athena

  1. Analyzing WAF Logs Using Amazon Athena:
    • Create an Amazon Athena table to reference your WAF logs stored in S3.

— Create Athena schema for AWS WAF logs stored in S3

— Define your database

CREATE DATABASE IF NOT EXISTS waf_logs_db;

— Switch to your database context

USE waf_logs_db;

— Create table for AWS WAF logs

CREATE EXTERNAL TABLE IF NOT EXISTS waf_logs (

    timestamp STRING,

    httpMethod STRING,

    uri STRING,

    clientIp STRING,

    countryCode STRING,

    userAgent STRING,

    action STRING,

    ruleName STRING,

    bytesIn INT,

    bytesOut INT,

    requestId STRING,

    status INT,

    responseCode INT,

    httpVersion STRING,

    protocol STRING,

    host STRING,

    userAgentBlock STRING,

    sqlInjectionMatchedValues ARRAY<STRING>,

    regexPatternSetMatchedRules ARRAY<STRING>,

    rateBasedRuleMatched BOOLEAN,

    rateBasedRuleActualValue INT

)

PARTITIONED BY (year STRING, month STRING, day STRING)

STORED AS PARQUET

LOCATION ‘s3://your-waf-logs-bucket/’;

— MSCK REPAIR TABLE to add partitions if not using AWS Glue crawler

MSCK REPAIR TABLE waf_logs;

  1. Querying and Visualizing Traffic Data:
    • Utilize SQL queries within Amazon Athena to analyze traffic patterns by various dimensions:
      • Source country: Gain insights into the geographic distribution of your visitors.
      • Device type: Identify traffic originating from different devices (desktops, mobiles, tablets).
      • IP address: Detect potentially malicious or suspicious IP addresses accessing your site.
      • Request patterns: Analyze popular pages, frequent errors, and more to optimize site performance.

Benefits of AWS ELB, WAF, and Athena for Traffic Analysis

  • Enhanced Security: Rapidly identify and mitigate potential security threats in real-time using AWS WAF.
  • Performance Optimization: Fine-tune your application based on insights gleaned from detailed traffic analytics.
  • Cost Efficiency: Pay only for the storage and compute resources used with AWS services like S3 and Athena.

Conclusion

Effectively tracking and measuring website traffic beyond traditional search analytics using AWS ELB, WAF logs, and Athena empowers businesses to make informed decisions, improve user experience, and bolster web application security. By harnessing AWS’s capabilities for traffic analysis, organizations can proactively manage and optimize their online presence.