Analyzing Terabytes of VPC Flow Log data – Part 2 – Notes from the field

First  read –  Analyzing Terabytes of VPC Flow Log Data – part 1

Example Workflow

1. Ingestion and Storage:
   • Configure VPC Flow Logs to send logs to an S3 bucket.
   • Use AWS Glue to create a catalog of the data.
2. Data Processing:
   • Set up an Amazon EMR cluster with Apache Spark.
   • Use Spark to process and transform the data, e.g., filtering specific IP ranges, aggregating traffic data, etc.
   python

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   from pyspark.sql import SparkSession

   spark = SparkSession.builder.appName("VPCFlowLogsAnalysis").getOrCreate()

   # Load data from S3
   df = spark.read.json("s3://your-bucket/vpc-flow-logs/*")

   # Data transformation
   df_filtered = df.filter(df['destination_port'] == 443)

   # Aggregation
   df_aggregated = df_filtered.groupBy("source_address").sum("bytes")

   # Save processed data back to S3
df_aggregated.write.mode("overwrite").json("s3://your-bucket/processed-vpc-flow-logs/")


3. Data Analysis:
   • Use Athena to query the processed data stored in S3.
   sql

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   SELECT source_address, SUM(bytes) AS total_bytes
FROM processed_vpc_flow_logs
GROUP BY source_address
ORDER BY total_bytes DESC;

4. Visualization and Reporting:
   • Connect Amazon QuickSight to Athena or Redshift.
   • Create dashboards to visualize metrics like total bytes transferred, top source IPs, etc.

Optimization Tips

• Partitioning: Partition the VPC Flow Log data in S3 by date (e.g., year/month/day) to improve query performance.
• Compression: Use data compression formats like Parquet or ORC to reduce storage costs and improve query performance.
• Scaling EMR: Adjust the size and number of nodes in the EMR cluster based on the volume of data and processing requirements.