Build With Me: PySpark Big Data

Step 1: Cluster Config & Extraction

We start by telling Spark how much computing power we want, and reading from our Data Lake.

from pyspark.sql import SparkSession
from pyspark.sql.functions import col, isnan

# Configure our computing cluster
WORKER_NODES = 4
BATCH_SIZE = 500  # MB per second incoming

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

# Read raw JSON logs from S3
df = spark.read.json("s3://voidx-data-lake/raw_events/*.json")

Step 2: The Bottleneck

Let's add our transformation layer. We need to filter out corrupted logs where the user_id is missing.

# Filter out bad rows
clean_df = df.filter(~isnan(col("user_id")))

# Save the clean data to our Warehouse
clean_df.write.format("parquet").save("s3://warehouse/clean/")

Your Turn: Hit Submit Job. Look at the Spark Cluster node in the center. With only 4 worker nodes, the cluster cannot process 500MB/s fast enough. The box turns red, and the backlog skyrockets. Your pipeline is failing!

Step 3: Scaling the Cluster

This is the magic of distributed computing. We don't need to rewrite our code to make it faster; we just throw more hardware at it.

Your Turn: Click Stop Job. In your code editor, change WORKER_NODES = 4 to WORKER_NODES = 12. Hit Submit Job again.

Watch the visualizer! With 12 nodes, the cluster processes the data instantly. The backlog stays at 0.0 GB, and the clean data flows beautifully into the Data Warehouse. You just scaled a Big Data pipeline!