Big Data

What I do

• Design and implement big data architectures
• Process large-scale datasets with distributed systems
• Optimize data pipelines for throughput
• Work with Hadoop, Spark, and cloud data platforms
• Implement real-time streaming data processing
• Manage data lakes and warehouses at scale

When to use me

Use me when:

• Working with datasets beyond single-machine capacity
• Building data pipelines at scale
• Processing streaming data in real-time
• Designing data lake architectures
• Optimizing big data workflows
• Managing petabyte-scale storage

Key Concepts

Big Data Ecosystem

• Storage: HDFS, S3, Azure Blob, GCS
• Processing: Spark, Flink, Hadoop MapReduce
• Query: Hive, Presto, BigQuery, Snowflake
• Stream: Kafka, Pulsar, Kinesis, Dataflow
• Orchestration: Airflow, Dagster, Prefect

Spark DataFrame Example

from pyspark.sql import SparkSession
from pyspark.sql.functions import col, sum, avg

spark = SparkSession.builder \
    .appName("Analytics") \
    .config("spark.sql.shuffle.partitions", 200) \
    .getOrCreate()

# Read from multiple sources
df = spark.read \
    .parquet("s3://data-lake/raw/events/") \
    .filter(col("date") >= "2024-01-01")

# Transform and aggregate
result = df.groupBy("user_id", "product_category") \
    .agg(
        sum("purchase_amount").alias("total_spent"),
        avg("purchase_amount").alias("avg_purchase"),
        count("*").alias("num_purchases")
    ) \
    .orderBy(col("total_spent").desc())

# Write aggregated results
result.write \
    .mode("overwrite") \
    .partitionBy("product_category") \
    .parquet("s3://data-warehouse/analytics/")

Data Processing Patterns

• Batch: Scheduled, periodic processing
• Streaming: Continuous, real-time
• Lambda: Batch + speed layers
• Kappa: Streaming-only architecture

Cloud Big Data Services

• AWS: EMR, Redshift, Athena, Kinesis
• Azure: Databricks, Synapse, Data Lake
• GCP: Dataproc, BigQuery, Dataflow