Data Processing
Data Processing¶
"Gen-Z doesn't hate complexity. They hate unclear systems."
Transforming and analyzing data at scale.
Overview¶
Data processing transforms raw data into analysis-ready datasets. This section covers the tools and patterns for processing data at scale.
Key Topics¶
Apache Spark¶
Distributed data processing with Spark.
Learn about: - Spark architecture - RDDs, DataFrames, Datasets - Optimization techniques - Best practices
Google BigQuery¶
Serverless data warehouse and analytics.
Learn about: - BigQuery architecture - Query optimization - Partitioning and clustering - Cost optimization
Processing Patterns¶
Batch Processing¶
When to use: - Large volumes - No real-time requirement - Complex transformations
Tools: Spark, BigQuery, Snowflake
Streaming Processing¶
When to use: - Real-time requirements - Event-driven architecture - Low-latency use cases
Tools: Flink, Spark Streaming, Dataflow
Related Topics¶
- Data Architecture - Where to process data
- Data Orchestration - Scheduling processing jobs
Next: Apache Spark →