Big Data Evolution & MapReduce
"Trace how enterprise data architectures evolved from monolithic mainframes and single-node SMP ETL engines (Informatica, SSIS) to scale-out distributed clusters (HDFS, MapReduce) and in-memory execution with Apache Spark."
What You'll Master
The Scaling Boundary
Why legacy mainframes & single-node ETL tools hit physical RAM bounds at scale.
Hadoop Architecture
How HDFS block storage & YARN decouple distributed storage from compute.
MapReduce Execution
Physical mechanics of Input Splits, Mappers, Combiners, Shuffling, & Reducers.
Spark's In-Memory Engine
How RDDs, DataFrames, Catalyst, & Tungsten eliminated disk-bound latency.
Learning Path & Course Syllabus
Vertical scaling (Scale-Up) vs horizontal cluster scaling (Scale-Out), single-node SMP RAM bounds, and shared-disk bottlenecks.
Master-slave cluster architecture: HDFS NameNode/DataNode block distribution, MapReduce compute, Hive metastore, Pig, and Oozie.
Data execution physics: Input Splits → Map → Combine → Network Shuffle/Sort → Reduce, with Python simulation trace.
Chronological evolution from Spark 0.x RDD genesis, Spark 1.x DataFrames, Spark 2.x Tungsten, Spark 3.x AQE, to Spark 4.0.
Step-by-step dataset trace parsing raw HTTP access logs (`/api/v1/checkout`) to compute endpoint latency using Map & Reduce.
FAANG-style scenario questions covering HDFS write pipeline failures, speculative stragglers, High Availability fencing, and Erasure Coding.
What's Included in This Module
| Area | Overview |
|---|---|
| Topics Covered | Mainframes, SMP Bottlenecks, HDFS, YARN, MapReduce, PySpark, Tungsten Engine |
| Practical Code | Python MapReduce Simulation & Access Log Data Tracing |
| Assessments | 1 Hands-On Log Parsing Lab + 1 FAANG System Design Interview Quiz |