1256  Big Data Overview

1256.1 Big Data Overview

This section provides a stable anchor for cross-references to big data concepts across the book. Big data in IoT represents a fundamental shift from traditional data management, requiring distributed processing where data is filtered at the edge, aggregated at intermediate nodes, and only valuable insights reach cloud storage.

Learning Objectives

After completing this series of chapters, you will be able to:

  • Understand the characteristics of big data in IoT contexts (the 5 V’s)
  • Analyze why traditional databases cannot handle IoT scale
  • Implement edge processing strategies for bandwidth and cost optimization
  • Select appropriate big data technologies (Hadoop, Spark, Kafka, time-series databases)
  • Design stream processing systems with Lambda architecture
  • Debug and monitor production big data pipelines
  • Apply lessons from real-world smart city deployments
TipMVU: Minimum Viable Understanding

Core concept: Big data in IoT is characterized by the 5 V’s–Volume (terabytes daily), Velocity (real-time streams), Variety (structured and unstructured), Veracity (data quality), and Value (actionable insights). Why it matters: Traditional analytics tools cannot handle IoT scale; understanding big data patterns enables you to design systems that process millions of events per second without data loss. Key takeaway: Start with stream processing (Apache Kafka/Flink) for real-time needs, use data lakes for raw storage, and apply batch processing (Spark) for historical analysis–the architecture follows your latency requirements.

1256.2 Chapter Series

This topic has been organized into focused chapters for easier learning:

1256.2.1 Big Data Fundamentals

Understanding the scale challenge: the 5 V’s of big data, why traditional databases fail at IoT scale, and the economics of distributed systems. Includes the Sensor Squad introduction for beginners.

1256.2.2 Edge Processing for Big Data

The 90/10 rule: how edge computing reduces data volume by 99%, making impossible big data problems manageable. Cost comparisons and real-world traffic camera examples.

1256.2.3 Big Data Technologies

Technology deep-dive: Apache Hadoop ecosystem (HDFS, Spark, Hive, Kafka), time-series databases (InfluxDB, TimescaleDB), and when to use each technology.

1256.2.4 Big Data Pipelines

Architecture patterns: Lambda architecture combining batch and stream processing, ETL vs ELT, data lakes vs data warehouses, and windowing strategies for stream processing.

1256.2.5 Big Data Operations

Production readiness: monitoring metrics, debugging common issues (Kafka lag, OOM errors, late arrivals), and avoiding the seven deadly pitfalls of IoT big data.

1256.2.6 Big Data Case Studies

Real-world applications: Barcelona Smart City deployment with concrete numbers, worked examples for data lake architecture, and decision frameworks for technology selection.

Data Analytics Topics:

Related Architectures:

Learning Hubs:

1256.3 Prerequisites

Before diving into these chapters, you should be familiar with:

1256.4 What’s Next

Start with Big Data Fundamentals to understand the scale challenge, or jump to specific topics: