Cloud Applications: MapReduce, Spark, and Apache Kafka
The chapter covers the core technologies pivotal for processing and managing vast datasets and real-time data in cloud environments, focusing on MapReduce, Apache Spark, and Apache Kafka. It explains the foundational principles of distributed data processing, the evolution of MapReduce to Spark for enhanced performance, and the role of Kafka in constructing scalable and fault-tolerant data pipelines. Understanding these systems is crucial for developing cloud-native applications aimed at big data analytics and machine learning.
Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- MapReduce is a programming model that simplifies large-scale data processing by breaking down computations into smaller tasks.
- Apache Spark extends the capabilities of MapReduce by enabling in-memory computation, which increases performance for iterative algorithms and interactive queries.
- Apache Kafka serves as a distributed streaming platform facilitating high-performance, real-time data pipelines.
Key Concepts
- -- MapReduce
- A programming model and execution framework for processing large datasets in parallel across distributed systems.
- -- Apache Spark
- An open-source unified analytics engine that supports batch and real-time data processing with in-memory computing capabilities.
- -- Apache Kafka
- A distributed streaming platform that enables the building of real-time data pipelines and streaming applications.
- -- Resilient Distributed Datasets (RDDs)
- The fundamental data abstraction in Spark that represents a fault-tolerant, distributed collection of data, supporting parallel operations.
- -- Streaming Analytics
- The real-time processing of data streams to extract insights as events occur.
Additional Learning Materials
Supplementary resources to enhance your learning experience.