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Book Description

Today’s digital companies demand real-time insights and immediate action for everything from purchase to fulfillment, recommendation, and more. As a result, many organizations are adopting fast data applications to accelerate the value they extract from data as it flows into the system. With this practical ebook, you’ll learn the common architectural patterns that form the foundation of successful fast data deployments.

Engineers from Lightbend identify the key characteristics of fast data architectures, separate them into functional blocks, and show you how to implement those functions using components like those in the SMACK stack—Spark, Mesos, Akka, Cassandra, and Kafka, as well as others. Architects will learn how to choose, combine, and run SMACK stack technologies to build resilient, scalable, and responsive systems that your company requires.

This ebook examines:

  • The anatomy of fast data applications: the application model, streaming data sources, processing engines, and data sinks
  • Functional composition of the SMACK stack and extensions
  • The event backbone that connects all the major components of a fast data platform together
  • Compute engines for transforming data into valuable insights
  • Storage systems that form the transition between the fast data domain and client applications
  • Patterns you can use in the data serving layer, including data-driven microservices
  • Container orchestrators in the substrate layer that provide resources to services, frameworks, and applications

Table of Contents

  1. Introduction
  2. 1. The Anatomy of Fast Data Applications
    1. A Basic Application Model
    2. Streaming Data Sources
      1. Stream Properties
    3. Processing Engines
    4. Data Sinks
  3. 2. Dissecting the SMACK Stack
    1. The SMACK Stack
    2. Functional Composition of the SMACK Stack
  4. 3. The Message Backbone
    1. Understanding Your Messaging Requirements
    2. Data Ingestion
    3. Fast Data, Low Latency
    4. Message Delivery Semantics
    5. Distributing Messages
  5. 4. Compute Engines
    1. Micro-Batch Processing
    2. One-at-a-Time Processing
    3. How to Choose
  6. 5. Storage
    1. Storage as the Fast Data Borders
    2. The Message Backbone as Transition Point
  7. 6. Serving
    1. Sharing Stateful Streaming State
    2. Data-Driven Microservices
    3. State and Microservices
  8. 7. Substrate
    1. Deployment Environments for Fast Data Apps
    2. Application Containerization
    3. Resource Scheduling
    4. Apache Mesos
    5. Kubernetes
    6. Cloud Deployments
  9. 8. Conclusions
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