Author: Kerry Koitzsch
Pub Date: 2017
Size: 20 Mb
Learn advanced analytical techniques and leverage existing toolkits to make your analytic applications more powerful, precise, and efficient. This book provides the right combination of architecture, design, and implementation information to create analytical systems which go beyond the basics of classification, clustering, and recommendation.
In Pro Hadoop Data Analytics best practices are emphasized to ensure coherent, efficient development. A complete example system will be developed using standard third-party components which will consist of the toolkits, libraries, visualization and reporting code, as well as support glue to provide a working and extensible end-to-end system.
The book emphasizes four important topics:
The importance of end-to-end, flexible, configurable, high-performance data pipeline systems with analytical components as well as appropriate visualization results.
Best practices and structured design principles. This will include strategic topics as well as the how to example portions.
The importance of mix-and-match or hybrid systems, using different analytical components in one application to accomplish application goals. The hybrid approach will be prominent in the examples.
Use of existing third-party libraries is key to effective development. Deep dive examples of the functionality of some of these toolkits will be showcased as you develop the example system.
What You’ll Learn
- The what, why, and how of building big data analytic systems with the Hadoop ecosystem
- Libraries, toolkits, and algorithms to make development easier and more effective
- Best practices to use when building analytic systems with Hadoop, and metrics to measure performance and efficiency of components and systems
- How to connect to standard relational databases, noSQL data sources, and more
- Useful case studies and example components which assist you in creating your own systems
Who This Book Is For
Software engineers, architects, and data scientists with an interest in the design and implementation of big data analytical systems using Hadoop, the Hadoop ecosystem, and other associated technologies.
Table of Contents
Part I: Concepts
1: Overview: Building Data Analytic Systems with Hadoop
2: A Scala and Python Refresher
3: Standard Toolkits for Hadoop and Analytics
4: Relational, NoSQL, and Graph Databases
5: Data Pipelines and How to Construct Them
6: Advanced Search Techniques with Hadoop, Lucene, and Solr
Part II: Architectures and Algorithms
7: An Overview of Analytical Techniques and Algorithms
8: Rule Engines, System Control, and System Orchestration
9: Putting It All Together: Designing a Complete Analytical System
Part III: Components and Systems
10: Data Visualizers: Seeing and Interacting with the Analysis
Part IV: Case Studies and Applications
11: A Case Study in Bioinformatics: Analyzing Microscope Slide Data
12: A Bayesian Analysis Component: Identifying Credit Card Fraud
13: Searching for Oil: Geographical Data Analysis with Apache Mahout
14: “Image As Big Data” Systems: Some Case Studies
15: Building a General Purpose Data Pipeline
16: Conclusions and the Future of Big Data Analysis
A : Setting Up the Distributed Analytics Environment
B: Getting, Installing, and Running the Example Analytics System