Skip to main content
main-content

Über dieses Buch

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 ecosystemLibraries, toolkits, and algorithms to make development easier and more effectiveBest practices to use when building analytic systems with Hadoop, and metrics to measure performance and efficiency of components and systemsHow to connect to standard relational databases, noSQL data sources, and moreUseful case studies and example components which assist you in creating your own systemsWho 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.

Inhaltsverzeichnis

Frontmatter

Concepts

Frontmatter

2017 | OriginalPaper | Buchkapitel

Chapter 1. Overview: Building Data Analytic Systems with Hadoop

Kerry Koitzsch

2017 | OriginalPaper | Buchkapitel

Chapter 2. A Scala and Python Refresher

Kerry Koitzsch

2017 | OriginalPaper | Buchkapitel

Chapter 3. Standard Toolkits for Hadoop and Analytics

Kerry Koitzsch

2017 | OriginalPaper | Buchkapitel

Chapter 4. Relational, NoSQL, and Graph Databases

Kerry Koitzsch

2017 | OriginalPaper | Buchkapitel

Chapter 5. Data Pipelines and How to Construct Them

Kerry Koitzsch

2017 | OriginalPaper | Buchkapitel

Chapter 6. Advanced Search Techniques with Hadoop, Lucene, and Solr

Kerry Koitzsch

Architectures and Algorithms

Frontmatter

2017 | OriginalPaper | Buchkapitel

Chapter 7. An Overview of Analytical Techniques and Algorithms

Kerry Koitzsch

2017 | OriginalPaper | Buchkapitel

Chapter 8. Rule Engines, System Control, and System Orchestration

Kerry Koitzsch

2017 | OriginalPaper | Buchkapitel

Chapter 9. Putting It All Together: Designing a Complete Analytical System

Kerry Koitzsch

Components and Systems

Frontmatter

2017 | OriginalPaper | Buchkapitel

Chapter 10. Data Visualizers: Seeing and Interacting with the Analysis

Kerry Koitzsch

Case Studies and Applications

Frontmatter

2017 | OriginalPaper | Buchkapitel

Chapter 11. A Case Study in Bioinformatics: Analyzing Microscope Slide Data

Kerry Koitzsch

2017 | OriginalPaper | Buchkapitel

Chapter 12. A Bayesian Analysis Component: Identifying Credit Card Fraud

Kerry Koitzsch

2017 | OriginalPaper | Buchkapitel

Chapter 13. Searching for Oil: Geographical Data Analysis with Apache Mahout

Kerry Koitzsch

2017 | OriginalPaper | Buchkapitel

Chapter 14. “Image As Big Data” Systems: Some Case Studies

Kerry Koitzsch

2017 | OriginalPaper | Buchkapitel

Chapter 15. Building a General Purpose Data Pipeline

Kerry Koitzsch

2017 | OriginalPaper | Buchkapitel

Chapter 16. Conclusions and the Future of Big Data Analysis

Kerry Koitzsch

Backmatter

Weitere Informationen

Premium Partner

Neuer Inhalt

BranchenIndex Online

Die B2B-Firmensuche für Industrie und Wirtschaft: Kostenfrei in Firmenprofilen nach Lieferanten, Herstellern, Dienstleistern und Händlern recherchieren.

Whitepaper

- ANZEIGE -

Product Lifecycle Management im Konzernumfeld – Herausforderungen, Lösungsansätze und Handlungsempfehlungen

Für produzierende Unternehmen hat sich Product Lifecycle Management in den letzten Jahrzehnten in wachsendem Maße zu einem strategisch wichtigen Ansatz entwickelt. Forciert durch steigende Effektivitäts- und Effizienzanforderungen stellen viele Unternehmen ihre Product Lifecycle Management-Prozesse und -Informationssysteme auf den Prüfstand. Der vorliegende Beitrag beschreibt entlang eines etablierten Analyseframeworks Herausforderungen und Lösungsansätze im Product Lifecycle Management im Konzernumfeld.
Jetzt gratis downloaden!

Bildnachweise