Hadoop for finance essentials : harness big data to provide meaningful insights, analytics, and business intelligence for your financial institution /

Annotation

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Tiwari, Rajiv (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham, UK : Packt Publishing, 2015.
Colección:Community experience distilled.
Temas:
Apache Hadoop.
Big data.
Business intelligence.
Information retrieval.
Données volumineuses.
Recherche de l'information.
information retrieval.
COMPUTERS > Database Management > General.
COMPUTERS > Desktop Applications > Databases.
COMPUTERS > System Administration > Storage & Retrieval.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Big Data Overview; What is big data?; Data volume; Data velocity; Data variety; Big data technology evolution; History; Current; Future; Big data landscape; Storage; NoSQL; NoSQL database types; Resource management; Data governance; Batch computing; Real-time computing; Data integration tools; Machine learning; Business intelligence and virtualization; Careers in big data; Hadoop architecture; HDFS cluster; MapReduce V1; MapReduce V2
  • YARN; The Hadoop jungle explained
  • Big data tamedHadoop
  • the hero; HDFS
  • Hadoop Distributed Filesystem; MapReduce; HBase; Hive; Pig; Zookeeper; Oozie; Flume; Sqoop; Hadoop distributions; Distribution
  • on premise; Distribution
  • cloud; Summary; Chapter 2: Big Data in Financial Services; Big data use cases across industry sectors; Healthcare; Human science; Telecom; Online retailer; Why big data in the financial sector?; Big data use cases in the financial sector; Data archival on HDFS; Regulatory; Fraud detection; Tick data; Risk management; Customer behavior prediction; Sentiment analysis (unstructured); Other use cases
  • Big data evolution in financeBig data tools
  • what to learn; Getting your data into HDFS; Querying data from HDFS; SQL on Hadoop; Real time; Data governance and operations; ETL tools; Data analytics and business intelligence; Big data implementations in finance; Key challenges; Overcoming the challenges; Generate interest (play area); Pilot with a low-cost project; Hadoop is live
  • now scale it up; Summary; Chapter 3: Hadoop in the Cloud; The big data cloud story; The why; The when; What's the catch?; Project details
  • risk simulations in the cloud; Solution; Current world; Target world
  • Data collectionConfiguring the Hadoop cluster; Data upload; Data transformation; Data analysis; Summary; Chapter 4: Data Migration Using Hadoop; Project details
  • archive your transaction data; Solution; Project Phase 1
  • split trade data into DW and Hadoop; Current world; Target world; Data collection; How to do it; Data analysis; Project Phase 2
  • migrate data from relational DW into Hadoop; Current world; Target world; Data collection; Data analysis; Summary; Chapter 5: Getting Started; Project details
  • risk and regulatory reporting; Solution; Current world; Target world; Data collection
  • Option 1
  • Apache OozieOption 2
  • ETL tool ingestion; Data transformation; Hive or Pig?; Hive; Pig; Java MapReduce; Data analysis; BI tools; Summary; Chapter 6: Getting Experienced; Real-time big data; Project details
  • Identifying fraudulent transactions; Solution; Current world; Target world; Markov Chain Model execution (batch mode); Storm architecture; Spark architecture; Data collection; Using Storm; Using Spark; Data transformation; Using Storm; Using Spark; Summary; Chapter 7: Scale It Up; Scale it up
  • actually horizontally; Few more big data use cases; Use case
  • fraud again

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