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商品描述
Ready to unlock the power of your data? With this comprehensive guide, you’ll learn how to build and maintain reliable, scalable, distributed systems with Apache Hadoop. This book is ideal for programmers looking to analyze datasets of any size, and for administrators who want to set up and run Hadoop clusters.
You’ll find illuminating case studies that demonstrate how Hadoop is used to solve specific problems. This third edition covers recent changes to Hadoop, including material on the new MapReduce API, as well as MapReduce 2 and its more flexible execution model (YARN).
- Store large datasets with the Hadoop Distributed File System (HDFS)
- Run distributed computations with MapReduce
- Use Hadoop’s data and I/O building blocks for compression, data integrity, serialization (including Avro), and persistence
- Discover common pitfalls and advanced features for writing real-world MapReduce programs
- Design, build, and administer a dedicated Hadoop cluster—or run Hadoop in the cloud
- Load data from relational databases into HDFS, using Sqoop
- Perform large-scale data processing with the Pig query language
- Analyze datasets with Hive, Hadoop’s data warehousing system
- Take advantage of HBase for structured and semi-structured data, and ZooKeeper for building distributed systems
商品描述(中文翻譯)
準備好釋放數據的力量了嗎?這本全面的指南將教您如何使用 Apache Hadoop 建立和維護可靠、可擴展的分散式系統。本書非常適合希望分析任何大小數據集的程式設計師,以及希望設置和運行 Hadoop 叢集的管理員。
您將找到啟發性的案例研究,展示 Hadoop 如何用於解決特定問題。本書的第三版涵蓋了 Hadoop 的最新變更,包括有關新 MapReduce API 的內容,以及 MapReduce 2 和其更靈活的執行模型(YARN)。
- 使用 Hadoop 分散式檔案系統(HDFS)儲存大型數據集
- 使用 MapReduce 執行分散式計算
- 利用 Hadoop 的數據和 I/O 基礎組件進行壓縮、數據完整性、序列化(包括 Avro)和持久性
- 發現撰寫實際 MapReduce 程式的常見陷阱和進階功能
- 設計、建構和管理專用的 Hadoop 叢集—或在雲端運行 Hadoop
- 使用 Sqoop 將數據從關聯式資料庫載入 HDFS
- 使用 Pig 查詢語言執行大規模數據處理
- 使用 Hive,Hadoop 的數據倉儲系統,分析數據集
- 利用 HBase 處理結構化和半結構化數據,並使用 ZooKeeper 建立分散式系統
