Hadoop in practice

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This book collects 85 Hadoop examples in a problem/solution format, addressing specific tasks like querying big data with Pig or writing a log file loader. Each technique is explored step by step, allowing readers to learn how to build and deploy solutions while understanding the design thinking behind them. As you engage with these tasks, your familiarity with Hadoop and big data will grow. Hadoop, an open-source MapReduce platform, is designed for querying and analyzing data across large clusters, making it especially effective for big data systems. It supports mission-critical software at major companies like Apple, eBay, LinkedIn, Yahoo, and Facebook, offering developers efficient ways to store, manage, and analyze data. The book balances conceptual foundations with practical recipes, covering key areas such as data ingress and egress, serialization, and LZO compression. Each technique is presented in a way that builds a well-structured codebase, which you can adapt to your needs. It assumes readers have a basic understanding of Hadoop. Inside, you'll find a conceptual overview of Hadoop and MapReduce, 85 practical techniques, real problems with real solutions, and guidance on integrating MapReduce with R. The author, Alex Holmes, is a senior software engineer with significant experience in tackling big data challenges using Hadoop.