Scaling Big Data with Hadoop and Solr

دسته بندی: شبکه سازی: اینترنت
ویرایش:  
نویسندگان: Karambelkar H.  
سری:  
 
ناشر:  
سال نشر:  
تعداد صفحات: 144 
زبان: English 
فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) 
حجم فایل: 2 مگابایت 

قیمت کتاب (تومان) : 52,000

Packt Publishing, 2013. — 144 p. — ISBN-13: 978-1-78328-137-4.
На англ. языке. از آنجایی که داده ها روز به روز به طور تصاعدی رشد می کنند، استخراج اطلاعات به خودی خود به یک فعالیت خسته کننده تبدیل می شود. فناوری هایی مانند Hadoop در تلاش هستند تا برخی از نگرانی ها را برطرف کنند، در حالی که Solr جستجوی وجهی با سرعت بالا را ارائه می دهد. در کنار هم قرار دادن این دو فناوری به سازمان‌ها کمک می‌کند تا مشکل استخراج اطلاعات از داده‌های بزرگ را با ارائه قابلیت‌های عالی جستجوی وجهی توزیع‌شده حل کنند. مقیاس‌سازی کلان داده‌ها با Hadoop and Solrراهنمای گام به گامی است که به شما کمک می کند موتورهای جستجوی سازمانی با کارایی بالا را در حین مقیاس بندی داده ها بسازید. این کتاب که با اصول اولیه Apache Hadoop و Solr شروع می‌شود، سپس به موضوعات پیشرفته بهینه‌سازی جستجو با چند مورد جالب استفاده در دنیای واقعی و نمونه کد جاوا می‌پردازد.

Packt Publishing, 2013. — 144 p. — ISBN-13: 978-1-78328-137-4.
На англ. языке. As data grows exponentially day-by-day, extracting information becomes a tedious activity in itself. Technologies like Hadoop are trying to address some of the concerns, while Solr provides high-speed faceted search. Bringing these two technologies together is helping organizations resolve the problem of information extraction from Big Data by providing excellent distributed faceted search capabilities. Scaling Big Data with Hadoop and Solr is a step-by-step guide that helps you build high performance enterprise search engines while scaling data. Starting with the basics of Apache Hadoop and Solr, this book then dives into advanced topics of optimizing search with some interesting real-world use cases and sample Java code. Scaling Big Data with Hadoop and Solr starts by teaching you the basics of Big Data technologies including Hadoop and its ecosystem and Apache Solr. It explains the different approaches of scaling Big Data with Hadoop and Solr, with discussion regarding the applicability, benefits, and drawbacks of each approach. It then walks readers through how sharding and indexing can be performed on Big Data followed by the performance optimization of Big Data search. Finally, it covers some real-world use cases for Big Data scaling. With this book, you will learn everything you need to know to build a distributed enterprise search platform as well as how to optimize this search to a greater extent resulting in maximum utilization of available resources. What You Will Learn:
Understand Apache Hadoop, its ecosystem, and Apache Solr;
Learn different industry-based architectures while designing Big Data enterprise search and understand their applicability and benefits;
Write map/reduce tasks for indexing your data;
Fine-tune the performance of your Big Data search while scaling your data;
Increase your awareness of new technologies available today in the market that provide you with Hadoop and Solr;
Use Solr as a NOSQL database;
Configure your Big Data instance to perform in the real world;
Address the key features of a distributed Big Data system such as ensuring high availability and reliability of your instances;
Integrate Hadoop and Solr together in your industry by means of use cases. Who This Book Is For:
Scaling Big Data with Hadoop and Solr provides guidance to developers who wish to build high-speed enterprise search platforms using Hadoop and Solr. This book is primarily aimed at Java programmers who wish to extend the Hadoop platform to make it run as an enterprise search without any prior knowledge of Apache Hadoop and Solr.