{"product_id":"algorithms-and-data-structures-for-massive-datasets","title":"Algorithms and Data Structures for Massive Datasets","description":"\u003cp\u003e\u003cspan class=\"a-text-bold\" style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size: font-weight:\u003eMassive modern datasets make traditional data structures and algorithms grind to a halt. This fun and practical guide introduces cutting-edge techniques that can reliably handle even the largest distributed datasets.\u003c\/span\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003eIn \u003c\/span\u003e\u003cspan class=\"a-text-italic\" style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size: font-style: italic\u003eAlgorithms and Data Structures for Massive Datasets\u003c\/span\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e you will learn:\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003eProbabilistic sketching data structures for practical problems\u003cbr style=\"box-sizing: border-box;\"\u003eChoosing the right database engine for your application\u003cbr style=\"box-sizing: border-box;\"\u003eEvaluating and designing efficient on-disk data structures and algorithms\u003cbr style=\"box-sizing: border-box;\"\u003eUnderstanding the algorithmic trade-offs involved in massive-scale systems\u003cbr style=\"box-sizing: border-box;\"\u003eDeriving basic statistics from streaming data\u003cbr style=\"box-sizing: border-box;\"\u003eCorrectly sampling streaming data\u003cbr style=\"box-sizing: border-box;\"\u003eComputing percentiles with limited space resources\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cspan class=\"a-text-italic\" style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size: font-style: italic\u003eAlgorithms and Data Structures for Massive Datasets\u003c\/span\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e reveals a toolbox of new methods that are perfect for handling modern big data applications. You’ll explore the novel data structures and algorithms that underpin Google, Facebook, and other enterprise applications that work with truly massive amounts of data. These effective techniques can be applied to any discipline, from finance to text analysis. Graphics, illustrations, and hands-on industry examples make complex ideas practical to implement in your projects—and there’s no mathematical proofs to puzzle over. Work through this one-of-a-kind guide, and you’ll find the sweet spot of saving space without sacrificing your data’s accuracy.\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003ePurchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e \u003c\/p\u003e\u003ch4 style=\"box-sizing: border-box; padding: 0px 0px 4px; margin: 0px; text-rendering: optimizelegibility; font-weight: 700; font-size: 18px; line-height: 24px; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif\u003e\u003cspan style=\"box-sizing: border-box;\"\u003eAbout the technology\u003c\/span\u003e\u003c\/h4\u003e\u003cp\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003eStandard algorithms and data structures may become slow—or fail altogether—when applied to large distributed datasets. Choosing algorithms designed for big data saves time, increases accuracy, and reduces processing cost. This unique book distills cutting-edge research papers into practical techniques for sketching, streaming, and organizing massive datasets on-disk and in the cloud.\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e \u003c\/p\u003e\u003ch4 style=\"box-sizing: border-box; padding: 0px 0px 4px; margin: 0px; text-rendering: optimizelegibility; font-weight: 700; font-size: 18px; line-height: 24px; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif\u003e\u003cspan style=\"box-sizing: border-box;\"\u003eAbout the book\u003c\/span\u003e\u003c\/h4\u003e\u003cp\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003c\/span\u003e\u003cspan class=\"a-text-italic\" style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size: font-style: italic\u003eAlgorithms and Data Structures for Massive Datasets\u003c\/span\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e introduces processing and analytics techniques for large distributed data. Packed with industry stories and entertaining illustrations, this friendly guide makes even complex concepts easy to understand. You’ll explore real-world examples as you learn to map powerful algorithms like Bloom filters, Count-min sketch, HyperLogLog, and LSM-trees to your own use cases.\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e \u003c\/p\u003e\u003ch4 style=\"box-sizing: border-box; padding: 0px 0px 4px; margin: 0px; text-rendering: optimizelegibility; font-weight: 700; font-size: 18px; line-height: 24px; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif\u003e\u003cspan style=\"box-sizing: border-box;\"\u003eWhat's inside\u003c\/span\u003e\u003c\/h4\u003e\u003cp\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003eProbabilistic sketching data structures\u003cbr style=\"box-sizing: border-box;\"\u003eChoosing the right database engine\u003cbr style=\"box-sizing: border-box;\"\u003eDesigning efficient on-disk data structures and algorithms\u003cbr style=\"box-sizing: border-box;\"\u003eAlgorithmic tradeoffs in massive-scale systems\u003cbr style=\"box-sizing: border-box;\"\u003eComputing percentiles with limited space resources\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e \u003c\/p\u003e\u003ch4 style=\"box-sizing: border-box; padding: 0px 0px 4px; margin: 0px; text-rendering: optimizelegibility; font-weight: 700; font-size: 18px; line-height: 24px; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif\u003e\u003cspan style=\"box-sizing: border-box;\"\u003eAbout the reader\u003c\/span\u003e\u003c\/h4\u003e\u003cp\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003eExamples in Python, R, and pseudocode.\u003c\/span\u003e\u003c\/p\u003e\u003cp\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cstrong\u003e Table of Contents\u003c\/strong\u003e\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e \u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e1 Introduction\u003c\/strong\u003e\u003cbr\u003e\u003cstrong\u003ePART 1 HASH-BASED SKETCHES\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e2 Review of hash tables and modern hashing\u003cbr\u003e3 Approximate membership: Bloom and quotient filters\u003cbr\u003e4 Frequency estimation and count-min sketch\u003cbr\u003e5 Cardinality estimation and HyperLogLog\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003cstrong\u003ePART 2 REAL-TIME ANALYTICS\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e6 Streaming data: Bringing everything together\u003cbr\u003e7 Sampling from data streams\u003cbr\u003e8 Approximate quantiles on data streams\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003cstrong\u003ePART 3 DATA STRUCTURES FOR DATABASES AND EXTERNAL MEMORY ALGORITHMS\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e9 Introducing the external memory model\u003cbr\u003e10 Data structures for databases: B-trees, Bε-trees, and LSM-trees\u003cbr\u003e11 External memory sorting\u003c\/p\u003e\u003cp\u003e\u003cb\u003e\u003cbr\u003e\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eEbook License\u003c\/b\u003e\u003c\/p\u003e\u003cb\u003e\u003c\/b\u003e\u003cp\u003eEnd-User Warranty And License Agreement\u003c\/p\u003e\u003cp\u003e1. Grant Of License\u003c\/p\u003e\u003cp\u003eManning Has Authorized The Download By You Of An Unrestricted Number Of Copies Of The Electronic Book (Ebook) In Any Of The Available Formats. Manning Grants You A Nonexclusive, Nontransferable License To Use The Ebook According To The Terms And Conditions Herein. This License Agreement Permits You To Install The Ebook On Any And All Your Devices For Your Personal Use Only.\u003c\/p\u003e\u003cp\u003e2. Restrictions\u003c\/p\u003e\u003cp\u003eYou Shall Not: (1) Share, Resell, Rent, Assign, Timeshare, Distribute, Or Transfer All Or Part Of The Ebook Or Any Rights Granted Hereunder To Any Other Person; (2) Duplicate The Ebook, Except For A Single Backup Or Archival Copy; (3) Remove Any Proprietary Notices, Labels, Or Marks From The Ebook; (4) Transfer Or Sublicense Title To The Ebook To Any Other Party.\u003c\/p\u003e\u003cp\u003e3. Intellectual Property Protection\u003c\/p\u003e\u003cp\u003eThe Ebook Is Owned By Manning And Is Protected By United States And International Copyright And Other Intellectual Property Laws. Manning Reserves All Rights In The Ebook Not Expressly Granted Herein. This License And Your Right To Use The Ebook Terminate Automatically If You Violate Any Part Of This Agreement. In The Event Of Termination, You Must Remove The Original And Any Copies Of The Ebook From All Your Devices.\u003c\/p\u003e\u003cp\u003e4. Source Code Supplementary Material\u003c\/p\u003e\u003cp\u003eAny Source Code Files Provided As A Supplement To The Book Are Freely Available To The Public For Download. Reuse Of The Code Is Permitted, In Whole Or In Part, Including The Creation Of Derivative Works, Provided That You Acknowledge That You Are Using It And Identify The Source: Title, Publisher And Year.\u003c\/p\u003e\u003cp\u003e5. Limited Warranty\u003c\/p\u003e\u003cp\u003eManning Warrants That The Ebook Files, A Copy Of Which You Are Authorized To Download, Are Free From Defects In The Operational Sense That They Can Be Read By A Pdf Reader Or Epub Reader, Or Other. Except For This Express Limited Warranty, Manning Makes And You Receive No Warranties, Express, Implied, Statutory Or In Any Communication With You, And Manning Specifically Disclaims Any Other Warranty Including The Implied Warranty Of Merchantability Or Fitness Or A Particular Purpose. Manning Does Not Warrant That The Operation Of The Ebook Will Be Uninterrupted Or Error Free. If The Ebook Was Purchased In The United States, The Above Exclusions May Not Apply To You As Some States Do Not Allow The Exclusion Of Implied Warranties. In Addition To The Above Warranty Rights, You May Also Have Other Rights That Vary From State To State.\u003c\/p\u003e\u003cp\u003e6. Limitation Of Liability\u003c\/p\u003e\u003cp\u003eIn No Event Will Manning Be Liable For Any Damages, Whether Arising For Tort Or Contract, Including Loss Of Data, Lost Profits, Or Other Special, Incidental, Consequential, Or Indirect Damages Arising Out Of The Use Or Inability To Use The Ebook.\u003c\/p\u003e\u003cp\u003e7. General\u003c\/p\u003e\u003cp\u003eThis Agreement Constitutes The Entire Agreement Between You And Manning And Supersedes Any Prior Agreement Concerning The Ebook. This Agreement Is Governed By The Laws Of The State Of New York.\u003c\/p\u003e\u003cp\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003c\/span\u003e\u003c\/p\u003e","brand":"Manning Publications","offers":[{"title":"Paperback","offer_id":50506263494726,"sku":"9781617298035","price":5318.0,"currency_code":"INR","in_stock":true},{"title":"E-Book","offer_id":50506263527494,"sku":"9781617298035-2","price":5121.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0754\/2797\/1142\/files\/Medjedovic-Algorithms-HI.png?v=1790324561","url":"https:\/\/cb-india.com\/products\/algorithms-and-data-structures-for-massive-datasets","provider":"Computer Bookshop (India) Pvt Ltd","version":"1.0","type":"link"}