{"id":8334,"date":"2026-08-10T09:54:46","date_gmt":"2026-08-10T09:54:46","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/oreilly-algorithms-and-data-structures-for-massive-datasets-video-edition-2022-10\/"},"modified":"2026-08-10T09:54:46","modified_gmt":"2026-08-10T09:54:46","slug":"oreilly-algorithms-and-data-structures-for-massive-datasets-video-edition-2022-10","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/oreilly-algorithms-and-data-structures-for-massive-datasets-video-edition-2022-10\/","title":{"rendered":"Oreilly \u2013 Algorithms and Data Structures for Massive Datasets, Video Edition 2022-10"},"content":{"rendered":"<div class=\"w-post-elm post_content\">\n<h2 style=\"text-align: left\"><span style=\"vertical-align: inherit\">Description<\/span><\/h2>\n<div class=\"presented-response-container ng-tns-c357060534-66\" style=\"text-align: left\">\n<div class=\"response-container-content ng-tns-c357060534-66\">\n<div class=\"response-content ng-tns-c357060534-66\">\n<div class=\"markdown markdown-main-panel\">\n<p data-sourcepos=\"5:1-5:222\"><span style=\"vertical-align: inherit\">Algorithms and Data Structures for Massive Datasets, Video Edition. Today\u2019s massive data sets challenge traditional data structures and algorithms. This engaging, practical guide introduces new techniques that can reliably handle even the largest distributed data sets. This course will help you become familiar with modern techniques for managing and analyzing very large data sets. By learning this course, you will be able to design and implement high-performance, scalable systems for processing large data sets. This course explains complex concepts in a simple way with practical, real-world examples.<\/span><\/p>\n<h3 data-sourcepos=\"7:1-7:22\"><span style=\"vertical-align: inherit\">What you will learn:<\/span><\/h3>\n<ul>\n<li data-sourcepos=\"11:1-11:57\"><span style=\"vertical-align: inherit\">Probabilistic Sketch Data Structures: For Solving Practical Problems<\/span><\/li>\n<li data-sourcepos=\"12:1-12:46\"><span style=\"vertical-align: inherit\">Choosing the right database: for your application<\/span><\/li>\n<li data-sourcepos=\"13:1-13:70\"><span style=\"vertical-align: inherit\">Evaluate and design efficient data structures and algorithms on disk<\/span><\/li>\n<li data-sourcepos=\"14:1-14:52\"><span style=\"vertical-align: inherit\">Understanding algorithmic trade-offs: in large-scale systems<\/span><\/li>\n<li data-sourcepos=\"15:1-15:40\"><span style=\"vertical-align: inherit\">Calculating basic statistics: from current data<\/span><\/li>\n<li data-sourcepos=\"16:1-16:39\"><span style=\"vertical-align: inherit\">Correct sampling: from current data<\/span><\/li>\n<li data-sourcepos=\"17:1-18:0\"><span style=\"vertical-align: inherit\">Calculating percentiles: with limited space resources<\/span><\/li>\n<\/ul>\n<h3 data-sourcepos=\"17:1-17:32\"><span style=\"vertical-align: inherit\">Who is this course suitable for?<\/span><\/h3>\n<ul>\n<li data-sourcepos=\"20:1-20:47\"><span style=\"vertical-align: inherit\">They face the challenges of managing big data.<\/span><\/li>\n<li data-sourcepos=\"21:1-21:68\"><span style=\"vertical-align: inherit\">They are looking for solutions to improve the efficiency of data processing systems.<\/span><\/li>\n<li data-sourcepos=\"22:1-22:59\"><span style=\"vertical-align: inherit\">They want to use modern techniques for data analysis.<\/span><\/li>\n<li data-sourcepos=\"23:1-24:0\"><span style=\"vertical-align: inherit\">They are interested in understanding the principles of how systems like Google and Facebook work.<\/span><\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<h3 style=\"text-align: left\"><span style=\"vertical-align: inherit\">Course details<\/span><\/h3>\n<ul style=\"text-align: left\">\n<li><span style=\"vertical-align: inherit\">Publisher: <\/span><a href=\"https:\/\/href.li\/?https:\/\/www.oreilly.com\/library\/view\/algorithms-and-data\/9781617298035VE\/\" target=\"_blank\" rel=\"noopener\"><span style=\"vertical-align: inherit\">Oreilly<\/span><\/a><\/li>\n<li><span style=\"vertical-align: inherit\">Instructor: <\/span><a class=\"author-name\" href=\"https:\/\/downloadlynet.ir\/tag\/emin-tahirovic\/\"><span style=\"vertical-align: inherit\">Emin Tahirovic<\/span><\/a><span style=\"vertical-align: inherit\"> ,&nbsp; <\/span><a class=\"author-name\" href=\"https:\/\/downloadlynet.ir\/tag\/dzejla-medjedovic\/\"><span style=\"vertical-align: inherit\">Dzejla Medjedovic<\/span><\/a><span style=\"vertical-align: inherit\"> ,&nbsp; <\/span><a class=\"author-name\" href=\"https:\/\/downloadlynet.ir\/tag\/ines-dedovic\/\"><span style=\"vertical-align: inherit\">Ines Dedovic<\/span><\/a><\/li>\n<li><span style=\"vertical-align: inherit\">Training level: Beginner to advanced<\/span><\/li>\n<li><span style=\"vertical-align: inherit\">Training duration: 9 hours and 53 minutes<\/span><\/li>\n<\/ul>\n<h3 style=\"text-align: left\"><span style=\"vertical-align: inherit\">Course topics<\/span><\/h3>\n<div class=\"ud-block-list-item ud-block-list-item-small ud-block-list-item-tight ud-block-list-item-neutral ud-text-sm\">\n<ul style=\"text-align: left\">\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. Introduction<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. An example: How to solve it<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. How to solve it, take two: A book walkthrough<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. The structure of this book<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. Latency vs. bandwidth<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Part 1. Hash-based sketches<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. Review of hash tables and modern hashing<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. Usage scenarios in modern systems<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. Collision resolution: Theory vs. practice<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. Hash tables for distributed systems: Consistent hashing<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. Adding a new node\/resource<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. Approximate membership: Bloom and quotient filters<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. A simple implementation<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. A bit of theory<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. Bloom filter adaptations and alternatives<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. Understanding metadata bits<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. Python code for lookup<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. Comparison between Bloom filters and quotient filters<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 4. Frequency estimation and count-min sketch<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 4. Update<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 4. Error vs. space in count-min sketch<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 4. Range queries with count-min sketch<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. Cardinality estimation and HyperLogLog<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. HyperLogLog incremental design<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. LogLog<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. Use case: Catching worms with HLL<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. The effect of the number of buckets (m)<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Part 2. Real-time analytics<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Streaming data: Bringing everything together<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Streaming data system: A meta example<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Deduplication<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Practical constraints and concepts in data streams<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Math bit: Sampling and estimation<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Biased sampling strategy<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. Sampling from data streams<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. Reservoir sampling<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. Biased reservoir sampling<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. Sampling from a sliding window<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. Priority sampling<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. Sampling algorithms comparison<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Approximate quantiles on data streams<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Approximate quantiles<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. T-digest: How it works<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Scale functions<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Merging t-digests<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Q-digest<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Quantile queries with q-digests<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Part 3. Data structures for databases and external memory algorithms<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 9. Introducing the external memory model<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 9. Example 1: Finding a minimum<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 9. Example 2: Binary search<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 9. Optimal searching<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 9. External memory model: Simple or simplistic?<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. Data structures for databases: B-trees, B\u03b5-trees, and LSM-trees<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. Data structures in this chapter<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. B-tree balancing<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. Delete<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. Math bit: Why are B-tree lookups optimal in external memory?<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. B\u03b5-trees<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. Lookups<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. Log-structured merge-trees (LSM-trees)<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. LSM-tree cost analysis<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 11. External memory sorting<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 11. Challenges of sorting in external memory: An example<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 11. External memory merge-sort (M\/B-way merge-sort)<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 11. What about external quick-sort?<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 11. Finding good enough pivots<\/span><\/li>\n<\/ul>\n<h3 style=\"text-align: left\"><span style=\"vertical-align: inherit\">Pictures from the course Algorithms and Data Structures for Massive Datasets, Video Edition<\/span><\/h3>\n<p style=\"text-align: left\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-960019 size-full\" src=\"https:\/\/downloadly.ir\/wp-content\/uploads\/2025\/01\/Algorithms-and-Data-Structures-for-Massive-Datasets-Video-Edition.png\" alt=\"Algorithms and Data Structures for Massive Datasets, Video Edition\" width=\"1483\" height=\"500\"><\/p>\n<h3 style=\"text-align: left\"><span style=\"vertical-align: inherit\">Sample course video<\/span><\/h3>\n<div style=\"width: 640px;\" class=\"wp-video\"><span class=\"mejs-offscreen\">Video Player<\/span><\/p>\n<div id=\"mep_0\" class=\"mejs-container mejs-container-keyboard-inactive wp-video-shortcode mejs-video\" tabindex=\"0\" role=\"application\" aria-label=\"Video Player\" style=\"width: 640px; height: 360px; 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Today\u2019s massive data sets challenge traditional data structures and algorithms. <\/p>\n","protected":false},"author":1,"template":"","dgi_category":[10458],"dgi_tag":[73966,73967,73968,73964,73969,73970,73971,73965],"class_list":["post-8334","digital_item","type-digital_item","status-publish","has-post-thumbnail","hentry","dgi_category-video-tutorials","dgi_tag-course-algorithms-and-data-structures-for-massive-datasets-video-edition","dgi_tag-download-algorithms-and-data-structures-for-massive-datasets-video-edition","dgi_tag-download-course-algorithms-and-data-structures-for-massive-datasets-video-edition","dgi_tag-dzejla-medjedovic","dgi_tag-emin-tahirovic","dgi_tag-free-algorithms-and-data-structures-for-massive-datasets-video-edition","dgi_tag-free-download-algorithms-and-data-structures-for-massive-datasets-video-edition","dgi_tag-ines-dedovic"],"_links":{"self":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/digital_item\/8334","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/digital_item"}],"about":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/types\/digital_item"}],"author":[{"embeddable":true,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"version-history":[{"count":0,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/digital_item\/8334\/revisions"}],"wp:attachment":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/media?parent=8334"}],"wp:term":[{"taxonomy":"dgi_category","embeddable":true,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/dgi_category?post=8334"},{"taxonomy":"dgi_tag","embeddable":true,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/dgi_tag?post=8334"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}