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What you would learn in Algorithms and Data Structures for Massive Datasets, Video Edition course?
Modern-day massive datasets create data structures that are traditional and algorithms slow to a crawl. This informative and entertaining guide outlines cutting-edge methods capable of handling the biggest data sets. Within algorithms and data Structures to prepare Massive Datasets, you will be taught:
Probabilistic sketching of data structures to solve practical issues
The right database engine to run your application
Evaluation and design of efficient data structures for disks and algorithms
Knowing the trade-offs between algorithms in large-scale systems
Basic statistics derived from streaming data
Correctly sampling stream data
Calculating percentiles in a limited amount of resources in space
algorithm and structures of data designed for Massive Datasets reveal a toolbox of novel techniques that are ideal for the latest big data applications. It will provide you with the latest algorithms and data structures that are at the heart of Google, Facebook, and other enterprise apps that work with massive quantities of data. These techniques are ably applied to every field including finance, and text analytics. Practical examples from the industry make complicated ideas feasible to incorporate into your projects. There are no mathematical proofs to debunk. Read this guide for the first time, and you'll be able to find the perfect spot to save space, without sacrificing quality.
The standard algorithms as well as data structure can get sluggish or fail entirely when applied to large-scale data sets. Selecting algorithms that are designed to handle large data can speed up processing, improve precision, and decreases the cost of processing. This book combines the latest research findings into practical strategies to sketch, stream, and organize huge datasets on disk as well as within the cloud.
About The Book
Algorithms and Data structures to Massive Datasets introduce processing and analytics methods for large-scale distributed data. Filled with stories from the industry, this easy guide makes even the most complex concepts easy to grasp. The book will provide real-world examples as you master mapping the most powerful algorithms such as Bloom filters and Count-min sketches, HyperLogLog, and LSM trees to your own applications.
Probabilistic sketching data structures
The right database engine to choose
Constructing effective on-disk structure and algorithm for data
Algorithmic trade-offs in large-scale systems
Calculating percentiles in a limited amount of space resources
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