Practical Apache Spark

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Practical Apache Spark

Author : Subhashini Chellappan
Publisher : Apress
ISBN 13 : 1484236521
Total Pages : 288 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Practical Apache Spark by : Subhashini Chellappan

Download or read book Practical Apache Spark written by Subhashini Chellappan and published by Apress. This book was released on 2018-12-12 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: Work with Apache Spark using Scala to deploy and set up single-node, multi-node, and high-availability clusters. This book discusses various components of Spark such as Spark Core, DataFrames, Datasets and SQL, Spark Streaming, Spark MLib, and R on Spark with the help of practical code snippets for each topic. Practical Apache Spark also covers the integration of Apache Spark with Kafka with examples. You’ll follow a learn-to-do-by-yourself approach to learning – learn the concepts, practice the code snippets in Scala, and complete the assignments given to get an overall exposure. On completion, you’ll have knowledge of the functional programming aspects of Scala, and hands-on expertise in various Spark components. You’ll also become familiar with machine learning algorithms with real-time usage. What You Will LearnDiscover the functional programming features of Scala Understand the complete architecture of Spark and its componentsIntegrate Apache Spark with Hive and Kafka Use Spark SQL, DataFrames, and Datasets to process data using traditional SQL queries Work with different machine learning concepts and libraries using Spark's MLlib packages Who This Book Is For Developers and professionals who deal with batch and stream data processing.

Practical Machine Learning with Spark

Author : Gourav Gupta
Publisher : BPB Publications
ISBN 13 : 9391392083
Total Pages : 501 pages
Book Rating : 4.3/5 (913 download)

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Book Synopsis Practical Machine Learning with Spark by : Gourav Gupta

Download or read book Practical Machine Learning with Spark written by Gourav Gupta and published by BPB Publications. This book was released on 2022-04-28 with total page 501 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the cosmic secrets of Distributed Processing for Deep Learning applications KEY FEATURES ● In-depth practical demonstration of ML/DL concepts using Distributed Framework. ● Covers graphical illustrations and visual explanations for ML/DL pipelines. ● Includes live codebase for each of NLP, computer vision and machine learning applications. DESCRIPTION This book provides the reader with an up-to-date explanation of Machine Learning and an in-depth, comprehensive, and straightforward understanding of the architectural techniques used to evaluate and anticipate the futuristic insights of data using Apache Spark. The book walks readers by setting up Hadoop and Spark installations on-premises, Docker, and AWS. Readers will learn about Spark MLib and how to utilize it in supervised and unsupervised machine learning scenarios. With the help of Spark, some of the most prominent technologies, such as natural language processing and computer vision, are evaluated and demonstrated in a realistic setting. Using the capabilities of Apache Spark, this book discusses the fundamental components that underlie each of these natural language processing, computer vision, and machine learning technologies, as well as how you can incorporate these technologies into your business processes. Towards the end of the book, readers will learn about several deep learning frameworks, such as TensorFlow and PyTorch. Readers will also learn to execute distributed processing of deep learning problems using the Spark programming language WHAT YOU WILL LEARN ●Learn how to get started with machine learning projects using Spark. ● Witness how to use Spark MLib's design for machine learning and deep learning operations. ● Use Spark in tasks involving NLP, unsupervised learning, and computer vision. ● Experiment with Spark in a cloud environment and with AI pipeline workflows. ● Run deep learning applications on a distributed network. WHO THIS BOOK IS FOR This book is valuable for data engineers, machine learning engineers, data scientists, data architects, business analysts, and technical consultants worldwide. It would be beneficial to have some familiarity with the fundamentals of Hadoop and Python. TABLE OF CONTENTS 1. Introduction to Machine Learning 2. Apache Spark Environment Setup and Configuration 3. Apache Spark 4. Apache Spark MLlib 5. Supervised Learning with Spark 6. Un-Supervised Learning with Apache Spark 7. Natural Language Processing with Apache Spark 8. Recommendation Engine with Distributed Framework 9. Deep Learning with Spark 10. Computer Vision with Apache Spark

Practical Apache Spark

Author : Subhashini Chellappan
Publisher : Apress
ISBN 13 : 1484236521
Total Pages : 288 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Practical Apache Spark by : Subhashini Chellappan

Practical Data Science with Hadoop and Spark

Author : Ofer Mendelevitch
Publisher : Addison-Wesley Professional
ISBN 13 : 0134029720
Total Pages : 463 pages
Book Rating : 4.1/5 (34 download)

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Book Synopsis Practical Data Science with Hadoop and Spark by : Ofer Mendelevitch

Download or read book Practical Data Science with Hadoop and Spark written by Ofer Mendelevitch and published by Addison-Wesley Professional. This book was released on 2016-12-08 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Complete Guide to Data Science with Hadoop—For Technical Professionals, Businesspeople, and Students Demand is soaring for professionals who can solve real data science problems with Hadoop and Spark. Practical Data Science with Hadoop® and Spark is your complete guide to doing just that. Drawing on immense experience with Hadoop and big data, three leading experts bring together everything you need: high-level concepts, deep-dive techniques, real-world use cases, practical applications, and hands-on tutorials. The authors introduce the essentials of data science and the modern Hadoop ecosystem, explaining how Hadoop and Spark have evolved into an effective platform for solving data science problems at scale. In addition to comprehensive application coverage, the authors also provide useful guidance on the important steps of data ingestion, data munging, and visualization. Once the groundwork is in place, the authors focus on specific applications, including machine learning, predictive modeling for sentiment analysis, clustering for document analysis, anomaly detection, and natural language processing (NLP). This guide provides a strong technical foundation for those who want to do practical data science, and also presents business-driven guidance on how to apply Hadoop and Spark to optimize ROI of data science initiatives. Learn What data science is, how it has evolved, and how to plan a data science career How data volume, variety, and velocity shape data science use cases Hadoop and its ecosystem, including HDFS, MapReduce, YARN, and Spark Data importation with Hive and Spark Data quality, preprocessing, preparation, and modeling Visualization: surfacing insights from huge data sets Machine learning: classification, regression, clustering, and anomaly detection Algorithms and Hadoop tools for predictive modeling Cluster analysis and similarity functions Large-scale anomaly detection NLP: applying data science to human language

Apache Spark for Machine Learning

Author : Deepak Gowda
Publisher : Packt Publishing Ltd
ISBN 13 : 1835460011
Total Pages : 306 pages
Book Rating : 4.8/5 (354 download)

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Book Synopsis Apache Spark for Machine Learning by : Deepak Gowda

Download or read book Apache Spark for Machine Learning written by Deepak Gowda and published by Packt Publishing Ltd. This book was released on 2024-11-01 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: Develop your data science skills with Apache Spark to solve real-world problems for Fortune 500 companies using scalable algorithms on large cloud computing clusters Key Features Apply techniques to analyze big data and uncover valuable insights for machine learning Learn to use cloud computing clusters for training machine learning models on large datasets Discover practical strategies to overcome challenges in model training, deployment, and optimization Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionIn the world of big data, efficiently processing and analyzing massive datasets for machine learning can be a daunting task. Written by Deepak Gowda, a data scientist with over a decade of experience and 30+ patents, this book provides a hands-on guide to mastering Spark’s capabilities for efficient data processing, model building, and optimization. With Deepak’s expertise across industries such as supply chain, cybersecurity, and data center infrastructure, he makes complex concepts easy to follow through detailed recipes. This book takes you through core machine learning concepts, highlighting the advantages of Spark for big data analytics. It covers practical data preprocessing techniques, including feature extraction and transformation, supervised learning methods with detailed chapters on regression and classification, and unsupervised learning through clustering and recommendation systems. You’ll also learn to identify frequent patterns in data and discover effective strategies to deploy and optimize your machine learning models. Each chapter features practical coding examples and real-world applications to equip you with the knowledge and skills needed to tackle complex machine learning tasks. By the end of this book, you’ll be ready to handle big data and create advanced machine learning models with Apache Spark.What you will learn Master Apache Spark for efficient, large-scale data processing and analysis Understand core machine learning concepts and their applications with Spark Implement data preprocessing techniques for feature extraction and transformation Explore supervised learning methods – regression and classification algorithms Apply unsupervised learning for clustering tasks and recommendation systems Discover frequent pattern mining techniques to uncover data trends Who this book is for This book is ideal for data scientists, ML engineers, data engineers, students, and researchers who want to deepen their knowledge of Apache Spark’s tools and algorithms. It’s a must-have for those struggling to scale models for real-world problems and a valuable resource for preparing for interviews at Fortune 500 companies, focusing on large dataset analysis, model training, and deployment.

Practical Machine Learning on Databricks

Author : Debu Sinha
Publisher : Packt Publishing Ltd
ISBN 13 : 1801818290
Total Pages : 244 pages
Book Rating : 4.8/5 (18 download)

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Book Synopsis Practical Machine Learning on Databricks by : Debu Sinha

Download or read book Practical Machine Learning on Databricks written by Debu Sinha and published by Packt Publishing Ltd. This book was released on 2023-11-24 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take your machine learning skills to the next level by mastering databricks and building robust ML pipeline solutions for future ML innovations Key Features Learn to build robust ML pipeline solutions for databricks transition Master commonly available features like AutoML and MLflow Leverage data governance and model deployment using MLflow model registry Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionUnleash the potential of databricks for end-to-end machine learning with this comprehensive guide, tailored for experienced data scientists and developers transitioning from DIY or other cloud platforms. Building on a strong foundation in Python, Practical Machine Learning on Databricks serves as your roadmap from development to production, covering all intermediary steps using the databricks platform. You’ll start with an overview of machine learning applications, databricks platform features, and MLflow. Next, you’ll dive into data preparation, model selection, and training essentials and discover the power of databricks feature store for precomputing feature tables. You’ll also learn to kickstart your projects using databricks AutoML and automate retraining and deployment through databricks workflows. By the end of this book, you’ll have mastered MLflow for experiment tracking, collaboration, and advanced use cases like model interpretability and governance. The book is enriched with hands-on example code at every step. While primarily focused on generally available features, the book equips you to easily adapt to future innovations in machine learning, databricks, and MLflow.What you will learn Transition smoothly from DIY setups to databricks Master AutoML for quick ML experiment setup Automate model retraining and deployment Leverage databricks feature store for data prep Use MLflow for effective experiment tracking Gain practical insights for scalable ML solutions Find out how to handle model drifts in production environments Who this book is forThis book is for experienced data scientists, engineers, and developers proficient in Python, statistics, and ML lifecycle looking to transition to databricks from DIY clouds. Introductory Spark knowledge is a must to make the most out of this book, however, end-to-end ML workflows will be covered. If you aim to accelerate your machine learning workflows and deploy scalable, robust solutions, this book is an indispensable resource.

MongoDB Recipes

Author : Subhashini Chellappan
Publisher : Apress
ISBN 13 : 1484248910
Total Pages : 257 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis MongoDB Recipes by : Subhashini Chellappan

Download or read book MongoDB Recipes written by Subhashini Chellappan and published by Apress. This book was released on 2019-12-13 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get the most out of MongoDB using a problem-solution approach. This book starts with recipes on the MongoDB query language, including how to query various data structures stored within documents. These self-contained code examples allow you to solve your MongoDB problems without fuss. MongoDB Recipes describes how to use advanced querying in MongoDB, such as indexing and the aggregation framework. It demonstrates how to use the Compass function, a GUI client interacting with MongoDB, and how to apply data modeling to your MongoDB application. You’ll see recipes on the latest features of MongoDB 4 allowing you to manage data in an efficient manner using MongoDB. What You Will Learn Work with the MongoDB document model Design MongoDB schemas Use the MongoDB query language Harness the aggregation framework Create replica sets and sharding in MongoDB Who This Book Is ForDevelopers and professionals who work with MongoDB.

Data Science in Practice

Author : Alan Said
Publisher : Springer
ISBN 13 : 3319975560
Total Pages : 199 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Data Science in Practice by : Alan Said

Download or read book Data Science in Practice written by Alan Said and published by Springer. This book was released on 2018-09-19 with total page 199 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book approaches big data, artificial intelligence, machine learning, and business intelligence through the lens of Data Science. We have grown accustomed to seeing these terms mentioned time and time again in the mainstream media. However, our understanding of what they actually mean often remains limited. This book provides a general overview of the terms and approaches used broadly in data science, and provides detailed information on the underlying theories, models, and application scenarios. Divided into three main parts, it addresses what data science is; how and where it is used; and how it can be implemented using modern open source software. The book offers an essential guide to modern data science for all students, practitioners, developers and managers seeking a deeper understanding of how various aspects of data science work, and of how they can be employed to gain a competitive advantage.

Apache Spark 2: Data Processing and Real-Time Analytics

Author : Romeo Kienzler
Publisher : Packt Publishing Ltd
ISBN 13 : 1789959918
Total Pages : 604 pages
Book Rating : 4.7/5 (899 download)

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Book Synopsis Apache Spark 2: Data Processing and Real-Time Analytics by : Romeo Kienzler

Download or read book Apache Spark 2: Data Processing and Real-Time Analytics written by Romeo Kienzler and published by Packt Publishing Ltd. This book was released on 2018-12-21 with total page 604 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build efficient data flow and machine learning programs with this flexible, multi-functional open-source cluster-computing framework Key FeaturesMaster the art of real-time big data processing and machine learning Explore a wide range of use-cases to analyze large data Discover ways to optimize your work by using many features of Spark 2.x and ScalaBook Description Apache Spark is an in-memory, cluster-based data processing system that provides a wide range of functionalities such as big data processing, analytics, machine learning, and more. With this Learning Path, you can take your knowledge of Apache Spark to the next level by learning how to expand Spark's functionality and building your own data flow and machine learning programs on this platform. You will work with the different modules in Apache Spark, such as interactive querying with Spark SQL, using DataFrames and datasets, implementing streaming analytics with Spark Streaming, and applying machine learning and deep learning techniques on Spark using MLlib and various external tools. By the end of this elaborately designed Learning Path, you will have all the knowledge you need to master Apache Spark, and build your own big data processing and analytics pipeline quickly and without any hassle. This Learning Path includes content from the following Packt products: Mastering Apache Spark 2.x by Romeo KienzlerScala and Spark for Big Data Analytics by Md. Rezaul Karim, Sridhar AllaApache Spark 2.x Machine Learning Cookbook by Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen MeiCookbookWhat you will learnGet to grips with all the features of Apache Spark 2.xPerform highly optimized real-time big data processing Use ML and DL techniques with Spark MLlib and third-party toolsAnalyze structured and unstructured data using SparkSQL and GraphXUnderstand tuning, debugging, and monitoring of big data applications Build scalable and fault-tolerant streaming applications Develop scalable recommendation enginesWho this book is for If you are an intermediate-level Spark developer looking to master the advanced capabilities and use-cases of Apache Spark 2.x, this Learning Path is ideal for you. Big data professionals who want to learn how to integrate and use the features of Apache Spark and build a strong big data pipeline will also find this Learning Path useful. To grasp the concepts explained in this Learning Path, you must know the fundamentals of Apache Spark and Scala.

Real-Time Big Data Analytics

Author : Sumit Gupta
Publisher : Packt Publishing Ltd
ISBN 13 : 1784397407
Total Pages : 326 pages
Book Rating : 4.7/5 (843 download)

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Book Synopsis Real-Time Big Data Analytics by : Sumit Gupta

Download or read book Real-Time Big Data Analytics written by Sumit Gupta and published by Packt Publishing Ltd. This book was released on 2016-02-26 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: Design, process, and analyze large sets of complex data in real time About This Book Get acquainted with transformations and database-level interactions, and ensure the reliability of messages processed using Storm Implement strategies to solve the challenges of real-time data processing Load datasets, build queries, and make recommendations using Spark SQL Who This Book Is For If you are a Big Data architect, developer, or a programmer who wants to develop applications/frameworks to implement real-time analytics using open source technologies, then this book is for you. What You Will Learn Explore big data technologies and frameworks Work through practical challenges and use cases of real-time analytics versus batch analytics Develop real-word use cases for processing and analyzing data in real-time using the programming paradigm of Apache Storm Handle and process real-time transactional data Optimize and tune Apache Storm for varied workloads and production deployments Process and stream data with Amazon Kinesis and Elastic MapReduce Perform interactive and exploratory data analytics using Spark SQL Develop common enterprise architectures/applications for real-time and batch analytics In Detail Enterprise has been striving hard to deal with the challenges of data arriving in real time or near real time. Although there are technologies such as Storm and Spark (and many more) that solve the challenges of real-time data, using the appropriate technology/framework for the right business use case is the key to success. This book provides you with the skills required to quickly design, implement and deploy your real-time analytics using real-world examples of big data use cases. From the beginning of the book, we will cover the basics of varied real-time data processing frameworks and technologies. We will discuss and explain the differences between batch and real-time processing in detail, and will also explore the techniques and programming concepts using Apache Storm. Moving on, we'll familiarize you with “Amazon Kinesis” for real-time data processing on cloud. We will further develop your understanding of real-time analytics through a comprehensive review of Apache Spark along with the high-level architecture and the building blocks of a Spark program. You will learn how to transform your data, get an output from transformations, and persist your results using Spark RDDs, using an interface called Spark SQL to work with Spark. At the end of this book, we will introduce Spark Streaming, the streaming library of Spark, and will walk you through the emerging Lambda Architecture (LA), which provides a hybrid platform for big data processing by combining real-time and precomputed batch data to provide a near real-time view of incoming data. Style and approach This step-by-step is an easy-to-follow, detailed tutorial, filled with practical examples of basic and advanced features. Each topic is explained sequentially and supported by real-world examples and executable code snippets.

DataBricks® PySpark 2.x Certification Practice Questions

Author :
Publisher : HadoopExam Learning Resources
ISBN 13 :
Total Pages : 183 pages
Book Rating : 4./5 ( download)

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Book Synopsis DataBricks® PySpark 2.x Certification Practice Questions by :

Download or read book DataBricks® PySpark 2.x Certification Practice Questions written by and published by HadoopExam Learning Resources. This book was released on with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains the questions answers and some FAQ about the Databricks Spark Certification for version 2.x, which is the latest release from Apache Spark. In this book we will be having in total 75 practice questions. Almost all required question would have in detail explanation to the questions and answers, wherever required. Don’t consider this book as a guide, it is more of question and answer practice book. This book also give some references as well like how to prepare further to ensure that you clear the certification exam. This book will particularly focus on the Python version of the certification preparation material. Please note these are practice questions and not dumps, hence just memorizing the question and answers will not help in the real exam. You need to understand the concepts in detail as well as you should be able to solve the programming questions at the end in real worlds work you should be able to write code using PySpark whether you are Data Engineer, Data Analytics Engineer, Data Scientists or Programmer. Hence, take the opportunity to learn each question and also go through the explanation of the questions.

Mastering Apache Spark 2.x

Author : Romeo Kienzler
Publisher : Packt Publishing Ltd
ISBN 13 : 178528522X
Total Pages : 345 pages
Book Rating : 4.7/5 (852 download)

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Book Synopsis Mastering Apache Spark 2.x by : Romeo Kienzler

Download or read book Mastering Apache Spark 2.x written by Romeo Kienzler and published by Packt Publishing Ltd. This book was released on 2017-07-26 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced analytics on your Big Data with latest Apache Spark 2.x About This Book An advanced guide with a combination of instructions and practical examples to extend the most up-to date Spark functionalities. Extend your data processing capabilities to process huge chunk of data in minimum time using advanced concepts in Spark. Master the art of real-time processing with the help of Apache Spark 2.x Who This Book Is For If you are a developer with some experience with Spark and want to strengthen your knowledge of how to get around in the world of Spark, then this book is ideal for you. Basic knowledge of Linux, Hadoop and Spark is assumed. Reasonable knowledge of Scala is expected. What You Will Learn Examine Advanced Machine Learning and DeepLearning with MLlib, SparkML, SystemML, H2O and DeepLearning4J Study highly optimised unified batch and real-time data processing using SparkSQL and Structured Streaming Evaluate large-scale Graph Processing and Analysis using GraphX and GraphFrames Apply Apache Spark in Elastic deployments using Jupyter and Zeppelin Notebooks, Docker, Kubernetes and the IBM Cloud Understand internal details of cost based optimizers used in Catalyst, SystemML and GraphFrames Learn how specific parameter settings affect overall performance of an Apache Spark cluster Leverage Scala, R and python for your data science projects In Detail Apache Spark is an in-memory cluster-based parallel processing system that provides a wide range of functionalities such as graph processing, machine learning, stream processing, and SQL. This book aims to take your knowledge of Spark to the next level by teaching you how to expand Spark's functionality and implement your data flows and machine/deep learning programs on top of the platform. The book commences with an overview of the Spark ecosystem. It will introduce you to Project Tungsten and Catalyst, two of the major advancements of Apache Spark 2.x. You will understand how memory management and binary processing, cache-aware computation, and code generation are used to speed things up dramatically. The book extends to show how to incorporate H20, SystemML, and Deeplearning4j for machine learning, and Jupyter Notebooks and Kubernetes/Docker for cloud-based Spark. During the course of the book, you will learn about the latest enhancements to Apache Spark 2.x, such as interactive querying of live data and unifying DataFrames and Datasets. You will also learn about the updates on the APIs and how DataFrames and Datasets affect SQL, machine learning, graph processing, and streaming. You will learn to use Spark as a big data operating system, understand how to implement advanced analytics on the new APIs, and explore how easy it is to use Spark in day-to-day tasks. Style and approach This book is an extensive guide to Apache Spark modules and tools and shows how Spark's functionality can be extended for real-time processing and storage with worked examples.

Hadoop in Practice

Author : Alex Holmes
Publisher : Simon and Schuster
ISBN 13 : 1638353360
Total Pages : 758 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis Hadoop in Practice by : Alex Holmes

Download or read book Hadoop in Practice written by Alex Holmes and published by Simon and Schuster. This book was released on 2014-09-29 with total page 758 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Hadoop in Practice, Second Edition provides over 100 tested, instantly useful techniques that will help you conquer big data, using Hadoop. This revised new edition covers changes and new features in the Hadoop core architecture, including MapReduce 2. Brand new chapters cover YARN and integrating Kafka, Impala, and Spark SQL with Hadoop. You'll also get new and updated techniques for Flume, Sqoop, and Mahout, all of which have seen major new versions recently. In short, this is the most practical, up-to-date coverage of Hadoop available anywhere. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Book It's always a good time to upgrade your Hadoop skills! Hadoop in Practice, Second Edition provides a collection of 104 tested, instantly useful techniques for analyzing real-time streams, moving data securely, machine learning, managing large-scale clusters, and taming big data using Hadoop. This completely revised edition covers changes and new features in Hadoop core, including MapReduce 2 and YARN. You'll pick up hands-on best practices for integrating Spark, Kafka, and Impala with Hadoop, and get new and updated techniques for the latest versions of Flume, Sqoop, and Mahout. In short, this is the most practical, up-to-date coverage of Hadoop available. Readers need to know a programming language like Java and have basic familiarity with Hadoop. What's Inside Thoroughly updated for Hadoop 2 How to write YARN applications Integrate real-time technologies like Storm, Impala, and Spark Predictive analytics using Mahout and RR Readers need to know a programming language like Java and have basic familiarity with Hadoop. About the Author Alex Holmes works on tough big-data problems. He is a software engineer, author, speaker, and blogger specializing in large-scale Hadoop projects. Table of Contents PART 1 BACKGROUND AND FUNDAMENTALS Hadoop in a heartbeat Introduction to YARN PART 2 DATA LOGISTICS Data serialization—working with text and beyond Organizing and optimizing data in HDFS Moving data into and out of Hadoop PART 3 BIG DATA PATTERNS Applying MapReduce patterns to big data Utilizing data structures and algorithms at scale Tuning, debugging, and testing PART 4 BEYOND MAPREDUCE SQL on Hadoop Writing a YARN application

A Practical Guide to Artificial Intelligence and Data Analytics

Author : Rayan Wali
Publisher : Rayan Wali
ISBN 13 :
Total Pages : 605 pages
Book Rating : 4./5 ( download)

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Book Synopsis A Practical Guide to Artificial Intelligence and Data Analytics by : Rayan Wali

Download or read book A Practical Guide to Artificial Intelligence and Data Analytics written by Rayan Wali and published by Rayan Wali. This book was released on 2021-06-12 with total page 605 pages. Available in PDF, EPUB and Kindle. Book excerpt: Whether you are looking to prepare for AI/ML/Data Science job interviews or you are a beginner in the field of Data Science and AI, this book is designed for engineers and AI enthusiasts like you at all skill levels. Taking a different approach from a traditional textbook style of instruction, A Practical Guide to AI and Data Analytics touches on all of the fundamental topics you will need to understand deeper into machine learning and artificial intelligence research, literature, and practical applications with its four parts: Part I: Concept Instruction Part II: 8 Full-Length Case Studies Part III: 50+ Mixed Exercises Part IV: A Full-Length Assessment With an illustrative approach to instruction, worked examples, and case studies, this easy-to-understand book simplifies many of the AI and Data Analytics key concepts, leading to an improvement of AI/ML system design skills.

The Definitive Guide to Data Integration

Author : Pierre-Yves BONNEFOY
Publisher : Packt Publishing Ltd
ISBN 13 : 1837634777
Total Pages : 490 pages
Book Rating : 4.8/5 (376 download)

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Book Synopsis The Definitive Guide to Data Integration by : Pierre-Yves BONNEFOY

Download or read book The Definitive Guide to Data Integration written by Pierre-Yves BONNEFOY and published by Packt Publishing Ltd. This book was released on 2024-03-29 with total page 490 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn the essentials of data integration with this comprehensive guide, covering everything from sources to solutions, and discover the key to making the most of your data stack Key Features Learn how to leverage modern data stack tools and technologies for effective data integration Design and implement data integration solutions with practical advice and best practices Focus on modern technologies such as cloud-based architectures, real-time data processing, and open-source tools and technologies Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe Definitive Guide to Data Integration is an indispensable resource for navigating the complexities of modern data integration. Focusing on the latest tools, techniques, and best practices, this guide helps you master data integration and unleash the full potential of your data. This comprehensive guide begins by examining the challenges and key concepts of data integration, such as managing huge volumes of data and dealing with the different data types. You’ll gain a deep understanding of the modern data stack and its architecture, as well as the pivotal role of open-source technologies in shaping the data landscape. Delving into the layers of the modern data stack, you’ll cover data sources, types, storage, integration techniques, transformation, and processing. The book also offers insights into data exposition and APIs, ingestion and storage strategies, data preparation and analysis, workflow management, monitoring, data quality, and governance. Packed with practical use cases, real-world examples, and a glimpse into the future of data integration, The Definitive Guide to Data Integration is an essential resource for data eclectics. By the end of this book, you’ll have the gained the knowledge and skills needed to optimize your data usage and excel in the ever-evolving world of data.What you will learn Discover the evolving architecture and technologies shaping data integration Process large data volumes efficiently with data warehousing Tackle the complexities of integrating large datasets from diverse sources Harness the power of data warehousing for efficient data storage and processing Design and optimize effective data integration solutions Explore data governance principles and compliance requirements Who this book is for This book is perfect for data engineers, data architects, data analysts, and IT professionals looking to gain a comprehensive understanding of data integration in the modern era. Whether you’re a beginner or an experienced professional enhancing your knowledge of the modern data stack, this definitive guide will help you navigate the data integration landscape.

Practical Weak Supervision

Author : Wee Hyong Tok
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492077038
Total Pages : 193 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Practical Weak Supervision by : Wee Hyong Tok

Download or read book Practical Weak Supervision written by Wee Hyong Tok and published by "O'Reilly Media, Inc.". This book was released on 2021-09-30 with total page 193 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most data scientists and engineers today rely on quality labeled data to train machine learning models. But building a training set manually is time-consuming and expensive, leaving many companies with unfinished ML projects. There's a more practical approach. In this book, Wee Hyong Tok, Amit Bahree, and Senja Filipi show you how to create products using weakly supervised learning models. You'll learn how to build natural language processing and computer vision projects using weakly labeled datasets from Snorkel, a spin-off from the Stanford AI Lab. Because so many companies have pursued ML projects that never go beyond their labs, this book also provides a guide on how to ship the deep learning models you build. Get up to speed on the field of weak supervision, including ways to use it as part of the data science process Use Snorkel AI for weak supervision and data programming Get code examples for using Snorkel to label text and image datasets Use a weakly labeled dataset for text and image classification Learn practical considerations for using Snorkel with large datasets and using Spark clusters to scale labeling

Computational Life Sciences

Author : Jens Dörpinghaus
Publisher : Springer Nature
ISBN 13 : 303108411X
Total Pages : 593 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Computational Life Sciences by : Jens Dörpinghaus

Download or read book Computational Life Sciences written by Jens Dörpinghaus and published by Springer Nature. This book was released on 2023-03-04 with total page 593 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book broadly covers the given spectrum of disciplines in Computational Life Sciences, transforming it into a strong helping hand for teachers, students, practitioners and researchers. In Life Sciences, problem-solving and data analysis often depend on biological expertise combined with technical skills in order to generate, manage and efficiently analyse big data. These technical skills can easily be enhanced by good theoretical foundations, developed from well-chosen practical examples and inspiring new strategies. This is the innovative approach of Computational Life Sciences-Data Engineering and Data Mining for Life Sciences: We present basic concepts, advanced topics and emerging technologies, introduce algorithm design and programming principles, address data mining and knowledge discovery as well as applications arising from real projects. Chapters are largely independent and often flanked by illustrative examples and practical advise.