Essential Pyspark For Scalable Data Analytics
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Essential Pyspark For Scalable Data Analytics
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Author : Sreeram Nudurupati
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-10-29
Essential Pyspark For Scalable Data Analytics written by Sreeram Nudurupati and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-29 with Computers categories.
Get started with distributed computing using PySpark, a single unified framework to solve end-to-end data analytics at scale Key FeaturesDiscover how to convert huge amounts of raw data into meaningful and actionable insightsUse Spark's unified analytics engine for end-to-end analytics, from data preparation to predictive analyticsPerform data ingestion, cleansing, and integration for ML, data analytics, and data visualizationBook Description Apache Spark is a unified data analytics engine designed to process huge volumes of data quickly and efficiently. PySpark is Apache Spark's Python language API, which offers Python developers an easy-to-use scalable data analytics framework. Essential PySpark for Scalable Data Analytics starts by exploring the distributed computing paradigm and provides a high-level overview of Apache Spark. You'll begin your analytics journey with the data engineering process, learning how to perform data ingestion, cleansing, and integration at scale. This book helps you build real-time analytics pipelines that help you gain insights faster. You'll then discover methods for building cloud-based data lakes, and explore Delta Lake, which brings reliability to data lakes. The book also covers Data Lakehouse, an emerging paradigm, which combines the structure and performance of a data warehouse with the scalability of cloud-based data lakes. Later, you'll perform scalable data science and machine learning tasks using PySpark, such as data preparation, feature engineering, and model training and productionization. Finally, you'll learn ways to scale out standard Python ML libraries along with a new pandas API on top of PySpark called Koalas. By the end of this PySpark book, you'll be able to harness the power of PySpark to solve business problems. What you will learnUnderstand the role of distributed computing in the world of big dataGain an appreciation for Apache Spark as the de facto go-to for big data processingScale out your data analytics process using Apache SparkBuild data pipelines using data lakes, and perform data visualization with PySpark and Spark SQLLeverage the cloud to build truly scalable and real-time data analytics applicationsExplore the applications of data science and scalable machine learning with PySparkIntegrate your clean and curated data with BI and SQL analysis toolsWho this book is for This book is for practicing data engineers, data scientists, data analysts, and data enthusiasts who are already using data analytics to explore distributed and scalable data analytics. Basic to intermediate knowledge of the disciplines of data engineering, data science, and SQL analytics is expected. General proficiency in using any programming language, especially Python, and working knowledge of performing data analytics using frameworks such as pandas and SQL will help you to get the most out of this book.
Essential Pyspark For Scalable Data Analytics
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Author : Sreeram Nudurupati
language : en
Publisher: Packt Publishing
Release Date : 2021-10
Essential Pyspark For Scalable Data Analytics written by Sreeram Nudurupati and has been published by Packt Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10 with Data mining categories.
Get started with distributed computing using PySpark, a single unified framework to solve end-to-end data analytics at scale Key Features: Discover how to convert huge amounts of raw data into meaningful and actionable insights Use Spark's unified analytics engine for end-to-end analytics, from data preparation to predictive analytics Perform data ingestion, cleansing, and integration for ML, data analytics, and data visualization Book Description: Apache Spark is a unified data analytics engine designed to process huge volumes of data quickly and efficiently. PySpark is Apache Spark's Python language API, which offers Python developers an easy-to-use scalable data analytics framework. Essential PySpark for Scalable Data Analytics starts by exploring the distributed computing paradigm and provides a high-level overview of Apache Spark. You'll begin your analytics journey with the data engineering process, learning how to perform data ingestion, cleansing, and integration at scale. This book helps you build real-time analytics pipelines that enable you to gain insights much faster. You'll then discover methods for building cloud-based data lakes, and explore Delta Lake, which brings reliability and performance to data lakes. The book also covers Data Lakehouse, an emerging paradigm, which combines the structure and performance of a data warehouse with the scalability of cloud-based data lakes. Later, you'll perform scalable data science and machine learning tasks using PySpark, such as data preparation, feature engineering, and model training and productionization. Finally, you'll learn ways to scale out standard Python ML libraries along with a new pandas API on top of PySpark called Koalas. By the end of this PySpark book, you'll be able to harness the power of PySpark to solve business problems. What You Will Learn: Understand the role of distributed computing in the world of big data Gain an appreciation for Apache Spark as the de facto go-to for big data processing Scale out your data analytics process using Apache Spark Build data pipelines using data lakes, and perform data visualization with PySpark and Spark SQL Leverage the cloud to build truly scalable and real-time data analytics applications Explore the applications of data science and scalable machine learning with PySpark Integrate your clean and curated data with BI and SQL analysis tools Who this book is for: This book is for practicing data engineers, data scientists, data analysts, and data enthusiasts who are already using data analytics to explore distributed and scalable data analytics. Basic to intermediate knowledge of the disciplines of data engineering, data science, and SQL analytics is expected. General proficiency in using any programming language, especially Python, and working knowledge of performing data analytics using frameworks such as pandas and SQL will help you to get the most out of this book.
Pyspark Essentials
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Author : Robert Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-01-08
Pyspark Essentials written by Robert Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-08 with Computers categories.
"PySpark Essentials: A Practical Guide to Distributed Computing" is an expertly crafted resource designed to demystify the complexities of distributed data processing with PySpark. Offering an in-depth exploration of PySpark's integration within the Apache Spark ecosystem, this book serves as a foundational text for both newcomers and seasoned data professionals. Readers will gain comprehensive insights into setting up their PySpark environment, navigating its core architecture, and harnessing its power for efficient data manipulation and analysis. Structured to enhance practical understanding, this guide covers a wide array of topics, from the creation and management of DataFrames and Datasets to advanced data processing with Resilient Distributed Datasets (RDDs). It delves into PySpark SQL, empowering users with the ability to perform sophisticated data queries, and explores MLlib for large-scale machine learning applications. The book also highlights strategies for optimizing PySpark applications and managing real-time data with PySpark Streaming. Through clearly defined best practices and troubleshooting tips, readers will be equipped to overcome common challenges, ensuring they can build robust, scalable, and effective data processing solutions. Whether aiming to enter the field of big data or to enhance current skills, this book offers the essential toolkit for mastering PySpark.
Apache Sedona Essentials
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Author : Robert Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-01-06
Apache Sedona Essentials written by Robert Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-06 with Computers categories.
"Apache Sedona Essentials: A Practical Guide to Spatial Data Processing" is meticulously crafted for beginners and professionals alike, offering a comprehensive overview of Apache Sedona's capabilities and applications in handling spatial data. This book serves as a definitive resource, equipping readers with the foundation needed to manage, query, and analyze spatial datasets efficiently using Sedona. Each chapter is structured to guide you progressively through core concepts and advanced techniques, ensuring a robust understanding of the functionalities that Apache Sedona provides. Focused on real-world applicability, this guide explores Sedona's integration within big data ecosystems, its performance optimization strategies, and the implementation of advanced spatial processing methods. From setting up your development environment to exploring complex spatial operations and deriving insights from data analytics, this book prepares you to tackle a variety of spatial data challenges across diverse domains. Through practical examples, detailed explanations, and best practice recommendations, readers will gain the skills needed to harness the full potential of spatial data intelligence using Apache Sedona.
Scalable Cloud Computing Patterns For Reliability And Performance
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Author : Peter Jones
language : en
Publisher: Walzone Press
Release Date : 2025-01-14
Scalable Cloud Computing Patterns For Reliability And Performance written by Peter Jones and has been published by Walzone Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-14 with Computers categories.
Dive into the transformative world of cloud computing with "Scalable Cloud Computing: Patterns for Reliability and Performance," your comprehensive guide to mastering the principles, strategies, and practices that define modern cloud environments. This carefully curated book navigates through the intricate landscape of cloud computing, from foundational concepts and architecture to designing resilient, scalable applications and managing complex data in the cloud. Whether you're a beginner seeking to understand the basics or an experienced professional aiming to enhance your skills, this book offers deep insights into ensuring reliability, optimizing performance, securing cloud environments, and much more. Explore the latest trends, including microservices, serverless computing, and emerging technologies that are pushing the boundaries of what's possible in the cloud. Through detailed explanations, practical examples, and real-world case studies, "Scalable Cloud Computing: Patterns for Reliability and Performance" equips you with the knowledge to architect and deploy robust applications that leverage the full potential of cloud computing. Unlock the secrets to optimizing costs, automating deployments with CI/CD, and navigating the complexities of data management and security in the cloud. This book is your gateway to becoming an expert in cloud computing, ready to tackle challenges and seize opportunities in this ever-evolving field. Join us on this journey to mastering cloud computing, where scalability and reliability are within your reach.
Big Data On Kubernetes
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Author : Neylson Crepalde
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-07-19
Big Data On Kubernetes written by Neylson Crepalde and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-19 with Computers categories.
Gain hands-on experience in building efficient and scalable big data architecture on Kubernetes, utilizing leading technologies such as Spark, Airflow, Kafka, and Trino Key Features Leverage Kubernetes in a cloud environment to integrate seamlessly with a variety of tools Explore best practices for optimizing the performance of big data pipelines Build end-to-end data pipelines and discover real-world use cases using popular tools like Spark, Airflow, and Kafka Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionIn today's data-driven world, organizations across different sectors need scalable and efficient solutions for processing large volumes of data. Kubernetes offers an open-source and cost-effective platform for deploying and managing big data tools and workloads, ensuring optimal resource utilization and minimizing operational overhead. If you want to master the art of building and deploying big data solutions using Kubernetes, then this book is for you. Written by an experienced data specialist, Big Data on Kubernetes takes you through the entire process of developing scalable and resilient data pipelines, with a focus on practical implementation. Starting with the basics, you’ll progress toward learning how to install Docker and run your first containerized applications. You’ll then explore Kubernetes architecture and understand its core components. This knowledge will pave the way for exploring a variety of essential tools for big data processing such as Apache Spark and Apache Airflow. You’ll also learn how to install and configure these tools on Kubernetes clusters. Throughout the book, you’ll gain hands-on experience building a complete big data stack on Kubernetes. By the end of this Kubernetes book, you’ll be equipped with the skills and knowledge you need to tackle real-world big data challenges with confidence.What you will learn Install and use Docker to run containers and build concise images Gain a deep understanding of Kubernetes architecture and its components Deploy and manage Kubernetes clusters on different cloud platforms Implement and manage data pipelines using Apache Spark and Apache Airflow Deploy and configure Apache Kafka for real-time data ingestion and processing Build and orchestrate a complete big data pipeline using open-source tools Deploy Generative AI applications on a Kubernetes-based architecture Who this book is for If you’re a data engineer, BI analyst, data team leader, data architect, or tech manager with a basic understanding of big data technologies, then this big data book is for you. Familiarity with the basics of Python programming, SQL queries, and YAML is required to understand the topics discussed in this book.
Ultimate Big Data Analytics With Apache Hadoop
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Author : Simhadri Govindappa
language : en
Publisher: Orange Education Pvt Ltd
Release Date : 2024-09-09
Ultimate Big Data Analytics With Apache Hadoop written by Simhadri Govindappa and has been published by Orange Education Pvt Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-09 with Computers categories.
TAGLINE Master the Hadoop Ecosystem and Build Scalable Analytics Systems KEY FEATURES ● Explains Hadoop, YARN, MapReduce, and Tez for understanding distributed data processing and resource management. ● Delves into Apache Hive and Apache Spark for their roles in data warehousing, real-time processing, and advanced analytics. ● Provides hands-on guidance for using Python with Hadoop for business intelligence and data analytics. DESCRIPTION In a rapidly evolving Big Data job market projected to grow by 28% through 2026 and with salaries reaching up to $150,000 annually—mastering big data analytics with the Hadoop ecosystem is most sought after for career advancement. The Ultimate Big Data Analytics with Apache Hadoop is an indispensable companion offering in-depth knowledge and practical skills needed to excel in today's data-driven landscape. The book begins laying a strong foundation with an overview of data lakes, data warehouses, and related concepts. It then delves into core Hadoop components such as HDFS, YARN, MapReduce, and Apache Tez, offering a blend of theory and practical exercises. You will gain hands-on experience with query engines like Apache Hive and Apache Spark, as well as file and table formats such as ORC, Parquet, Avro, Iceberg, Hudi, and Delta. Detailed instructions on installing and configuring clusters with Docker are included, along with big data visualization and statistical analysis using Python. Given the growing importance of scalable data pipelines, this book equips data engineers, analysts, and big data professionals with practical skills to set up, manage, and optimize data pipelines, and to apply machine learning techniques effectively. Don’t miss out on the opportunity to become a leader in the big data field to unlock the full potential of big data analytics with Hadoop. WHAT WILL YOU LEARN ● Gain expertise in building and managing large-scale data pipelines with Hadoop, YARN, and MapReduce. ● Master real-time analytics and data processing with Apache Spark’s powerful features. ● Develop skills in using Apache Hive for efficient data warehousing and complex queries. ● Integrate Python for advanced data analysis, visualization, and business intelligence in the Hadoop ecosystem. ● Learn to enhance data storage and processing performance using formats like ORC, Parquet, and Delta. ● Acquire hands-on experience in deploying and managing Hadoop clusters with Docker and Kubernetes. ● Build and deploy machine learning models with tools integrated into the Hadoop ecosystem. WHO IS THIS BOOK FOR? This book is tailored for data engineers, analysts, software developers, data scientists, IT professionals, and engineering students seeking to enhance their skills in big data analytics with Hadoop. Prerequisites include a basic understanding of big data concepts, programming knowledge in Java, Python, or SQL, and basic Linux command line skills. No prior experience with Hadoop is required, but a foundational grasp of data principles and technical proficiency will help readers fully engage with the material. TABLE OF CONTENTS 1. Introduction to Hadoop and ASF 2. Overview of Big Data Analytics 3. Hadoop and YARN MapReduce and Tez 4. Distributed Query Engines: Apache Hive 5. Distributed Query Engines: Apache Spark 6. File Formats and Table Formats (Apache Ice-berg, Hudi, and Delta) 7. Python and the Hadoop Ecosystem for Big Data Analytics - BI 8. Data Science and Machine Learning with Hadoop Ecosystem 9. Introduction to Cloud Computing and Other Apache Projects Index
Modin For Scalable Data Science
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Author : William Smith
language : en
Publisher: HiTeX Press
Release Date : 2025-07-24
Modin For Scalable Data Science written by William Smith and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-24 with Computers categories.
"Modin for Scalable Data Science" In the era of massive datasets and ever-expanding analytics pipelines, "Modin for Scalable Data Science" is a comprehensive guide for data engineers and scientists determined to break through the limits of single-node data workflows. The book opens by analyzing the bottlenecks inherent in contemporary data science, from memory and CPU constraints in pandas to the challenges of distributed data movement. It offers a thorough survey of modern distributed frameworks such as Spark and Dask, before introducing Modin—a breakthrough library that bridges the ease of pandas with the power of distributed computing. Real-world use cases, including large-scale ETL, feature engineering, and interactive analytics, highlight the practical motivations behind adopting scalable data science solutions. Diving deep into Modin’s architecture, the book explores its pluggable execution backends, innovative task graph design, and robust integration with crucial data science and machine learning ecosystems like NumPy, scikit-learn, and RAPIDS. Readers learn best practices for deploying and tuning Modin in diverse environments: from laptops to cloud clusters, containerized solutions via Kubernetes, and advanced resource management in production-grade settings. Thorough attention is paid to security, data locality, and the nuances of environment-specific configuration, ensuring readers gain both strategic understanding and actionable know-how for leveraging Modin at scale. As a hands-on reference, the book meticulously details Modin’s compatibility with pandas, approaches to debugging distributed DataFrames, and advanced profiling and optimization techniques. It empowers practitioners to automate machine learning pipelines, handle real-time inference, and scale MLOps with tools such as Ray Tune and Kubeflow. For those looking to extend or contribute to Modin, the closing chapters provide blueprints for plugin development, internal API mastery, and effective engagement with the open source community. This guide is essential for anyone seeking to harness the full potential of distributed data science without sacrificing the simplicity of familiar Python workflows.
Databricks Essentials
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Author : Robert Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-01-06
Databricks Essentials written by Robert Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-06 with Computers categories.
"Databricks Essentials: A Guide to Unified Data Analytics" delivers a comprehensive exploration of the contemporary Databricks platform, designed to empower professionals seeking to harness the capabilities of data analytics, engineering, and machine learning in an integrated environment. This book provides a structured approach, guiding readers through meticulously crafted chapters that cover every aspect of Databricks—from establishing a foundational understanding to advanced performance optimization and security best practices. Each chapter is developed with accessibility and practical application in mind, ensuring that both beginners and seasoned data professionals can benefit from its insights. As organizations face increasing demands for data-driven decision-making, the need for a unified analytics platform has never been more critical. This book unravels the intricacies of Databricks, showcasing its potential to streamline workflows and revolutionize data operations through collaborative tools and real-time processing capabilities. Readers will discover how to optimize resources, implement scalable solutions, and leverage machine learning to drive results. Enhanced by illustrative case studies and practical examples, "Databricks Essentials" not only educates but also inspires readers to explore new frontiers in data analytics, making it an indispensable resource for those committed to innovation and excellence in the field.
Python For Data Analysis
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Author : Dr. Katta Padmaja
language : en
Publisher: RK Publication
Release Date : 2024-07-29
Python For Data Analysis written by Dr. Katta Padmaja and has been published by RK Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-29 with Computers categories.
Python for Data Analysis for data enthusiasts, scientists, and analysts looking to harness Python’s capabilities in data manipulation, processing, and visualization. Covering essential libraries like Pandas, NumPy, and Matplotlib, this data cleaning, aggregation, and exploratory data analysis techniques. It emphasizes hands-on examples and real-world datasets to build a strong foundation in Python-based data analysis, making it an ideal resource for both beginners and professionals aiming to deepen their data skills in Python's versatile ecosystem.