Download or read book Building ETL Pipelines with Python written by Brij Kishore Pandey and published by Packt Publishing Ltd. This book was released on 2023-09-29 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: Develop production-ready ETL pipelines by leveraging Python libraries and deploying them for suitable use cases Key Features Understand how to set up a Python virtual environment with PyCharm Learn functional and object-oriented approaches to create ETL pipelines Create robust CI/CD processes for ETL pipelines Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionModern extract, transform, and load (ETL) pipelines for data engineering have favored the Python language for its broad range of uses and a large assortment of tools, applications, and open source components. With its simplicity and extensive library support, Python has emerged as the undisputed choice for data processing. In this book, you’ll walk through the end-to-end process of ETL data pipeline development, starting with an introduction to the fundamentals of data pipelines and establishing a Python development environment to create pipelines. Once you've explored the ETL pipeline design principles and ET development process, you'll be equipped to design custom ETL pipelines. Next, you'll get to grips with the steps in the ETL process, which involves extracting valuable data; performing transformations, through cleaning, manipulation, and ensuring data integrity; and ultimately loading the processed data into storage systems. You’ll also review several ETL modules in Python, comparing their pros and cons when building data pipelines and leveraging cloud tools, such as AWS, to create scalable data pipelines. Lastly, you’ll learn about the concept of test-driven development for ETL pipelines to ensure safe deployments. By the end of this book, you’ll have worked on several hands-on examples to create high-performance ETL pipelines to develop robust, scalable, and resilient environments using Python.What you will learn Explore the available libraries and tools to create ETL pipelines using Python Write clean and resilient ETL code in Python that can be extended and easily scaled Understand the best practices and design principles for creating ETL pipelines Orchestrate the ETL process and scale the ETL pipeline effectively Discover tools and services available in AWS for ETL pipelines Understand different testing strategies and implement them with the ETL process Who this book is for If you are a data engineer or software professional looking to create enterprise-level ETL pipelines using Python, this book is for you. Fundamental knowledge of Python is a prerequisite.
Download or read book Practical Guide to Building an ETL Pipeline written by John Smith and published by Independently Published. This book was released on 2024-05-02 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unlock the potential of data with "Practical Guide to Building an ETL Pipeline with Python and SQL," the definitive resource for creating high-performance ETL pipelines. This essential guide is meticulously designed for data professionals seeking to harness the data-intensive capabilities of Python and SQL. From establishing a development environment and extracting raw data to optimizing and securing data processes, this book offers comprehensive coverage of every aspect of ETL pipeline development. Whether you're a data engineer, IT professional, or a scholar in data science, this book provides step-by-step instructions, practical examples, and expert insights necessary for mastering the creation and management of robust ETL pipelines. By the end of this guide, you will possess the skills to transform disparate data into meaningful insights, ensuring your data processes are efficient, scalable, and secure. Dive into advanced topics with ease and explore best practices that will make your data workflows more productive and error-resistant. With this book, elevate your organization's data strategy and foster a data-driven culture that thrives on precision and performance. Embrace the journey to becoming an adept data professional with a solid foundation in ETL processes, equipped to handle the challenges of today's data demands.
Download or read book Data Pipelines Pocket Reference written by James Densmore and published by O'Reilly Media. This book was released on 2021-02-10 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data pipelines are the foundation for success in data analytics. Moving data from numerous diverse sources and transforming it to provide context is the difference between having data and actually gaining value from it. This pocket reference defines data pipelines and explains how they work in today's modern data stack. You'll learn common considerations and key decision points when implementing pipelines, such as batch versus streaming data ingestion and build versus buy. This book addresses the most common decisions made by data professionals and discusses foundational concepts that apply to open source frameworks, commercial products, and homegrown solutions. You'll learn: What a data pipeline is and how it works How data is moved and processed on modern data infrastructure, including cloud platforms Common tools and products used by data engineers to build pipelines How pipelines support analytics and reporting needs Considerations for pipeline maintenance, testing, and alerting
Download or read book Learn Python by Building Data Science Applications written by Philipp Kats and published by Packt Publishing Ltd. This book was released on 2019-08-30 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understand the constructs of the Python programming language and use them to build data science projects Key FeaturesLearn the basics of developing applications with Python and deploy your first data applicationTake your first steps in Python programming by understanding and using data structures, variables, and loopsDelve into Jupyter, NumPy, Pandas, SciPy, and sklearn to explore the data science ecosystem in PythonBook Description Python is the most widely used programming language for building data science applications. Complete with step-by-step instructions, this book contains easy-to-follow tutorials to help you learn Python and develop real-world data science projects. The “secret sauce” of the book is its curated list of topics and solutions, put together using a range of real-world projects, covering initial data collection, data analysis, and production. This Python book starts by taking you through the basics of programming, right from variables and data types to classes and functions. You’ll learn how to write idiomatic code and test and debug it, and discover how you can create packages or use the range of built-in ones. You’ll also be introduced to the extensive ecosystem of Python data science packages, including NumPy, Pandas, scikit-learn, Altair, and Datashader. Furthermore, you’ll be able to perform data analysis, train models, and interpret and communicate the results. Finally, you’ll get to grips with structuring and scheduling scripts using Luigi and sharing your machine learning models with the world as a microservice. By the end of the book, you’ll have learned not only how to implement Python in data science projects, but also how to maintain and design them to meet high programming standards. What you will learnCode in Python using Jupyter and VS CodeExplore the basics of coding – loops, variables, functions, and classesDeploy continuous integration with Git, Bash, and DVCGet to grips with Pandas, NumPy, and scikit-learnPerform data visualization with Matplotlib, Altair, and DatashaderCreate a package out of your code using poetry and test it with PyTestMake your machine learning model accessible to anyone with the web APIWho this book is for If you want to learn Python or data science in a fun and engaging way, this book is for you. You’ll also find this book useful if you’re a high school student, researcher, analyst, or anyone with little or no coding experience with an interest in the subject and courage to learn, fail, and learn from failing. A basic understanding of how computers work will be useful.
Download or read book Data Engineering with Python written by Paul Crickard and published by Packt Publishing Ltd. This book was released on 2020-10-23 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build, monitor, and manage real-time data pipelines to create data engineering infrastructure efficiently using open-source Apache projects Key Features Become well-versed in data architectures, data preparation, and data optimization skills with the help of practical examples Design data models and learn how to extract, transform, and load (ETL) data using Python Schedule, automate, and monitor complex data pipelines in production Book DescriptionData engineering provides the foundation for data science and analytics, and forms an important part of all businesses. This book will help you to explore various tools and methods that are used for understanding the data engineering process using Python. The book will show you how to tackle challenges commonly faced in different aspects of data engineering. You’ll start with an introduction to the basics of data engineering, along with the technologies and frameworks required to build data pipelines to work with large datasets. You’ll learn how to transform and clean data and perform analytics to get the most out of your data. As you advance, you'll discover how to work with big data of varying complexity and production databases, and build data pipelines. Using real-world examples, you’ll build architectures on which you’ll learn how to deploy data pipelines. By the end of this Python book, you’ll have gained a clear understanding of data modeling techniques, and will be able to confidently build data engineering pipelines for tracking data, running quality checks, and making necessary changes in production.What you will learn Understand how data engineering supports data science workflows Discover how to extract data from files and databases and then clean, transform, and enrich it Configure processors for handling different file formats as well as both relational and NoSQL databases Find out how to implement a data pipeline and dashboard to visualize results Use staging and validation to check data before landing in the warehouse Build real-time pipelines with staging areas that perform validation and handle failures Get to grips with deploying pipelines in the production environment Who this book is for This book is for data analysts, ETL developers, and anyone looking to get started with or transition to the field of data engineering or refresh their knowledge of data engineering using Python. This book will also be useful for students planning to build a career in data engineering or IT professionals preparing for a transition. No previous knowledge of data engineering is required.
Download or read book Data Pipelines with Apache Airflow written by Bas P. Harenslak and published by Simon and Schuster. This book was released on 2021-04-27 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book teaches you how to build and maintain effective data pipelines. Youll explore the most common usage patterns, including aggregating multiple data sources, connecting to and from data lakes, and cloud deployment. --
Download or read book Building Data Pipelines with Python written by Katherine Jarmul and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "This course shows you how to build data pipelines and automate workflows using Python 3. From simple task-based messaging queues to complex frameworks like Luigi and Airflow, the course delivers the essential knowledge you need to develop your own automation solutions. You'll learn the architecture basics, and receive an introduction to a wide variety of the most popular frameworks and tools. Designed for the working data professional who is new to the world of data pipelines and distributed solutions, the course requires intermediate level Python experience and the ability to manage your own system set-ups."--Resource description page.
Download or read book Pragmatic AI written by Noah Gift and published by Addison-Wesley Professional. This book was released on 2018-07-12 with total page 720 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results—even if you don’t have a strong background in math or data science. Gift illuminates powerful off-the-shelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you streamline and simplify every step, from deployment to production, and build exceptionally scalable solutions. As you learn how machine language (ML) solutions work, you’ll gain a more intuitive understanding of what you can achieve with them and how to maximize their value. Building on these fundamentals, you’ll walk step-by-step through building cloud-based AI/ML applications to address realistic issues in sports marketing, project management, product pricing, real estate, and beyond. Whether you’re a business professional, decision-maker, student, or programmer, Gift’s expert guidance and wide-ranging case studies will prepare you to solve data science problems in virtually any environment. Get and configure all the tools you’ll need Quickly review all the Python you need to start building machine learning applications Master the AI and ML toolchain and project lifecycle Work with Python data science tools such as IPython, Pandas, Numpy, Juypter Notebook, and Sklearn Incorporate a pragmatic feedback loop that continually improves the efficiency of your workflows and systems Develop cloud AI solutions with Google Cloud Platform, including TPU, Colaboratory, and Datalab services Define Amazon Web Services cloud AI workflows, including spot instances, code pipelines, boto, and more Work with Microsoft Azure AI APIs Walk through building six real-world AI applications, from start to finish Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Download or read book Python Automation Mastery written by Rob Botwright and published by Rob Botwright. This book was released on 101-01-01 with total page 265 pages. Available in PDF, EPUB and Kindle. Book excerpt: 🚀 PYTHON AUTOMATION MASTERY: From Novice to Pro Book Bundle 🚀 Are you ready to unlock the full potential of Python for automation? Look no further than the "Python Automation Mastery" book bundle, a comprehensive collection designed to take you from a beginner to an automation pro! 📘 Book 1 - Python Automation Mastery: A Beginner's Guide · Perfect for newcomers to programming and Python. · Learn Python fundamentals and the art of automation. · Start automating everyday tasks right away! 📗 Book 2 - Python Automation Mastery: Intermediate Techniques · Take your skills to the next level. · Discover web scraping, scripting, error handling, and data manipulation. · Tackle real-world automation challenges with confidence. 📙 Book 3 - Python Automation Mastery: Advanced Strategies · Explore advanced automation concepts. · Master object-oriented programming and external libraries. · Design and implement complex automation projects. 📕 Book 4 - Python Automation Mastery: Expert-Level Solutions · Become an automation architect. · Handle high-level use cases in AI, network security, and data analysis. · Elevate your automation skills to expert status. 🌟 What Makes This Bundle Special? · Comprehensive journey from novice to pro in one bundle. · Easy-to-follow, step-by-step guides in each book. · Real-world examples and hands-on exercises. · Learn ethical automation practices and best strategies. · Access a treasure trove of automation knowledge. 🚀 Why Python? Python is the go-to language for automation due to its simplicity and versatility. Whether you're looking to streamline everyday tasks or tackle complex automation challenges, Python is your ultimate tool. 📈 Invest in Your Future Automation skills are in high demand across industries. By mastering Python automation, you'll enhance your career prospects, supercharge your productivity, and become a sought-after automation expert. 📚 Grab the Complete Bundle Now! Don't miss out on this opportunity to become a Python automation master. Get all four books in one bundle and embark on your journey from novice to pro. Buy now and transform your Python skills into automation mastery!
Download or read book Data Analytics for Marketing written by Guilherme Diaz-Bérrio and published by Packt Publishing Ltd. This book was released on 2024-05-10 with total page 452 pages. Available in PDF, EPUB and Kindle. Book excerpt: Conduct data-driven marketing research and analysis with hands-on examples using Python by leveraging open-source tools and libraries Key Features Analyze marketing data using proper statistical techniques Use data modeling and analytics to understand customer preferences and enhance strategies without complex math Implement Python libraries like DoWhy, Pandas, and Prophet in a business setting with examples and use cases Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionMost marketing professionals are familiar with various sources of customer data that promise insights for success. There are extensive sources of data, from customer surveys to digital marketing data. Moreover, there is an increasing variety of tools and techniques to shape data, from small to big data. However, having the right knowledge and understanding the context of how to use data and tools is crucial. In this book, you’ll learn how to give context to your data and turn it into useful information. You’ll understand how and where to use a tool or dataset for a specific question, exploring the "what and why questions" to provide real value to your stakeholders. Using Python, this book will delve into the basics of analytics and causal inference. Then, you’ll focus on visualization and presentation, followed by understanding guidelines on how to present and condense large amounts of information into KPIs. After learning how to plan ahead and forecast, you’ll delve into customer analytics and insights. Finally, you’ll measure the effectiveness of your marketing efforts and derive insights for data-driven decision-making. By the end of this book, you’ll understand the tools you need to use on specific datasets to provide context and shape your data, as well as to gain information to boost your marketing efforts.What you will learn Understand the basic ideas behind the main statistical models used in marketing analytics Apply the right models and tools to a specific analytical question Discover how to conduct causal inference, experimentation, and statistical modeling with Python Implement common open source Python libraries for specific use cases with immediately applicable code Analyze customer lifetime data and generate customer insights Go through the different stages of analytics, from descriptive to prescriptive Who this book is for This book is for data analysts and data scientists working in a marketing team supporting analytics and marketing research, who want to provide better insights that lead to data-driven decision-making. Prior knowledge of Python, data analysis, and statistics is required to get the most out of this book.
Download or read book Building Big Data Pipelines with Apache Beam written by Jan Lukavsky and published by Packt Publishing Ltd. This book was released on 2022-01-21 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: Implement, run, operate, and test data processing pipelines using Apache Beam Key FeaturesUnderstand how to improve usability and productivity when implementing Beam pipelinesLearn how to use stateful processing to implement complex use cases using Apache BeamImplement, test, and run Apache Beam pipelines with the help of expert tips and techniquesBook Description Apache Beam is an open source unified programming model for implementing and executing data processing pipelines, including Extract, Transform, and Load (ETL), batch, and stream processing. This book will help you to confidently build data processing pipelines with Apache Beam. You'll start with an overview of Apache Beam and understand how to use it to implement basic pipelines. You'll also learn how to test and run the pipelines efficiently. As you progress, you'll explore how to structure your code for reusability and also use various Domain Specific Languages (DSLs). Later chapters will show you how to use schemas and query your data using (streaming) SQL. Finally, you'll understand advanced Apache Beam concepts, such as implementing your own I/O connectors. By the end of this book, you'll have gained a deep understanding of the Apache Beam model and be able to apply it to solve problems. What you will learnUnderstand the core concepts and architecture of Apache BeamImplement stateless and stateful data processing pipelinesUse state and timers for processing real-time event processingStructure your code for reusabilityUse streaming SQL to process real-time data for increasing productivity and data accessibilityRun a pipeline using a portable runner and implement data processing using the Apache Beam Python SDKImplement Apache Beam I/O connectors using the Splittable DoFn APIWho this book is for This book is for data engineers, data scientists, and data analysts who want to learn how Apache Beam works. Intermediate-level knowledge of the Java programming language is assumed.
Download or read book Continuous Integration and Delivery with Test driven Development written by Amit Bhanushali and published by BPB Publications. This book was released on 2024-03-19 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: Building tomorrow, today: Seamless integration, continuous deliver KEY FEATURES ● Step-by-step guidance to construct automated software and data CI/CD pipelines. ● Real-world case studies demonstrating CI/CD best practices across diverse organizations and development environments. ● Actionable frameworks to instill an organizational culture of collaboration, quality, and rapid iteration grounded in TDD values. DESCRIPTION As software complexity grows, quality and delivery speed increasingly rely on automated pipelines. This practical guide equips readers to construct robust CI/CD workflows that boost productivity and reliability. Step-by-step walkthroughs detail the technical implementation of continuous practices, while real-world case studies showcase solutions tailored for diverse systems and organizational needs. Master CI/CD, crucial for modern software development, with this book. It compares traditional versus test-driven development, stressing testing's importance. In this book, we will explore CI/CD's principles, benefits, and DevOps integration. We will build robust pipelines covering containerization, version control, and infrastructure as code. Through this book, you will learn about effective CD with monitoring, security, and release management, you will learn how to optimize CI/CD for different scenarios and applications, emphasizing collaboration and automation for success. With actionable best practices grounded in TDD principles, this book teaches how to leverage automated processes to cultivate shared ownership, design simplicity, comprehensive testing, and ultimately deliver exceptional business value. WHAT YOU WILL LEARN ● Construct smooth automated CI/CD pipelines tailored for complex systems. ● Master implementation strategies for diverse development environments. ● Design comprehensive test suites leveraging leading tools and frameworks. ● Instill a collaborative culture grounded in TDD values for ownership and simplicity. ● Optimize release processes for efficiency, quality, and business alignment. WHO THIS BOOK IS FOR This book is ideal for software engineers, developers, testers, and technical leads seeking to improve their CI/CD proficiency. Whether you are starting to explore the tool or looking to deepen your understanding, this book is a valuable resource for anyone eager to learn and master the technology. TABLE OF CONTENTS 1. Adopting a Test-driven Development Mindset 2. Understanding CI/CD Concepts 3. Building the CI/CD Pipeline 4. Ensuring Effective CD 5. Optimizing CI/CD Practices 6. Specialized CI/CD Applications 7. Model Operations: DevOps Pipeline Case Studies 8. Data CI/CD: Emerging Trends and Roles
Download or read book Python Web Scraping Cookbook written by Michael Heydt and published by Packt Publishing Ltd. This book was released on 2018-02-09 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: Untangle your web scraping complexities and access web data with ease using Python scripts Key Features Hands-on recipes for advancing your web scraping skills to expert level One-stop solution guide to address complex and challenging web scraping tasks using Python Understand web page structures and collect data from a website with ease Book Description Python Web Scraping Cookbook is a solution-focused book that will teach you techniques to develop high-performance Scrapers, and deal with cookies, hidden form fields, Ajax-based sites and proxies. You'll explore a number of real-world scenarios where every part of the development or product life cycle will be fully covered. You will not only develop the skills to design reliable, high-performing data flows, but also deploy your codebase to Amazon Web Services (AWS). If you are involved in software engineering, product development, or data mining or in building data-driven products, you will find this book useful as each recipe has a clear purpose and objective. Right from extracting data from websites to writing a sophisticated web crawler, the book's independent recipes will be extremely helpful while on the job. This book covers Python libraries, requests, and BeautifulSoup. You will learn about crawling, web spidering, working with AJAX websites, and paginated items. You will also understand to tackle problems such as 403 errors, working with proxy, scraping images, and LXML. By the end of this book, you will be able to scrape websites more efficiently and deploy and operate your scraper in the cloud. What you will learn Use a variety of tools to scrape any website and data, including Scrapy and Selenium Master expression languages, such as XPath and CSS, and regular expressions to extract web data Deal with scraping traps such as hidden form fields, throttling, pagination, and different status codes Build robust scraping pipelines with SQS and RabbitMQ Scrape assets like image media and learn what to do when Scraper fails to run Explore ETL techniques of building a customized crawler, parser, and convert structured and unstructured data from websites Deploy and run your scraper as a service in AWS Elastic Container Service Who this book is for This book is ideal for Python programmers, web administrators, security professionals, and anyone who wants to perform web analytics. Familiarity with Python and basic understanding of web scraping will be useful to make the best of this book.
Download or read book Python Tutorial 3 11 3 written by Guido Van Rossum and published by . This book was released on 2023-05-12 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Azure Data Factory Cookbook written by Dmitry Anoshin and published by Packt Publishing Ltd. This book was released on 2020-12-24 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: Solve real-world data problems and create data-driven workflows for easy data movement and processing at scale with Azure Data Factory Key FeaturesLearn how to load and transform data from various sources, both on-premises and on cloudUse Azure Data Factory’s visual environment to build and manage hybrid ETL pipelinesDiscover how to prepare, transform, process, and enrich data to generate key insightsBook Description Azure Data Factory (ADF) is a modern data integration tool available on Microsoft Azure. This Azure Data Factory Cookbook helps you get up and running by showing you how to create and execute your first job in ADF. You’ll learn how to branch and chain activities, create custom activities, and schedule pipelines. This book will help you to discover the benefits of cloud data warehousing, Azure Synapse Analytics, and Azure Data Lake Gen2 Storage, which are frequently used for big data analytics. With practical recipes, you’ll learn how to actively engage with analytical tools from Azure Data Services and leverage your on-premise infrastructure with cloud-native tools to get relevant business insights. As you advance, you’ll be able to integrate the most commonly used Azure Services into ADF and understand how Azure services can be useful in designing ETL pipelines. The book will take you through the common errors that you may encounter while working with ADF and show you how to use the Azure portal to monitor pipelines. You’ll also understand error messages and resolve problems in connectors and data flows with the debugging capabilities of ADF. By the end of this book, you’ll be able to use ADF as the main ETL and orchestration tool for your data warehouse or data platform projects. What you will learnCreate an orchestration and transformation job in ADFDevelop, execute, and monitor data flows using Azure SynapseCreate big data pipelines using Azure Data Lake and ADFBuild a machine learning app with Apache Spark and ADFMigrate on-premises SSIS jobs to ADFIntegrate ADF with commonly used Azure services such as Azure ML, Azure Logic Apps, and Azure FunctionsRun big data compute jobs within HDInsight and Azure DatabricksCopy data from AWS S3 and Google Cloud Storage to Azure Storage using ADF's built-in connectorsWho this book is for This book is for ETL developers, data warehouse and ETL architects, software professionals, and anyone who wants to learn about the common and not-so-common challenges faced while developing traditional and hybrid ETL solutions using Microsoft's Azure Data Factory. You’ll also find this book useful if you are looking for recipes to improve or enhance your existing ETL pipelines. Basic knowledge of data warehousing is expected.
Download or read book Spark The Definitive Guide written by Bill Chambers and published by "O'Reilly Media, Inc.". This book was released on 2018-02-08 with total page 594 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of the open-source cluster-computing framework. With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals. Youâ??ll explore the basic operations and common functions of Sparkâ??s structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Sparkâ??s scalable machine-learning library. Get a gentle overview of big data and Spark Learn about DataFrames, SQL, and Datasetsâ??Sparkâ??s core APIsâ??through worked examples Dive into Sparkâ??s low-level APIs, RDDs, and execution of SQL and DataFrames Understand how Spark runs on a cluster Debug, monitor, and tune Spark clusters and applications Learn the power of Structured Streaming, Sparkâ??s stream-processing engine Learn how you can apply MLlib to a variety of problems, including classification or recommendation
Download or read book The Data Warehouse ETL Toolkit written by Ralph Kimball and published by John Wiley & Sons. This book was released on 2011-04-27 with total page 530 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cowritten by Ralph Kimball, the world's leading data warehousing authority, whose previous books have sold more than 150,000 copies Delivers real-world solutions for the most time- and labor-intensive portion of data warehousing-data staging, or the extract, transform, load (ETL) process Delineates best practices for extracting data from scattered sources, removing redundant and inaccurate data, transforming the remaining data into correctly formatted data structures, and then loading the end product into the data warehouse Offers proven time-saving ETL techniques, comprehensive guidance on building dimensional structures, and crucial advice on ensuring data quality