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Data Pipelines Pocket Reference
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Book Synopsis Data Pipelines Pocket Reference by : James Densmore
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
Book Synopsis Data Pipelines Pocket Reference by : James Densmore
Download or read book Data Pipelines Pocket Reference written by James Densmore and published by "O'Reilly Media, Inc.". This book was released on 2021-02-10 with total page 276 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
Book Synopsis Data Pipelines Pocket Reference by : James Densmore
Download or read book Data Pipelines Pocket Reference written by James Densmore and published by . This book was released on 2021 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data pipelines are the foundation for success in data analytics and machine learning. Moving data from many diverse sources and processing 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 data pipeline design patterns, data ingestion implementation, data transformation, the orchestration of pipelines, and build versus buy decision making. 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 machine learning and analytics needs Considerations for pipeline maintenance, testing, and alerting.
Book Synopsis Machine Learning Pocket Reference by : Matt Harrison
Download or read book Machine Learning Pocket Reference written by Matt Harrison and published by "O'Reilly Media, Inc.". This book was released on 2019-08-27 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project. Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. You’ll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics. This pocket reference includes sections that cover: Classification, using the Titanic dataset Cleaning data and dealing with missing data Exploratory data analysis Common preprocessing steps using sample data Selecting features useful to the model Model selection Metrics and classification evaluation Regression examples using k-nearest neighbor, decision trees, boosting, and more Metrics for regression evaluation Clustering Dimensionality reduction Scikit-learn pipelines
Book Synopsis Data Engineering with Python by : Paul Crickard
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.
Book Synopsis Data Pipelines with Apache Airflow by : Bas P. Harenslak
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. --
Book Synopsis Kafka: The Definitive Guide by : Neha Narkhede
Download or read book Kafka: The Definitive Guide written by Neha Narkhede and published by "O'Reilly Media, Inc.". This book was released on 2017-08-31 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: Every enterprise application creates data, whether it’s log messages, metrics, user activity, outgoing messages, or something else. And how to move all of this data becomes nearly as important as the data itself. If you’re an application architect, developer, or production engineer new to Apache Kafka, this practical guide shows you how to use this open source streaming platform to handle real-time data feeds. Engineers from Confluent and LinkedIn who are responsible for developing Kafka explain how to deploy production Kafka clusters, write reliable event-driven microservices, and build scalable stream-processing applications with this platform. Through detailed examples, you’ll learn Kafka’s design principles, reliability guarantees, key APIs, and architecture details, including the replication protocol, the controller, and the storage layer. Understand publish-subscribe messaging and how it fits in the big data ecosystem. Explore Kafka producers and consumers for writing and reading messages Understand Kafka patterns and use-case requirements to ensure reliable data delivery Get best practices for building data pipelines and applications with Kafka Manage Kafka in production, and learn to perform monitoring, tuning, and maintenance tasks Learn the most critical metrics among Kafka’s operational measurements Explore how Kafka’s stream delivery capabilities make it a perfect source for stream processing systems
Book Synopsis PyTorch Pocket Reference by : Joe Papa
Download or read book PyTorch Pocket Reference written by Joe Papa and published by O'Reilly Media. This book was released on 2021-09-14 with total page 265 pages. Available in PDF, EPUB and Kindle. Book excerpt: This concise, easy-to-use reference puts one of the most popular frameworks for deep learning research and development at your fingertips. Author Joe Papa provides instant access to syntax, design patterns, and code examples to accelerate your development and reduce the time you spend searching for answers. Research scientists, machine learning engineers, and software developers will find clear, structured PyTorch code that covers every step of neural network development--from loading data to customizing training loops to model optimization and GPU/TPU acceleration. Quickly learn how to deploy your code to production using AWS, GCP, or Azure, and your ML models to mobile and edge devices. Learn basic PyTorch syntax and design patterns Create custom models and data transforms Train and deploy models using a GPU and TPU Train and test a deep learning classifier Accelerate training using optimization and distributed training Access useful PyTorch libraries and the PyTorch ecosystem
Book Synopsis 97 Things Every Data Engineer Should Know by : Tobias Macey
Download or read book 97 Things Every Data Engineer Should Know written by Tobias Macey and published by "O'Reilly Media, Inc.". This book was released on 2021-06-11 with total page 243 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take advantage of today's sky-high demand for data engineers. With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Contributors from notable companies including Twitter, Google, Stitch Fix, Microsoft, Capital One, and LinkedIn share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges. Edited by Tobias Macey, host of the popular Data Engineering Podcast, this book presents 97 concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data. Data engineers, data architects, data team managers, data scientists, machine learning engineers, and software engineers will greatly benefit from the wisdom and experience of their peers. Topics include: The Importance of Data Lineage - Julien Le Dem Data Security for Data Engineers - Katharine Jarmul The Two Types of Data Engineering and Data Engineers - Jesse Anderson Six Dimensions for Picking an Analytical Data Warehouse - Gleb Mezhanskiy The End of ETL as We Know It - Paul Singman Building a Career as a Data Engineer - Vijay Kiran Modern Metadata for the Modern Data Stack - Prukalpa Sankar Your Data Tests Failed! Now What? - Sam Bail
Book Synopsis Tableau Desktop Pocket Reference by : Ryan Sleeper
Download or read book Tableau Desktop Pocket Reference written by Ryan Sleeper and published by "O'Reilly Media, Inc.". This book was released on 2021-01-21 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a crowded field of data visualization and analytics tools, Tableau Desktop has emerged as the clear leader. This is partly due to its ease of use, but once you dive into Tableau's extensive feature set, you'll understand just how powerful and flexible this software can be for your business or organization. With this handy pocket reference, author Ryan Sleeper (Innovative Tableau) shows you how to translate the vast amounts of data into useful information. Tableau has done an amazing job of making valuable insights accessible to analysts and executives who would otherwise need to rely on IT. This book quickly guides you through Tableau Desktop's learning curve. You'll learn: How to shape data for use with Tableau Desktop How to create the most effective chart types Core concepts including discrete versus continuous Must-know technical features including filters, parameters, and sets Key syntax for creating the most useful analyses How to bring it all together with dashboards And more!
Book Synopsis Bash Pocket Reference by : Arnold Robbins
Download or read book Bash Pocket Reference written by Arnold Robbins and published by "O'Reilly Media, Inc.". This book was released on 2016-02-17 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: It’s simple: if you want to interact deeply with Mac OS X, Linux, and other Unix-like systems, you need to know how to work with the Bash shell. This concise little book puts all of the essential information about Bash right at your fingertips. You’ll quickly find answers to the annoying questions that generally come up when you’re writing shell scripts: What characters do you need to quote? How do you get variable substitution to do exactly what you want? How do you use arrays? Updated for Bash version 4.4, this book has the answers to these and other problems in a format that makes browsing quick and easy. Topics include: Invoking the shell Syntax Functions and variables Arithmetic expressions Command history Programmable completion Job control Shell options Command execution Coprocesses Restricted shells Built-in commands
Book Synopsis Building Big Data Pipelines with Apache Beam by : Jan Lukavsky
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.
Book Synopsis The Self-Service Data Roadmap by : Sandeep Uttamchandani
Download or read book The Self-Service Data Roadmap written by Sandeep Uttamchandani and published by "O'Reilly Media, Inc.". This book was released on 2020-09-10 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data-driven insights are a key competitive advantage for any industry today, but deriving insights from raw data can still take days or weeks. Most organizations can’t scale data science teams fast enough to keep up with the growing amounts of data to transform. What’s the answer? Self-service data. With this practical book, data engineers, data scientists, and team managers will learn how to build a self-service data science platform that helps anyone in your organization extract insights from data. Sandeep Uttamchandani provides a scorecard to track and address bottlenecks that slow down time to insight across data discovery, transformation, processing, and production. This book bridges the gap between data scientists bottlenecked by engineering realities and data engineers unclear about ways to make self-service work. Build a self-service portal to support data discovery, quality, lineage, and governance Select the best approach for each self-service capability using open source cloud technologies Tailor self-service for the people, processes, and technology maturity of your data platform Implement capabilities to democratize data and reduce time to insight Scale your self-service portal to support a large number of users within your organization
Book Synopsis Data Science on AWS by : Chris Fregly
Download or read book Data Science on AWS written by Chris Fregly and published by "O'Reilly Media, Inc.". This book was released on 2021-04-07 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt: With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more
Book Synopsis Designing Cloud Data Platforms by : Danil Zburivsky
Download or read book Designing Cloud Data Platforms written by Danil Zburivsky and published by Simon and Schuster. This book was released on 2021-03-17 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: In Designing Cloud Data Platforms, Danil Zburivsky and Lynda Partner reveal a six-layer approach that increases flexibility and reduces costs. Discover patterns for ingesting data from a variety of sources, then learn to harness pre-built services provided by cloud vendors. Summary Centralized data warehouses, the long-time defacto standard for housing data for analytics, are rapidly giving way to multi-faceted cloud data platforms. Companies that embrace modern cloud data platforms benefit from an integrated view of their business using all of their data and can take advantage of advanced analytic practices to drive predictions and as yet unimagined data services. Designing Cloud Data Platforms is a hands-on guide to envisioning and designing a modern scalable data platform that takes full advantage of the flexibility of the cloud. As you read, you’ll learn the core components of a cloud data platform design, along with the role of key technologies like Spark and Kafka Streams. You’ll also explore setting up processes to manage cloud-based data, keep it secure, and using advanced analytic and BI tools to analyze it. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Well-designed pipelines, storage systems, and APIs eliminate the complicated scaling and maintenance required with on-prem data centers. Once you learn the patterns for designing cloud data platforms, you’ll maximize performance no matter which cloud vendor you use. About the book In Designing Cloud Data Platforms, Danil Zburivsky and Lynda Partner reveal a six-layer approach that increases flexibility and reduces costs. Discover patterns for ingesting data from a variety of sources, then learn to harness pre-built services provided by cloud vendors. What's inside Best practices for structured and unstructured data sets Cloud-ready machine learning tools Metadata and real-time analytics Defensive architecture, access, and security About the reader For data professionals familiar with the basics of cloud computing, and Hadoop or Spark. About the author Danil Zburivsky has over 10 years of experience designing and supporting large-scale data infrastructure for enterprises across the globe. Lynda Partner is the VP of Analytics-as-a-Service at Pythian, and has been on the business side of data for over 20 years. Table of Contents 1 Introducing the data platform 2 Why a data platform and not just a data warehouse 3 Getting bigger and leveraging the Big 3: Amazon, Microsoft Azure, and Google 4 Getting data into the platform 5 Organizing and processing data 6 Real-time data processing and analytics 7 Metadata layer architecture 8 Schema management 9 Data access and security 10 Fueling business value with data platforms
Book Synopsis Creating a Data-Driven Organization by : Carl Anderson
Download or read book Creating a Data-Driven Organization written by Carl Anderson and published by "O'Reilly Media, Inc.". This book was released on 2015-07-23 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: "What do you need to become a data-driven organization? Far more than having big data or a crack team of unicorn data scientists, it requires establishing an effective, deeply-ingrained data culture. This practical book shows you how true data-drivenness involves processes that require genuine buy-in across your company ... Through interviews and examples from data scientists and analytics leaders in a variety of industries ... Anderson explains the analytics value chain you need to adopt when building predictive business models"--Publisher's description.
Book Synopsis Python Data Science Handbook by : Jake VanderPlas
Download or read book Python Data Science Handbook written by Jake VanderPlas and published by "O'Reilly Media, Inc.". This book was released on 2016-11-21 with total page 743 pages. Available in PDF, EPUB and Kindle. Book excerpt: For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms
