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Summary of Joe Reis & Matt Housley's Fundamentals of Data Engineering
Buy now to get the main key ideas from Joe Reis & Matt Housley's Fundamentals of Data Engineering In Fundamentals of Data Engineering (2022), data experts Joe Reis and Matt Housley provide a comprehensive overview of the field, from foundational concepts to advanced practices. They outline the data engineering lifecycle, with a detailed guide for planning and building systems that meet any organization’s needs. They explain how to evaluate and integrate the best technologies available, ensuring the architecture is robust and efficient. Their guide aims to help aspiring and current data engineers navigate the evolving landscape of the field, offering insights into best practices and approaches for managing data from its source to its final use.
Financial Data Engineering
Today, investment in financial technology and digital transformation is reshaping the financial landscape and generating many opportunities. Too often, however, engineers and professionals in financial institutions lack a practical and comprehensive understanding of the concepts, problems, techniques, and technologies necessary to build a modern, reliable, and scalable financial data infrastructure. This is where financial data engineering is needed. A data engineer developing a data infrastructure for a financial product possesses not only technical data engineering skills but also a solid understanding of financial domain-specific challenges, methodologies, data ecosystems, providers, form...
97 Things Every Data Engineer Should Know
Take advantage of the sky-high demand for data engineers today. With this in-depth book, current and aspiring engineers will learn powerful, real-world best practices for managing data big and small. Contributors from Google, Microsoft, IBM, Facebook, Databricks, and GitHub share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges. Edited by Tobias Macey from MIT Open Learning, 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. Projects include: Building pipelines Stream processing Data privacy and security Data governance and lineage Data storage and architecture Ecosystem of modern tools Data team makeup and culture Career advice.
Delta Lake: The Definitive Guide
Ready to simplify the process of building data lakehouses and data pipelines at scale? In this practical guide, learn how Delta Lake is helping data engineers, data scientists, and data analysts overcome key data reliability challenges with modern data engineering and management techniques. Authors Denny Lee, Tristen Wentling, Scott Haines, and Prashanth Babu (with contributions from Delta Lake maintainer R. Tyler Croy) share expert insights on all things Delta Lake--including how to run batch and streaming jobs concurrently and accelerate the usability of your data. You'll also uncover how ACID transactions bring reliability to data lakehouses at scale. This book helps you: Understand key data reliability challenges and how Delta Lake solves them Explain the critical role of Delta transaction logs as a single source of truth Learn the Delta Lake ecosystem with technologies like Apache Flink, Kafka, and Trino Architect data lakehouses with the medallion architecture Optimize Delta Lake performance with features like deletion vectors and liquid clustering
Fundamentals of Data Observability
Quickly detect, troubleshoot, and prevent a wide range of data issues through data observability, a set of best practices that enables data teams to gain greater visibility of data and its usage. If you're a data engineer, data architect, or machine learning engineer who depends on the quality of your data, this book shows you how to focus on the practical aspects of introducing data observability in your everyday work. Author Andy Petrella helps you build the right habits to identify and solve data issues, such as data drifts and poor quality, so you can stop their propagation in data applications, pipelines, and analytics. You'll learn ways to introduce data observability, including setting up a framework for generating and collecting all the information you need. Learn the core principles and benefits of data observability Use data observability to detect, troubleshoot, and prevent data issues Follow the book's recipes to implement observability in your data projects Use data observability to create a trustworthy communication framework with data consumers Learn how to educate your peers about the benefits of data observability
Blacks in Blackface
Published in 1980, Blacks in Blackface was the first and most extensive book up to that time to deal exclusively with every aspect of all-African American musical comedies performed on the stage between 1900 and 1940. An invaluable resource for scholars and historians focused on African American culture, this new edition features significantly revised, expanded, and new material. In Blacks in Blackface: A Sourcebook on Early Black Musical Shows, Henry T. Sampson provides an unprecedented wealth of information on legitimate musical comedies, including show synopses, casts, songs, and production credits. Sampson also recounts the struggles of African American performers and producers to overco...
Principles of Data Fabric
Apply Data Fabric solutions to automate Data Integration, Data Sharing, and Data Protection across disparate data sources using different data management styles. Purchase of the print or Kindle book includes a free PDF eBook Key Features Learn to design Data Fabric architecture effectively with your choice of tool Build and use a Data Fabric solution using DataOps and Data Mesh frameworks Find out how to build Data Integration, Data Governance, and Self-Service analytics architecture Book Description Data can be found everywhere, from cloud environments and relational and non-relational databases to data lakes, data warehouses, and data lakehouses. Data management practices can be standardiz...
The Complete Book of 1900s Broadway Musicals
Broadway musicals of the 1900s saw the emergence of George M. Cohan and his quintessentially American musical comedies which featured contemporary American stories, ragtime-flavored songs, and a tongue-in-cheek approach to musical comedy conventions. But when the Austrian import The Merry Widow opened in 1907, waltz-driven operettas became all the rage. In The Complete Book of 1900s Broadway Musicals, Dan Dietz surveys every single book musical that opened during the decade. Each musical has its own entry which features the following: Plot summary Cast members Creative team Song lists Opening and closing dates Number of performances Critical commentary Film adaptations, recordings, and published scripts, when applicable Numerous appendixes include a chronology of book musicals by season; chronology of revues; chronology of revivals of Gilbert and Sullivan operettas; a selected discography; filmography; published scripts; Black musicals; long and short runs; and musicals based on comic strips. The most comprehensive reference work on Broadway musicals of the 1900s, this book is an invaluable and significant resource for all scholars, historians, and fans of Broadway musicals.
The Enterprise Data Catalog
Combing the web is simple, but how do you search for data at work? It's difficult and time-consuming, and can sometimes seem impossible. This book introduces a practical solution: the data catalog. Data analysts, data scientists, and data engineers will learn how to create true data discovery in their organizations, making the catalog a key enabler for data-driven innovation and data governance. Author Ole Olesen-Bagneux explains the benefits of implementing a data catalog. You'll learn how to organize data for your catalog, search for what you need, and manage data within the catalog. Written from a data management perspective and from a library and information science perspective, this book helps you: Learn what a data catalog is and how it can help your organization Organize data and its sources into domains and describe them with metadata Search data using very simple-to-complex search techniques and learn to browse in domains, data lineage, and graphs Manage the data in your company via a data catalog Implement a data catalog in a way that exactly matches the strategic priorities of your organization Understand what the future has in store for data catalogs
Data Quality Engineering in Financial Services
Data quality will either make you or break you in the financial services industry. Missing prices, wrong market values, trading violations, client performance restatements, and incorrect regulatory filings can all lead to harsh penalties, lost clients, and financial disaster. This practical guide provides data analysts, data scientists, and data practitioners in financial services firms with the framework to apply manufacturing principles to financial data management, understand data dimensions, and engineer precise data quality tolerances at the datum level and integrate them into your data processing pipelines. You'll get invaluable advice on how to: Evaluate data dimensions and how they apply to different data types and use cases Determine data quality tolerances for your data quality specification Choose the points along the data processing pipeline where data quality should be assessed and measured Apply tailored data governance frameworks within a business or technical function or across an organization Precisely align data with applications and data processing pipelines And more
