Databaser

This is a first-principles-based, practical introduction to the fundamentals of data science aimed at the mathematically-comfortable reader with some programming skills.

Comprehensive hands-on reference focuses on advanced querying techniques using Transact-SQL (T-SQL). For database developers & adminstrators. Includes code & table examples.

This book is for executives, practitioners, and project managers who are tasked with the movement of data from old systems to a new repository. It uses a series of steps developed in real life situations that will get the reader from an empty new system to one that is populated, working and backed by the user population. This new edition of the primary text on the subject is updated to take account of changes in technology and the maturing of the market for Data Migration services. The most recent figures suggest that nearly 40% of Data Migration projects are over time, over budget or fail entirely. Using the proven methodology in Practical Data Migration will vastly increase the chances of achieving on time, on budget, and zero defect migrations.

For database systems courses in Computer Science This book introduces the fundamental concepts necessary for designing, using, and implementing database systems and database applications. Our presentation stresses the fundamentals of database modeling and design, the languages and models provided by the database management systems, and database system implementation techniques.The book is meant to be used as a textbook for a one- or two-semester course in database systems at the junior, senior, or graduate level, and as a reference book. The goal is to provide an in-depth and up-to-date presentation of the most important aspects of database systems and applications, and related technologies. It is assumed that readers are familiar with elementary programming and data-structuring concepts and that they have had some exposure to the basics of computer organization.

Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications.Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. Youll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes.Train models in computer vision, natural language processing, tabular data, and collaborative filteringLearn the latest deep learning techniques that matter most in practiceImprove accuracy, speed, and reliability by understanding how deep learning models workDiscover how to turn your models into web applicationsImplement deep learning algorithms from scratchConsider the ethical implications of your workGain insight from the foreword by PyTorch cofounder, Soumith Chintala

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. Youll 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 datasetCleaning data and dealing with missing dataExploratory data analysisCommon preprocessing steps using sample dataSelecting features useful to the modelModel selectionMetrics and classification evaluationRegression examples using k-nearest neighbor, decision trees, boosting, and moreMetrics for regression evaluationClusteringDimensionality reductionScikit-learn pipelines

Machine learning is an intimidating subject until you know the fundamentals. If you understand basic coding concepts, this introductory guide will help you gain a solid foundation in machine learning principles.

Exam Ref 70-761 Querying Data with Transact-SQL offers professional-level preparation that helps candidates maximise their exam performance and sharpen their skills on the job. It focuses on the specific areas of expertise modern database professionals need to succeed with T-SQL database queries. Exam Ref 70-762 Developing SQL Databases offers professional-level preparation that helps candidates maximise their exam performance and sharpen their skills on the job. It focuses on the specific areas of expertise modern database professionals need to succeed with SQL database development. These are the two exams necessary for the MCSA certification in SQL Server 2016 Database Development.

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.

Data has never mattered more. Our lives are increasingly shaped by it and how it is defined, collected and used. But who counts in the collection, analysis and application of data?This important book is the first to look at queer data - defined as data relating to gender, sex, sexual orientation and trans identity/history. The author shows us how current data practices reflect an incomplete account of LGBTQ lives and helps us understand how data biases are used to delegitimise the everyday experiences of queer people.Guyan demonstrates why it is important to understand, collect and analyse queer data, the benefits and challenges involved in doing so, and how we might better use queer data in our work. Arming us with the tools for action, this book shows how greater knowledge about queer identities is instrumental in informing decisions about resource allocation, changes to legislation, access to services, representation and visibility.

Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle.Authors Joe Reis and Matt Housley walk you through the data engineering lifecycle and show you how to stitch together a variety of cloud technologies to serve the needs of downstream data consumers. You'll understand how to apply the concepts of data generation, ingestion, orchestration, transformation, storage, and governance that are critical in any data environment regardless of the underlying technology.This book will help you:Get a concise overview of the entire data engineering landscapeAssess data engineering problems using an end-to-end framework of best practicesCut through marketing hype when choosing data technologies, architecture, and processesUse the data engineering lifecycle to design and build a robust architectureIncorporate data governance and security across the data engineering lifecycle

This practical book provides a comprehensive overview of troubleshooting and performance tuning best practices for Microsoft SQL Server. Database engineers, including database developers and administrators, will learn how to identify performance issues, troubleshoot the system in a holistic fashion, and properly prioritize tuning efforts to attain the best system performance possible.Author Dmitri Korotkevitch, Microsoft Data Platform MVP and Microsoft Certified Master (MCM), explains the interdependencies between SQL Server database components. You'll learn how to quickly diagnose your system and discover the root cause of any issue. Techniques in this book are compatible with all versions of SQL Server and cover both on-premises and cloud-based SQL Server installations.Discover how performance issues present themselves in SQL ServerLearn about SQL Server diagnostic tools, methods, and technologiesPerform health checks on SQL Server installationsLearn the dependencies between SQL Server componentsTune SQL Server to improve performance and reduce bottlenecksDetect poorly optimized queries and inefficiencies in query execution plansFind inefficient indexes and common database design issuesUse these techniques with Microsoft Azure SQL databases, Azure SQL Managed Instances, and Amazon RDS for SQL Server

Get going with tidymodels, a collection of R packages for modeling and machine learning. Whether you're just starting out or have years of experience with modeling, this practical introduction shows data analysts, business analysts, and data scientists how the tidymodels framework offers a consistent, flexible approach for your work.RStudio engineers Max Kuhn and Julia Silge demonstrate ways to create models by focusing on an R dialect called the tidyverse. Software that adopts tidyverse principles shares both a high-level design philosophy and low-level grammar and data structures, so learning one piece of the ecosystem makes it easier to learn the next. You'll understand why the tidymodels framework has been built to be used by a broad range of people.With this book, you will:Learn the steps necessary to build a model from beginning to endUnderstand how to use different modeling and feature engineering approaches fluentlyExamine the options for avoiding common pitfalls of modeling, such as overfittingLearn practical methods to prepare your data for modelingTune models for optimal performanceUse good statistical practices to compare, evaluate, and choose among models

Updated for Python 3.10 and pandas 1.4, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively.

Snowflake's ability to eliminate data silos and run workloads from a single platform creates opportunities to democratize data analytics, allowing users within an organization to make data-driven decisions. This clear, comprehensive guide will show you how to build integrated data applications and develop new revenue streams based on data.

A SEARCH AND FIND BOOK

This book covers the need for good business modeling design and how you can achieve it through DAX. It helps analysts get the most out of Microsoft BI tools such as Power BI using DAX, ensuring that they get the most effective results from running their analyses.

The data lakehouse is the next generation of the data warehouse and data lake, designed to meet today's complex and ever-changing analytics, machine learning, and data science requirements.Learn about the features and architecture of the data lakehouse, along with its powerful analytical infrastructure. Appreciate how the universal common connector blends structured, textual, analog, and IoT data. Maintain the lakehouse for future generations through Data Lakehouse Housekeeping and Data Future-proofing. Know how to incorporate the lakehouse into an existing data governance strategy. Incorporate data catalogs, data lineage tools, and open source software into your architecture to ensure your data scientists, analysts, and end users live happily ever after.

The Azure Databricks Cookbook shows you how to work with the latest as well as older versions of Apache Spark and integrate with various Azure resources for orchestrating, deploying, and monitoring big data solutions. You'll use Azure Databricks to build end-to-end solutions and address challenges in securing, productionizing, and monitoring them.