Data Warehouse. In computing, a data warehouse, also known as an enterprise data warehouse, is a system used for reporting and data analysis, and is considered a core component of business intelligence. DWs are central repositories of integrated data from one or more disparate sources
#9 New Release Data Warehousing Books for Data Scientist
Throughout the book, you’ll explore relevant material gleaned from numerous books, papers, blog posts, and the source code of several open source databases. These resources are listed at the end of parts one and two. You’ll discover that the most significant distinctions among many modern databases reside in subsystems that determine how storage is organized and how data is distributed.
This book examines:
- Storage engines: Explore storage classification and taxonomy, and dive into B-Tree-based and immutable Log Structured storage engines, with differences and use-cases for each
- Storage building blocks: Learn how database files are organized to build efficient storage, using auxiliary data structures such as Page Cache, Buffer Pool and Write-Ahead Log
- Distributed systems: Learn step-by-step how nodes and processes connect and build complex communication patterns
- Database clusters: Which consistency models are commonly used by modern databases and how distributed storage systems achieve consistency
Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase.
Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challengesin time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly.
#3. Google BigQuery: The Definitive Guide: Data Warehousing, Analytics, and Machine Learning at Scale
Work with petabyte-scale datasets while building a collaborative, agile workplace in the process. This practical book is the canonical reference to Google BigQuery, the query engine that lets you conduct interactive analysis of large datasets. BigQuery enables enterprises to efficiently store, query, ingest, and learn from their data in a convenient framework. With this book, you’ll examine how to analyze data at scale to derive insights from large datasets efficiently.
Valliappa Lakshmanan, tech lead for Google Cloud Platform, and Jordan Tigani, engineering director for the BigQuery team, provide best practices for modern data warehousing within an autoscaled, serverless public cloud. Whether you want to explore parts of BigQuery you’re not familiar with or prefer to focus on specific tasks, this reference is indispensable.
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As of 2019, Microsoft’s Power BI is the leading analytics and business intelligence platform available on mobile applications, clouds, on-premise data gateway, data modeling applications, report authorizing applications, and other utilities. This book offers a comprehensive analysis of the powerful tools and features contained in Power BI’s arsenal. It includes the stepwise directions on how to start a Power BI project and how to share the project with a large number of users. As a reader, the book will get you familiarized with the basic concepts of Power BI and how its datasets, dashboards, and reports can be used to give insights and interactive experiences. This book will help you become conversant with management techniques and administration topics available on Power BI. With the knowledge acquired in the book, you will be able to utilize Power BI’s powerful features and carry out successful Power BI projects for your organization.
#5. Python Data Visualization: An Easy Introduction to Data Visualization in Python with Matplotlip, Pandas, and Seaborn
Data Visualization is the presentation of data in graphical format.In this tutorial for beginners, you will learn how to present data graphically with Python, Matplotlib, and Seaborn. If you need a short book to master data vizualisation from scratch, this guide is for you.
The author has discussed everything related to data visualization. You are first familiarized with the fundamentals of data visualization to help you know what it is and why it is of importance to any organization. The author has then discussed the various types of tools that can be used for data visualization. These tools include the basic, specialized and advanced ones. Practically, the author focuses on how to visualize data in the Python programming language. The process of plotting different types of data using different types of plots has been discussed. You will learn how to plot textual, numerical and geospatial data in Python using different libraries such as Pandas, Matplotlib, Seaborn and Folium. Python codes have been provided alongside images of the expected outputs and the corresponding code descriptions.
As more and more data floods into your company, you need to put it to work right away—and SQL is a vital tool for getting the job done. With the latest edition of this introductory guide, author Alan Beaulieu helps developers quickly get up to speed with SQL fundamentals for writing database applications, performing administrative tasks, and generating reports. You’ll find new chapters on SQL and big data, working with very large databases, and analytic functions.
Each chapter presents a self-contained lesson on a key SQL concept or technique using numerous illustrations and annotated examples. Exercises at the end of each chapter let you practice the skills you learn. Knowledge of SQL is a must for interacting with data. With Learning SQL, you’ll quickly learn how to put the power and flexibility of this language to work.
Data Science Design Patterns brings together several dozen proven patterns for building successful decision-support and decision-automation systems in the enterprise. Like Martin Fowler’s classic Patterns of Enterprise Application Architecture, it helps you rapidly hone in on proven solutions to common problems, leveraging the hard-won expertise of those who have come before you.
Todd Morley helps you draw upon and integrate diverse domains including statistics, machine learning, information retrieval, compression, optimization, and other areas of software development and business consulting. His patterns address many common challenges, including categorization, prediction, optimization, testing, and human factors. They link directly to key goals for data science and analytics: increasing revenue, decreasing costs, reducing risk, choosing strategies, and making key decisions.
#8. Apache Spark Projects: A complete walk-through of Apache Spark’s core capabilities with 7 real-world Big Data projects
Apache Spark is one of the most popular Big Data tools used in a plethora of industries today right from E-commerce, Entertainment to Travel and Retail Industry. This book demonstrates how to leverage the capabilities of Apache Spark and use them in practical projects using real-world scenarios.
The book begins with a quick introduction to all the components of the Spark ecosystem and later teach the readers how to use them in real-world scenarios. It demonstrates how to use each component of Apache Spark ecosystem, i.e. Spark SQL, Spark Streaming, Spark Mllib, PySpark to build an efficient, end to end Big Data processing pipeline. Some of the projects that are covered such as Sales forecasting using SparkR and recommendation engine using PySpark. The readers will learn about the different libraries like Mlib, Spark SQL, GraphX and Spark Streaming. Throughout the book, the readers will gain knowledge about the different components of the Spark ecosystem and will also be able to manage their big data pipelines using Apache Spark.
With an emphasis on clarity, style, and performance, author J.T. Wolohan expertly guides you through implementing a functionally-influenced approach to Python coding. You’ll get familiar with Python’s functional built-ins like the functools operator and itertools modules, as well as the toolz library.
Mastering Large Datasets teaches you to write easily readable, easily scalable Python code that can efficiently process large volumes of structured and unstructured data. By the end of this comprehensive guide, you’ll have a solid grasp on the tools and methods that will take your code beyond the laptop and your data science career to the next level!
Data warehousing is the process of constructing and using a data warehouse. A data warehouse is constructed by integrating data from multiple heterogeneous sources that support analytical reporting, structured and/or ad hoc queries, and decision making.