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Database Reliability Engineering: Designing and Operating Resilient Database Systems

Database Reliability Engineering: Designing and Operating Resilient Database Systems

The infrastructure-as-code revolution in IT is also affecting database administration. with this practical book, developers, system administrators and junior to mid-level DBAs will learn how the modern practice of site reliability engineering applies to the craft of database architecture and operations. Authors Laine Campbell and Charity Majors provide a framework for professionals looking to join the ranks of todayís database reliability engineers (DBRE).
Youíll begin by exploring core operational concepts that DBREs need to master. Then youíll examine a wide range of database persistence options, including how to implement key technologies to provide resilient, scalable and performant data storage and retrieval. with a firm foundation in database reliability engineering, youíll be ready to dive into the architecture and operations of any modern database.
This book covers:

Service-level requirements and risk management
Building and evolving an architecture for operational visibility
Infrastructure engineering and infrastructure management
How to facilitate the release management process
Data storage, indexing and replication
Identifying datastore characteristics and best use cases
Datastore architectural components and data-driven architectures

Practical Tableau: 100 Tips, Tutorials, and Strategies from a Tableau Zen Master

Practical Tableau: 100 Tips, Tutorials, and Strategies from a Tableau Zen Master


Whether you have some experience with Tableau software or are just getting started, this manual goes beyond the basics to help you build compelling, interactive data visualization applications. Author Ryan Sleeper, one of the world and rsquo;s most qualified Tableau consultants, complements his web posts and instructional videos with this guide to give you a firm understanding of how to use Tableau to find valuable insights in data.

Over five sections, Sleeper and mdash;recognized as a Tableau Zen Master, Tableau Public Visualization of the Year author and Tableau Iron Viz Champion and mdash;provides visualization tips, tutorials and strategies to help you avoid the pitfalls and take your Tableau knowledge to the next level.

Practical Tableau sections include:


Fundamentals: get started with Tableau from the beginning

Chart types: use step-by-step tutorials to build a variety of charts in Tableau

Tips and tricks: learn innovative uses of parameters, color theory, how to make your Tableau workbooks run efficiently and more

Framework: explore the INSIGHT framework, a proprietary process for building Tableau dashboards

Storytelling: learn tangible tactics for storytelling with data, including specific and actionable tips you can implement immediately

Spark: The Definitive Guide

Spark: The Definitive Guide


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.

You'll explore the basic operations and common functions of Spark'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, Spark's scalable machine-learning library.


Get a gentle overview of big data and Spark

Learn about DataFrames, SQL and Datasets-Spark's core APIs-through worked examples

Dive into Spark'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, Spark';s stream-processing engine

Learn how you can apply MLlib to a variety of problems, including classification or recommendation

Introducing Data Science: Big Data, Machine Learning, and More, Using Python Tools

Introducing Data Science: Big Data, Machine Learning, and More, Using Python Tools

Introducing Data Science explains vital data science concepts and teaches you how to accomplish the fundamental tasks that occupy data scientists. You’ll explore data visualization, graph databases, the use of NoSQL, and the data science process. You’ll use the Python language and common Python libraries as you experience firsthand the challenges of dealing with data at scale. Discover how Python allows you to gain insights from data sets so big that they need to be stored on multiple machines, or from data moving so quickly that no single machine can handle it.

Big Data and Hadoop- Learn by Example

Big Data and Hadoop- Learn by Example

The book contains the latest trend in IT industry 'BigData and Hadoop'. It explains how big is 'Big Data' and why everybody is trying to implement this into their IT project. It includes research work on various topics, theoretical and practical approach, each component of the architecture is described along with current industry trends. Big Data and Hadoop have taken together are a new skill as per the industry standards. Readers will get a compact book along with the industry experience and would be a reference to help readers.

Python Machine Learning By Example

Python Machine Learning By Example

Take tiny steps to enter the big world of data science through this interesting guide About This Book * Learn the fundamentals of machine learning and build your own intelligent applications * Master the art of building your own machine learning systems with this example-based practical guide * Work with important classification and regression algorithms and other machine learning techniques Who This Book Is For This book is for anyone interested in entering the data science stream with machine learning. Basic familiarity with Python is assumed. What You Will Learn * Exploit the power of Python to handle data extraction, manipulation, and exploration techniques * Use Python to visualize data spread across multiple dimensions and extract useful features * Dive deep into the world of analytics to predict situations correctly * Implement machine learning classification and regression algorithms from scratch in Python * Be amazed to see the algorithms in action * Evaluate the performance of a machine learning model and optimize it * Solve interesting real-world problems using machine learning and Python as the journey unfolds In Detail Data science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning. This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Moving ahead, you will learn all the important concepts such as, exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation. With the help of various projects included, you will find it intriguing to acquire the mechanics of several important machine learning algorithms - they are no more obscure as they thought. Also, you will be guided step by step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and best practices of applying machine learning techniques. Through this book, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple language, Python. Interesting and easy-to-follow examples, to name some, news topic classification, spam email detection, online ad click-through prediction, stock prices forecast, will keep you glued till you reach your goal. Style and approach This book is an enticing journey that starts from the very basics and gradually picks up pace as the story unfolds. Each concept is first succinctly defined in the larger context of things, followed by a detailed explanation of their application. Every concept is explained with the help of a project that solves a real-world problem, and involves hands-on work-giving you a deep insight into the world of machine learning. With simple yet rich language-Python-you will understand and be able to implement the examples with ease.

Think Stats: Exploratory Data Analysis, Second Edition

Think Stats: Exploratory Data Analysis, Second Edition

All Indian Reprints of O'Reilly are printed in Grayscale.

If you know how to program, you have the skills to turn data into knowledge, using tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.

By working with a single case study throughout this thoroughly revised book, you’ll learn the entire process of exploratory data analysis—from collecting data and generating statistics to identifying patterns and testing hypotheses. You’ll explore distributions, rules of probability, visualizationand many other tools and concepts.

New chapters on regression, time series analysis, survival analysisand analytic methods will enrich your discoveries.

  • Develop an understanding of probability and statistics by writing and testing code
  • Run experiments to test statistical behavior, such as generating samples from several distributions
  • Use simulations to understand concepts that are hard to grasp mathematically
  • Import data from most sources with Python, rather than rely on data that’s cleaned and formatted for statistics tools
  • Use statistical inference to answer questions about real-world data

Practical Statistics for Data Scientists: 50 Essential Concepts

Practical Statistics for Data Scientists: 50 Essential Concepts

"Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse and gives you advice on what's important and what's not.
Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If youíre familiar with the R programming language and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.
With this book, youíll learn:

Why exploratory data analysis is a key preliminary step in data science
How random sampling can reduce bias and yield a higher quality dataset, even with big data
How the principles of experimental design yield definitive answers to questions
How to use regression to estimate outcomes and detect anomalies
Key classification techniques for predicting which categories a record belongs to
Statistical machine learning methods that ìlearnî from data
Unsupervised learning methods for extracting meaning from unlabeled data
"

Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists

Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists


Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. with this practical book, you and rsquo;ll learn techniques for extracting and transforming features and mdash;the numeric representations of raw dataóinto formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering.

Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn and Matplotlib are used in code examples.

You'll examine:


Feature engineering for numeric data: filtering, binning, scaling, log transforms and power transforms

Natural text techniques: bag-of-words, n-grams and phrase detection

Frequency-based filtering and feature scaling for eliminating uninformative features

Encoding techniques of categorical variables, including feature hashing and bin-counting

Model-based feature engineering with principal component analysis

The concept of model stacking, using k-means as a featurization technique

Image feature extraction with manual and deep-learning techniques

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