Designing a HIPAA-Compliant Security Operations Center
Develop a comprehensive plan for building a HIPAA-compliant security operations center, designed to detect and respond to an increasing number of healthcare data breaches and events. Using risk analysis, assessment, and management data combined with knowledge of cybersecurity program maturity, this book gives you the tools you need to operationalize threat intelligence, vulnerability management, security monitoring, and incident response processes to effectively meet the challenges presented by healthcare's current threats. Healthcare entities are bombarded with data. Threat intelligence feeds, news updates, and messages come rapidly and in many forms such as email, podcasts, and more. New vulnerabilities are found every day in applications, operating systems, and databases while older vulnerabilities remain exploitable. Add in the number of dashboards, alerts, and data points each information security tool provides and security teams find themselves swimming in oceans of data and ...
Develop a comprehensive plan for building a HIPAA-compliant security operations center, designed to detect and respond to an increasing number of healthcare data breaches and events. Using risk analysis, assessment, and management data combined with knowledge of cybersecurity program maturity, this book gives you the tools you need to operationalize threat intelligence, vulnerability management, security monitoring, and incident response processes to effectively meet the challenges presented by healthcare's current threats. Healthcare entities are bombarded with data. Threat intelligence feeds, news updates, and messages come rapidly and in many forms such as email, podcasts, and more. New vulnerabilities are found every day in applications, operating systems, and databases while older vulnerabilities remain exploitable. Add in the number of dashboards, alerts, and data points each information security tool provides and security teams find themselves swimming in oceans of data and ...
Pro T-SQL 2019
Design and write simple and efficient T-SQL code in SQL Server 2019 and beyond. Writing T-SQL that pulls back correct results can be challenging. This book provides the help you need in writing T-SQL that performs fast and is easy to maintain. You also will learn how to implement version control, testing, and deployment strategies. Hands-on examples show modern T-SQL practices and provide straightforward explanations. Attention is given to selecting the right data types and objects when designing T-SQL solutions. Author Elizabeth Noble teaches you how to improve your T-SQL performance through good design practices that benefit programmers and ultimately the users of the applications. You will know the common pitfalls of writing T-SQL and how to avoid those pitfalls going forward. - Choose correct data types and database objects when designing T-SQL - Write T-SQL that searches data efficiently and uses hardware effectively - Implement source control and testing methods to stream ...
Design and write simple and efficient T-SQL code in SQL Server 2019 and beyond. Writing T-SQL that pulls back correct results can be challenging. This book provides the help you need in writing T-SQL that performs fast and is easy to maintain. You also will learn how to implement version control, testing, and deployment strategies. Hands-on examples show modern T-SQL practices and provide straightforward explanations. Attention is given to selecting the right data types and objects when designing T-SQL solutions. Author Elizabeth Noble teaches you how to improve your T-SQL performance through good design practices that benefit programmers and ultimately the users of the applications. You will know the common pitfalls of writing T-SQL and how to avoid those pitfalls going forward. - Choose correct data types and database objects when designing T-SQL - Write T-SQL that searches data efficiently and uses hardware effectively - Implement source control and testing methods to stream ...
Cassandra: The Definitive Guide, 3rd Edition
Imagine what you could do if scalability wasn't a problem. With this hands-on guide, you'll learn how the Cassandra database management system handles hundreds of terabytes of data while remaining highly available across multiple data centers. This third edition - updated for Cassandra 4.0 - provides the technical details and practical examples you need to put this database to work in a production environment. Authors Jeff Carpenter and Eben Hewitt demonstrate the advantages of Cassandra's nonrelational design, with special attention to data modeling. If you're a developer, DBA, or application architect looking to solve a database scaling issue or future-proof your application, this guide helps you harness Cassandra's speed and flexibility. Understand Cassandra's distributed and decentralized structure; Use the Cassandra Query Language (CQL) and cqlsh - the CQL shell; Create a working data model and compare it with an equivalent relational model; Develop sample applications using ...
Imagine what you could do if scalability wasn't a problem. With this hands-on guide, you'll learn how the Cassandra database management system handles hundreds of terabytes of data while remaining highly available across multiple data centers. This third edition - updated for Cassandra 4.0 - provides the technical details and practical examples you need to put this database to work in a production environment. Authors Jeff Carpenter and Eben Hewitt demonstrate the advantages of Cassandra's nonrelational design, with special attention to data modeling. If you're a developer, DBA, or application architect looking to solve a database scaling issue or future-proof your application, this guide helps you harness Cassandra's speed and flexibility. Understand Cassandra's distributed and decentralized structure; Use the Cassandra Query Language (CQL) and cqlsh - the CQL shell; Create a working data model and compare it with an equivalent relational model; Develop sample applications using ...
Distributed Tracing in Practice
Since most applications today are distributed in some fashion, monitoring their health and performance requires a new approach. Enter distributed tracing, a method of profiling and monitoring distributed applications - particularly those that use microservice architectures. There's just one problem: distributed tracing can be hard. But it doesn't have to be. With this guide, you'll learn what distributed tracing is and how to use it to understand the performance and operation of your software. Key players at LightStep and other organizations walk you through instrumenting your code for tracing, collecting the data that your instrumentation produces, and turning it into useful operational insights. If you want to implement distributed tracing, this book tells you what you need to know. You'll learn: The pieces of a distributed tracing deployment: instrumentation, data collection, and analysis; Best practices for instrumentation: methods for generating trace data from your services ...
Since most applications today are distributed in some fashion, monitoring their health and performance requires a new approach. Enter distributed tracing, a method of profiling and monitoring distributed applications - particularly those that use microservice architectures. There's just one problem: distributed tracing can be hard. But it doesn't have to be. With this guide, you'll learn what distributed tracing is and how to use it to understand the performance and operation of your software. Key players at LightStep and other organizations walk you through instrumenting your code for tracing, collecting the data that your instrumentation produces, and turning it into useful operational insights. If you want to implement distributed tracing, this book tells you what you need to know. You'll learn: The pieces of a distributed tracing deployment: instrumentation, data collection, and analysis; Best practices for instrumentation: methods for generating trace data from your services ...
Building an Anonymization Pipeline
How can you use data in a way that protects individual privacy but still provides useful and meaningful analytics? With this practical book, data architects and engineers will learn how to establish and integrate secure, repeatable anonymization processes into their data flows and analytics in a sustainable manner. Luk Arbuckle and Khaled El Emam from Privacy Analytics explore end-to-end solutions for anonymizing device and IoT data, based on collection models and use cases that address real business needs. These examples come from some of the most demanding data environments, such as healthcare, using approaches that have withstood the test of time. Create anonymization solutions diverse enough to cover a spectrum of use cases; Match your solutions to the data you use, the people you share it with, and your analysis goals; Build anonymization pipelines around various data collection models to cover different business needs; Generate an anonymized version of original data or use ...
How can you use data in a way that protects individual privacy but still provides useful and meaningful analytics? With this practical book, data architects and engineers will learn how to establish and integrate secure, repeatable anonymization processes into their data flows and analytics in a sustainable manner. Luk Arbuckle and Khaled El Emam from Privacy Analytics explore end-to-end solutions for anonymizing device and IoT data, based on collection models and use cases that address real business needs. These examples come from some of the most demanding data environments, such as healthcare, using approaches that have withstood the test of time. Create anonymization solutions diverse enough to cover a spectrum of use cases; Match your solutions to the data you use, the people you share it with, and your analysis goals; Build anonymization pipelines around various data collection models to cover different business needs; Generate an anonymized version of original data or use ...
The Data Science Design Manual
This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an "Introduction to Data Science" course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Pra ...
This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an "Introduction to Data Science" course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Pra ...
Thinking in Pandas
Understand and implement big data analysis solutions in pandas with an emphasis on performance. This book strengthens your intuition for working with pandas, the Python data analysis library, by exploring its underlying implementation and data structures. Thinking in Pandas introduces the topic of big data and demonstrates concepts by looking at exciting and impactful projects that pandas helped to solve. From there, you will learn to assess your own projects by size and type to see if pandas is the appropriate library for your needs. Author Hannah Stepanek explains how to load and normalize data in pandas efficiently, and reviews some of the most commonly used loaders and several of their most powerful options. You will then learn how to access and transform data efficiently, what methods to avoid, and when to employ more advanced performance techniques. You will also go over basic data access and munging in pandas and the intuitive dictionary syntax. Choosing the right DataFrame f ...
Understand and implement big data analysis solutions in pandas with an emphasis on performance. This book strengthens your intuition for working with pandas, the Python data analysis library, by exploring its underlying implementation and data structures. Thinking in Pandas introduces the topic of big data and demonstrates concepts by looking at exciting and impactful projects that pandas helped to solve. From there, you will learn to assess your own projects by size and type to see if pandas is the appropriate library for your needs. Author Hannah Stepanek explains how to load and normalize data in pandas efficiently, and reviews some of the most commonly used loaders and several of their most powerful options. You will then learn how to access and transform data efficiently, what methods to avoid, and when to employ more advanced performance techniques. You will also go over basic data access and munging in pandas and the intuitive dictionary syntax. Choosing the right DataFrame f ...
Practical DataOps
Gain a practical introduction to DataOps, a new discipline for delivering data science at scale inspired by practices at companies such as Facebook, Uber, LinkedIn, Twitter, and eBay. Organizations need more than the latest AI algorithms, hottest tools, and best people to turn data into insight-driven action and useful analytical data products. Processes and thinking employed to manage and use data in the 20th century are a bottleneck for working effectively with the variety of data and advanced analytical use cases that organizations have today. This book provides the approach and methods to ensure continuous rapid use of data to create analytical data products and steer decision making. Practical DataOps shows you how to optimize the data supply chain from diverse raw data sources to the final data product, whether the goal is a machine learning model or other data-orientated output. The book provides an approach to eliminate wasted effort and improve collaboration between data pr ...
Gain a practical introduction to DataOps, a new discipline for delivering data science at scale inspired by practices at companies such as Facebook, Uber, LinkedIn, Twitter, and eBay. Organizations need more than the latest AI algorithms, hottest tools, and best people to turn data into insight-driven action and useful analytical data products. Processes and thinking employed to manage and use data in the 20th century are a bottleneck for working effectively with the variety of data and advanced analytical use cases that organizations have today. This book provides the approach and methods to ensure continuous rapid use of data to create analytical data products and steer decision making. Practical DataOps shows you how to optimize the data supply chain from diverse raw data sources to the final data product, whether the goal is a machine learning model or other data-orientated output. The book provides an approach to eliminate wasted effort and improve collaboration between data pr ...
Angular for Material Design
Build Angular applications faster and better with TypeScript and Material Design. You will learn how to build a web interface and use Google's open source Angular Material library of ready-made and easy-to-use components. This book uses Angular with TypeScript (a superset to JavaScript) to enable use of data types and take advantage of programming constructs such as classes, interfaces, generic templates, and more. You also will utilize various Angular features, including data binding, components, services, etc. You will build a single page application with the help of routing capabilities available out of the box (Angular CLI) and interface with remote services over HTTP. ...
Build Angular applications faster and better with TypeScript and Material Design. You will learn how to build a web interface and use Google's open source Angular Material library of ready-made and easy-to-use components. This book uses Angular with TypeScript (a superset to JavaScript) to enable use of data types and take advantage of programming constructs such as classes, interfaces, generic templates, and more. You also will utilize various Angular features, including data binding, components, services, etc. You will build a single page application with the help of routing capabilities available out of the box (Angular CLI) and interface with remote services over HTTP. ...
Tableau Prep: Up & Running
For self-service data preparation, Tableau Prep is relatively easy to use - as long as you know how to clean and organize your datasets. Carl Allchin, from The Information Lab in London, gets you up to speed on Tableau Prep through a series of practical lessons that include methods for preparing, cleaning, automating, organizing, and outputting your datasets. Based on Allchin's popular blog, Preppin' Data, this practical guide takes you step-by-step through Tableau Prep's fundamentals. Self-service data preparation reduces the time it takes to complete data projects and improves the quality of your analyses. Discover how Tableau Prep helps you access your data and turn it into valuable information. ...
For self-service data preparation, Tableau Prep is relatively easy to use - as long as you know how to clean and organize your datasets. Carl Allchin, from The Information Lab in London, gets you up to speed on Tableau Prep through a series of practical lessons that include methods for preparing, cleaning, automating, organizing, and outputting your datasets. Based on Allchin's popular blog, Preppin' Data, this practical guide takes you step-by-step through Tableau Prep's fundamentals. Self-service data preparation reduces the time it takes to complete data projects and improves the quality of your analyses. Discover how Tableau Prep helps you access your data and turn it into valuable information. ...