Showing results by publisher "Ascent Audio" in Data Science

Author Martin Kleppmann helps you navigate the diverse data landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications.

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.

Data projects are an intrinsic part of an organization's technical ecosystem, but data engineers in many companies continue to work on problems that others have already solved. This hands-on guide shows you how to provide valuable data by focusing on various aspects of data engineering, including data ingestion, data quality, idempotency, and more.

In this comprehensive study guide, you will build a strong foundation in all topics covered on the certification exam, including the Databricks Lakehouse and its tools and benefits.

Organizations have more data at their fingertips than ever, and their ability to put that data to productive use should be a key source of sustainable competitive advantage. Yet, business leaders looking to tap into a steady and manageable stream of "actionable insights" often, instead, get blasted with a deluge of dashboards, chart-filled slide decks, and opaque machine learning jargon that leaves them asking, "So what?" Analytics the Right Way is a guide for these leaders.

Data fabric, data lakehouse, and data mesh have recently appeared as viable alternatives to the modern data warehouse. These new architectures have solid benefits, but they're also surrounded by a lot of hyperbole and confusion. This practical book provides a guided tour of these architectures to help data professionals understand the pros and cons of each.

When it comes to choosing, using, and maintaining a database, understanding its internals is essential. But with so many distributed databases and tools available today, it's often difficult to understand what each one offers. With this practical guide, Alex Petrov guides developers through the concepts behind modern database and storage engine internals.

In Fusion Strategy, world-renowned innovation guru Vijay Govindarajan and digital strategy expert Venkat Venkataraman offer a first-of-its-kind playbook that will help industrial companies combine what they do best-create physical products-with what digitals do best-use algorithms and AI to parse expansive, interconnected datasets-to make strategic connections that would otherwise be impossible.

From technology, media, and retail to sport teams, government agencies, and financial institutions, learn the actual strategies and processes being used to learn about customers, improve manufacturing, spur innovation, improve safety, and so much more. Organized for easy dip-in navigation, each chapter follows the same structure to give you the information you need quickly. For each company profiled, learn what data was used, what problem it solved, and the processes put in place to make it practical, as well as the technical details, challenges, and lessons learned from each unique scenario.

As data floods into your company, you need to put it to work right away—and SQL is the best tool for the job. With the latest edition of this introductory guide, author Alan Beaulieu helps developers 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, analytic functions, and working with very large databases.

Ransomware attacks are no longer a question of if—it's a matter of when. With hackers increasingly targeting backup and disaster recovery (DR) systems, organizations need more than prevention strategies; they need a battle-tested plan for minimizing damage, forensically determining what's happened and restoring your environment without paying the ransom. Renowned experts W. Curtis Preston and Dr. Mike Saylor offer a comprehensive guide to protecting critical systems and responding effectively when the worst happens.

Phishing Dark Waters addresses the growing and continuing scourge of phishing emails, and provides actionable defensive techniques and tools to help you steer clear of malicious emails. Phishing is analyzed from the viewpoint of human decision-making and the impact of deliberate influence and manipulation on the recipient. With expert guidance, this book provides insight into the financial, corporate espionage, nation state, and identity theft goals of the attackers, and teaches you how to spot a spoofed e-mail or cloned website.

While the world is transfixed by bitcoin mania, your competitors are tuning out the noise and making strategic bets on blockchain. Your rivals are effortlessly tracking every last link in their supply chains. They're making bureaucratic paper trails obsolete while keeping their customers' data safer and discovering new ways to use this next foundational technology to sustain their competitive advantage.

Take advantage of today's sky-high demand for data engineers. With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Contributors from notable companies including Twitter, Google, Stitch Fix, Microsoft, Capital One, and LinkedIn share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges. Edited by Tobias Macey, this book presents ninety-seven concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data.

Celebrated data-driven growth leader Lillian Pierson delivers a masterclass in developing custom strategies to harness the full potential of data and AI within your organization. This book offers a clear, actionable roadmap for leveraging your company's data and technology assets to drive significant, reliable growth.

Data analytics offer an incredible opportunity to predict the future, engage with new technological advancements, and gain valuable insights about your business. But unless you're staying on top of the cutting edge of analytics, your company is wasting that potential-and your competitors will be gaining speed while you fall behind. These articles by experts and consultants will help you understand today's essential thinking about what data analytics are capable of and how to adopt their use in your organization.

When "metadata" became breaking news, appearing in stories about surveillance by the National Security Agency, many members of the public encountered this once-obscure term from information science for the first time. Should people be reassured that the NSA was "only" collecting metadata about phone calls - information about the caller, the recipient, the time, the duration, the location - and not recordings of the conversations themselves? Or does phone call metadata reveal more than it seems?

Big data - the enormous amount of data that is created as virtually every movement, transaction, and choice we make becomes digitized - is revolutionizing business. Offering real-world insight and explanations, this audiobook provides a roadmap for organizations looking to develop a profitable big data strategy...and reveals why it's not something they can leave to the I.T. department. Think Bigger is an essential resource for anyone who wants to ensure that their company isn't left in the dust.

Data science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project's success—and is absolutely essential for those working with production code. This practical book bridges the gap between data science and software engineering, and clearly explains how to apply the best practices from software engineering to data science.

Between 1959 and 1989, Soviet scientists and officials made numerous attempts to network their nation - to construct a nationwide computer network. None of these attempts succeeded, and the enterprise had been abandoned by the time the Soviet Union fell apart. Meanwhile, ARPANET, the American precursor to the Internet, went online in 1969. Why did the Soviet network, with top-level scientists and patriotic incentives, fail while the American network succeeded? Find out.