Informatyka

Data Engineering with Scala and Spark. Build streaming and batch pipelines that process massive amounts of data using Scala

Eric Tome, Rupam Bhattacharjee, David Radford

Most data engineers know that performance issues in a distributed computing environment can easily lead to issues impacting the overall efficiency and effectiveness of data engineering tasks. While Python remains a popular choice for data engineering due to its ease of use, Scala shines in scenarios where the performance of distributed data processing is paramount.This book will teach you how to leverage the Scala programming language on the Spark framework and use the latest cloud technologies to build continuous and triggered data pipelines. You’ll do this by setting up a data engineering environment for local development and scalable distributed cloud deployments using data engineering best practices, test-driven development, and CI/CD. You’ll also get to grips with DataFrame API, Dataset API, and Spark SQL API and its use. Data profiling and quality in Scala will also be covered, alongside techniques for orchestrating and performance tuning your end-to-end pipelines to deliver data to your end users. By the end of this book, you will be able to build streaming and batch data pipelines using Scala while following software engineering best practices.

Data Governance Handbook. A practical approach to building trust in data

Wendy S. Batchelder

2.5 quintillion bytes! This is the amount of data being generated every single day across the globe. As this number continues to grow, understanding and managing data becomes more complex. Data professionals know that it’s their responsibility to navigate this complexity and ensure effective governance, empowering businesses with the right data, at the right time, and with the right controls.If you are a data professional, this book will equip you with valuable guidance to conquer data governance complexities with ease. Written by a three-time chief data officer in global Fortune 500 companies, the Data Governance Handbook is an exhaustive guide to understanding data governance, its key components, and how to successfully position solutions in a way that translates into tangible business outcomes.By the end, you’ll be able to successfully pitch and gain support for your data governance program, demonstrating tangible outcomes that resonate with key stakeholders.*Email sign-up and proof of purchase required

Data Ingestion with Python Cookbook. A practical guide to ingesting, monitoring, and identifying errors in the data ingestion process

Gláucia Esppenchutz

Data Ingestion with Python Cookbook offers a practical approach to designing and implementing data ingestion pipelines. It presents real-world examples with the most widely recognized open source tools on the market to answer commonly asked questions and overcome challenges.You’ll be introduced to designing and working with or without data schemas, as well as creating monitored pipelines with Airflow and data observability principles, all while following industry best practices. The book also addresses challenges associated with reading different data sources and data formats. As you progress through the book, you’ll gain a broader understanding of error logging best practices, troubleshooting techniques, data orchestration, monitoring, and storing logs for further consultation.By the end of the book, you’ll have a fully automated set that enables you to start ingesting and monitoring your data pipeline effortlessly, facilitating seamless integration with subsequent stages of the ETL process.

Data Lake Development with Big Data. Explore architectural approaches to building Data Lakes that ingest, index, manage, and analyze massive amounts of data using Big Data technologies

Pradeep Pasupuleti, Beulah Salome Purra

A Data Lake is a highly scalable platform for storing huge volumes of multistructured data from disparate sources with centralized data management services. This book explores the potential of Data Lakes and explores architectural approaches to building data lakes that ingest, index, manage, and analyze massive amounts of data using batch and real-time processing frameworks. It guides you on how to go about building a Data Lake that is managed by Hadoop and accessed as required by other Big Data applications.This book will guide readers (using best practices) in developing Data Lake's capabilities. It will focus on architect data governance, security, data quality, data lineage tracking, metadata management, and semantic data tagging. By the end of this book, you will have a good understanding of building a Data Lake for Big Data.

Data Lake for Enterprises. Lambda Architecture for building enterprise data systems

Vivek Mishra, Tomcy John, Pankaj Misra

The term Data Lake has recently emerged as a prominent term in the big data industry. Data scientists can make use of it in deriving meaningful insights that can be used by businesses to redefine or transform the way they operate. Lambda architecture is also emerging as one of the very eminent patterns in the big data landscape, as it not only helps to derive useful information from historical data but also correlates real-time data to enable business to take critical decisions. This book tries to bring these two important aspects — data lake and lambda architecture—together.This book is divided into three main sections. The first introduces you to the concept of data lakes, the importance of data lakes in enterprises, and getting you up-to-speed with the Lambda architecture. The second section delves into the principal components of building a data lake using the Lambda architecture. It introduces you to popular big data technologies such as Apache Hadoop, Spark, Sqoop, Flume, and ElasticSearch. The third section is a highly practical demonstration of putting it all together, and shows you how an enterprise data lake can be implemented, along with several real-world use-cases. It also shows you how other peripheral components can be added to the lake to make it more efficient.By the end of this book, you will be able to choose the right big data technologies using the lambda architectural patterns to build your enterprise data lake.

Data Literacy in Practice. A complete guide to data literacy and making smarter decisions with data through intelligent actions

Angelika Klidas, Kevin Hanegan

Data is more than a mere commodity in our digital world. It is the ebb and flow of our modern existence. Individuals, teams, and enterprises working with data can unlock a new realm of possibilities. And the resultant agility, growth, and inevitable success have one origin—data literacy.This comprehensive guide is written by two data literacy pioneers, each with a thorough footprint within the data and analytics commercial world and lectures at top universities in the US and the Netherlands. Complete with best practices, practical models, and real-world examples, Data Literacy in Practice will help you start making your data work for you by building your understanding of data literacy basics and accelerating your journey to independently uncovering insights.You’ll learn the four-pillar model that underpins all data and analytics and explore concepts such as measuring data quality, setting up a pragmatic data management environment, choosing the right graphs for your readers, and questioning your insights.By the end of the book, you'll be equipped with a combination of skills and mindset as well as with tools and frameworks that will allow you to find insights and meaning within your data for data-informed decision making.

Data Management Strategy at Microsoft. Best practices from a tech giant's decade-long data transformation journey

Aleksejs Plotnikovs

Microsoft pioneered data innovation and investment ahead of many in the industry, setting a remarkable standard for data maturity. Written by a data leader with over 15 years of experience following Microsoft’s data journey, this book delves into every crucial aspect of this journey, including change management, aligning with business needs, enhancing data value, and cultivating a data-driven culture.This book emphasizes that success in a data-driven enterprise goes beyond relying solely on modern technology and highlights the importance of prioritizing genuine business needs to propel necessary modernizations through change management practices. You’ll see how data-driven innovation does not solely reside within central IT engineering teams but also among the data's business owners who rely on data daily for their operational needs. This guide empower these professionals with clean, easily discoverable, and business-ready data, marking a significant breakthrough in how data is perceived and utilized throughout an enterprise. You’ll also discover advanced techniques to nurture the value of data as unique intellectual property, and differentiate your organization with the power of data.Its storytelling approach and summary of essential insights at the end of each chapter make this book invaluable for business and data leaders to advocate for crucial data investments.

Data Mining. Eksploracja danych w sieciach społecznościowych. Wydanie III

Matthew A. Russell, Mikhail Klassen

Internetu nie można rozważać wyłącznie jako tworu techniki. Powstanie tej sieci doprowadziło do rozwoju różnych zjawisk społecznych. Z tej perspektywy na szczególną uwagę zasługują media społecznościowe. Są źródłem informacji, które, właściwie spożytkowane, mogą przynieść niezły dochód. Mogą też dać odpowiedzi na wiele pytań zadawanych przez naukowców z różnych branż. Sama eksploracja tych danych przynosi sporo satysfakcji i radości. Zaskakujące przy tym jest to, że przygotowanie zestawu potrzebnych narzędzi i nauka posługiwania się nimi zabiera naprawdę niewiele czasu i nie wymaga specjalnych talentów! To trzecie, zaktualizowane wydanie popularnego podręcznika dla osób, które chcą zająć się wydobywaniem danych z sieci społecznościowych. Uwzględniono tu zmiany interfejsów API wprowadzone do poszczególnych platform i dodano rozdział o eksploracji Instagrama. Dowiesz się, jak dzięki danym z mediów społecznościowych określić sieć powiązań użytkowników, zorientować się, kto o czym mówi i gdzie się znajduje. Treść bogato zilustrowano przykładami kodu w Pythonie, a także plikami Jupyter Notebook lub kontenerów Dockera. Ciekawym elementem książki jest zbiór receptur dotyczących rozwiązywania konkretnych problemów z Twitterem. W tej książce między innymi: wprowadzenie do świata mediów społecznościowych przybliżenie bogactwa danych zawartych w mediach społecznościowych eksploracja danych za pomocą narzędzi Pythona 3 zaawansowane techniki eksploracji danych, w tym współczynniki TFIDF, podobieństwo kosinusów i rozpoznawanie obrazów tworzenie wizualizacji pozyskanych danych Jakie informacje dziś znajdziesz dzięki danym z Facebooka?