Scalable Data Analytics with Azure Data Explorer. Modern ways to query, analyze, and perform real-time data analysis on large volumes of data

Jason Myerscough, Arunee Singhchawla

Azure Data Explorer (ADX) enables developers and data scientists to make data-driven business decisions. This book will help you rapidly explore and query your data at scale and secure your ADX clusters.The book begins by introducing you to ADX, its architecture, core features, and benefits. You'll learn how to securely deploy ADX instances and navigate through the ADX Web UI, cover data ingestion, and discover how to query and visualize your data using the powerful Kusto Query Language (KQL). Next, you'll get to grips with KQL operators and functions to efficiently query and explore your data, as well as perform time series analysis and search for anomalies and trends in your data. As you progress through the chapters, you'll explore advanced ADX topics, including deploying your ADX instances using Infrastructure as Code (IaC). The book also shows you how to manage your cluster performance and monthly ADX costs by handling cluster scaling and data retention periods. Finally, you'll understand how to secure your ADX environment by restricting access with best practices for improving your KQL query performance.By the end of this Azure book, you'll be able to securely deploy your own ADX instance, ingest data from multiple sources, rapidly query your data, and produce reports with KQL and Power BI.

Scalable Data Streaming with Amazon Kinesis. Design and secure highly available, cost-effective data streaming applications with Amazon Kinesis

Tarik Makota, Brian Maguire, Danny Gagne, Rajeev Chakrabarti

Amazon Kinesis is a collection of secure, serverless, durable, and highly available purpose-built data streaming services. This data streaming service provides APIs and client SDKs that enable you to produce and consume data at scale.Scalable Data Streaming with Amazon Kinesis begins with a quick overview of the core concepts of data streams, along with the essentials of the AWS Kinesis landscape. You'll then explore the requirements of the use case shown through the book to help you get started and cover the key pain points encountered in the data stream life cycle. As you advance, you'll get to grips with the architectural components of Kinesis, understand how they are configured to build data pipelines, and delve into the applications that connect to them for consumption and processing. You'll also build a Kinesis data pipeline from scratch and learn how to implement and apply practical solutions. Moving on, you'll learn how to configure Kinesis on a cloud platform. Finally, you’ll learn how other AWS services can be integrated into Kinesis. These services include Redshift, Dynamo Database, AWS S3, Elastic Search, and third-party applications such as Splunk.By the end of this AWS book, you’ll be able to build and deploy your own Kinesis data pipelines with Kinesis Data Streams (KDS), Kinesis Data Firehose (KFH), Kinesis Video Streams (KVS), and Kinesis Data Analytics (KDA).

Scientific Computing with Python. High-performance scientific computing with NumPy, SciPy, and pandas - Second Edition

Claus Führer, Jan Erik Solem, Olivier Verdier

Python has tremendous potential within the scientific computing domain. This updated edition of Scientific Computing with Python features new chapters on graphical user interfaces, efficient data processing, and parallel computing to help you perform mathematical and scientific computing efficiently using Python.This book will help you to explore new Python syntax features and create different models using scientific computing principles. The book presents Python alongside mathematical applications and demonstrates how to apply Python concepts in computing with the help of examples involving Python 3.8. You'll use pandas for basic data analysis to understand the modern needs of scientific computing, and cover data module improvements and built-in features. You'll also explore numerical computation modules such as NumPy and SciPy, which enable fast access to highly efficient numerical algorithms. By learning to use the plotting module Matplotlib, you will be able to represent your computational results in talks and publications. A special chapter is devoted to SymPy, a tool for bridging symbolic and numerical computations.By the end of this Python book, you'll have gained a solid understanding of task automation and how to implement and test mathematical algorithms within the realm of scientific computing.

scikit-learn Cookbook. Over 80 recipes for machine learning in Python with scikit-learn - Second Edition

Julian Avila, Trent Hauck

Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility, and within the Python data space, scikit-learn is the unequivocal choice for machine learning. This book includes walk throughs and solutions to the common as well as the not-so-common problems in machine learning, and how scikit-learn can be leveraged to perform various machine learning tasks effectively.The second edition begins with taking you through recipes on evaluating the statistical properties of data and generates synthetic data for machine learning modelling. As you progress through the chapters, you will comes across recipes that will teach you to implement techniques like data pre-processing, linear regression, logistic regression, K-NN, Naïve Bayes, classification, decision trees, Ensembles and much more. Furthermore, you’ll learn to optimize your models with multi-class classification, cross validation, model evaluation and dive deeper in to implementing deep learning with scikit-learn. Along with covering the enhanced features on model section, API and new features like classifiers, regressors and estimators the book also contains recipes on evaluating and fine-tuning the performance of your model. By the end of this book, you will have explored plethora of features offered by scikit-learn for Python to solve any machine learning problem you come across.

SciPy Recipes. A cookbook with over 110 proven recipes for performing mathematical and scientific computations

Luiz Felipe Martins, V Kishore Ayyadevara, Ruben Oliva Ramos

With the SciPy Stack, you get the power to effectively process, manipulate, and visualize your data using the popular Python language. Utilizing SciPy correctly can sometimes be a very tricky proposition. This book provides the right techniques so you can use SciPy to perform different data science tasks with ease.This book includes hands-on recipes for using the different components of the SciPy Stack such as NumPy, SciPy, matplotlib, and pandas, among others. You will use these libraries to solve real-world problems in linear algebra, numerical analysis, data visualization, and much more. The recipes included in the book will ensure you get a practical understanding not only of how a particular feature in SciPy Stack works, but also of its application to real-world problems. The independent nature of the recipes also ensure that you can pick up any one and learn about a particular feature of SciPy without reading through the other recipes, thus making the book a very handy and useful guide.

Secret Recipes of the Python Ninja. Over 70 recipes that uncover powerful programming tactics in Python

Cody Jackson, Steven F. Lott

This book covers the unexplored secrets of Python, delve into its depths, and uncover its mysteries.You’ll unearth secrets related to the implementation of the standard library, by looking at how modules actually work. You’ll understand the implementation of collections, decimals, and fraction modules. If you haven’t used decorators, coroutines, and generator functions much before, as you make your way through the recipes, you’ll learn what you’ve been missing out on. We’ll cover internal special methods in detail, so you understand what they are and how they can be used to improve the engineering decisions you make. Next, you’ll explore the CPython interpreter, which is a treasure trove of secret hacks that not many programmers are aware of. We’ll take you through the depths of the PyPy project, where you’ll come across several exciting ways that you can improve speed and concurrency. Finally, we’ll take time to explore the PEPs of the latest versions to discover some interesting hacks.

Security Automation with Python. Practical Python solutions for automating and scaling security operations

Corey Charles Sr., Frank McMahon

Designed to address the most common pain point for security teams—scalability—Security Automation with Python leverages the author’s years of experience in vulnerability management to provide you with actionable guidance on automating security workflows to streamline your operations and improve your organization’s overall security posture.What makes this book stand out is its hands-on approach. You won’t just learn theoretical concepts—you’ll apply Python-based automation techniques directly to real-world scenarios. Whether you're automating vulnerability scans, managing firewall rules, or responding to security incidents, this book provides clear examples and use cases, breaking down complex topics into easily digestible steps. With libraries like Paramiko, Requests, and PyAutoGUI, you’ll automate everything from network scanning and threat intelligence gathering to system patching and alert management. Plus, this book focuses heavily on practical tips for error handling, scaling automation workflows, and integrating Python scripts into larger security infrastructures.By the end of this book, you'll have developed a set of highly valuable skills, from creating custom automation scripts to deploying them in production environments, and completed projects that can be immediately put to use in your organization.

Serverless Analytics with Amazon Athena. Query structured, unstructured, or semi-structured data in seconds without setting up any infrastructure

Anthony Virtuoso, Mert Turkay Hocanin, Aaron Wishnick, Rahul Pathak

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using SQL, without needing to manage any infrastructure.This book begins with an overview of the serverless analytics experience offered by Athena and teaches you how to build and tune an S3 Data Lake using Athena, including how to structure your tables using open-source file formats like Parquet. You’ll learn how to build, secure, and connect to a data lake with Athena and Lake Formation. Next, you’ll cover key tasks such as ad hoc data analysis, working with ETL pipelines, monitoring and alerting KPI breaches using CloudWatch Metrics, running customizable connectors with AWS Lambda, and more. Moving on, you’ll work through easy integrations, troubleshooting and tuning common Athena issues, and the most common reasons for query failure. You will also review tips to help diagnose and correct failing queries in your pursuit of operational excellence. Finally, you’ll explore advanced concepts such as Athena Query Federation and Athena ML to generate powerful insights without needing to touch a single server.By the end of this book, you’ll be able to build and use a data lake with Amazon Athena to add data-driven features to your app and perform the kind of ad hoc data analysis that often precedes many of today’s ML modeling exercises.

Skrypty powłoki systemu Linux. Zagadnienia zaawansowane. Wydanie II

Mokhtar Ebrahim, Andrew Mallett

Mimo że nowe wydania dystrybucji Linuksa są coraz łatwiejsze w obsłudze, a ważniejsze czynności administracyjne mogą być wykonywane za pomocą intuicyjnego interfejsu graficznego, wciąż nie można się obejść bez powłoki Bourne'a, znanej jako bash. Dobrze napisany skrypt powłoki pozwala na automatyzację nudnych obowiązków, umożliwia monitorowanie stanu systemu, optymalizację jego wydajności czy dostosowanie go do potrzeb. Warto też wypróbować ciekawą alternatywę dla tradycyjnych skryptów powłoki bash, czyli kod Pythona. Dzięki tej książce nauczysz się wszystkiego, co jest potrzebne do pisania profesjonalnych skryptów powłoki. Dowiesz się, czym są powłoki systemu Linux, dlaczego tak ważna jest powłoka bash i w jaki sposób edytuje się skrypty. Nauczysz się pracy na zmiennych, debugowania kodu i tworzenia skryptów interaktywnych. Będziesz korzystać z instrukcji warunkowych i pętli, a także z edytora vim, pakietu Visual Studio Code oraz edytora strumieniowego sed. Zapoznasz się z zasadami pisania funkcji, dzięki którym będziesz mógł wielokrotnie używać uniwersalnych fragmentów kodu. Ponadto zdobędziesz umiejętność przetwarzania danych tekstowych, zarówno za pomocą polecenia AWK, jak i wyrażeń regularnych. Na koniec przekonasz się, jak ciekawą alternatywą dla skryptów powłoki bash jest kod napisany w Pythonie! W tej książce między innymi: wyczerpujące wprowadzenie do tworzenia i debugowania skryptów powłoki składnia alternatywna i operacje arytmetyczne praca z blokami kodu i korzystanie z funkcji automatyzacja tworzenia hostów wirtualnych zaawansowane korzystanie z polecenia AWK skrypty do analizy plików dziennika i tworzenia raportów Opanuj sztukę pisania doskonałych skryptów powłoki!

Snowflake Cookbook. Techniques for building modern cloud data warehousing solutions

Hamid Mahmood Qureshi, Hammad Sharif

Snowflake is a unique cloud-based data warehousing platform built from scratch to perform data management on the cloud. This book introduces you to Snowflake's unique architecture, which places it at the forefront of cloud data warehouses.You'll explore the compute model available with Snowflake, and find out how Snowflake allows extensive scaling through the virtual warehouses. You will then learn how to configure a virtual warehouse for optimizing cost and performance. Moving on, you'll get to grips with the data ecosystem and discover how Snowflake integrates with other technologies for staging and loading data.As you progress through the chapters, you will leverage Snowflake's capabilities to process a series of SQL statements using tasks to build data pipelines and find out how you can create modern data solutions and pipelines designed to provide high performance and scalability. You will also get to grips with creating role hierarchies, adding custom roles, and setting default roles for users before covering advanced topics such as data sharing, cloning, and performance optimization.By the end of this Snowflake book, you will be well-versed in Snowflake's architecture for building modern analytical solutions and understand best practices for solving commonly faced problems using practical recipes.

Software Architecture with Python. Design and architect highly scalable, robust, clean, and high performance applications in Python

Anand Balachandran Pillai

This book starts by explaining how Python fits into an application's architecture. As you move along, you will get to grips with architecturally significant demands and how to determine them. Later, you’ll gain a complete understanding of the different architectural quality requirements for building a product that satisfies business needs, such as maintainability/reusability, testability, scalability, performance, usability, and security.You will also use various techniques such as incorporating DevOps, continuous integration, and more to make your application robust. You will discover when and when not to use object orientation in your applications, and design scalable applications.The focus is on building the business logic based on the business process documentation, and understanding which frameworks to use and when to use them. The book also covers some important patterns that should be taken into account while solving design problems, as well as those in relatively new domains such as the Cloud.By the end of this book, you will have understood the ins and outs of Python so that you can make critical design decisions that not just live up to but also surpassyour clients’ expectations.

Speed Up Your Python with Rust. Optimize Python performance by creating Python pip modules in Rust with PyO3

Maxwell Flitton

Python has made software development easier, but it falls short in several areas including memory management that lead to poor performance and security. Rust, on the other hand, provides memory safety without using a garbage collector, which means that with its low memory footprint, you can build high-performant and secure apps relatively easily. However, rewriting everything in Rust can be expensive and risky as there might not be package support in Rust for the problem being solved. This is where Python bindings and pip come in.This book will help you, as a Python developer, to start using Rust in your Python projects without having to manage a separate Rust server or application. Seeing as you'll already understand concepts like functions and loops, this book covers the quirks of Rust such as memory management to code Rust in a productive and structured manner. You'll explore the PyO3 crate to fuse Rust code with Python, learn how to package your fused Rust code in a pip package, and then deploy a Python Flask application in Docker that uses a private Rust pip module. Finally, you'll get to grips with advanced Rust binding topics such as inspecting Python objects and modules in Rust.By the end of this Rust book, you'll be able to develop safe and high-performant applications with better concurrency support.

Sphinx Search Beginner's Guide. Implement full-text search with lightning speed and accuracy using Sphinx

Abbas Ali

Statystyka praktyczna w data science. 50 kluczowych zagadnień w językach R i Python. Wydanie II

Peter Bruce, Andrew Bruce, Peter Gedeck

Metody statystyczne są kluczowym narzędziem w data science, mimo to niewielu analityków danych zdobyło wykształcenie w ich zakresie. Może im to utrudniać uzyskiwanie dobrych efektów. Zrozumienie praktycznych zasad statystyki okazuje się ważne również dla programistów R i Pythona, którzy tworzą rozwiązania dla data science. Kursy podstaw statystyki rzadko jednak uwzględniają tę perspektywę, a większość podręczników do statystyki w ogóle nie zajmuje się narzędziami wywodzącymi się z informatyki. To drugie wydanie popularnego podręcznika statystyki przeznaczonego dla analityków danych. Uzupełniono je o obszerne przykłady w Pythonie oraz wyjaśnienie, jak stosować poszczególne metody statystyczne w problemach data science, a także jak ich nie używać. Skoncentrowano się też na tych zagadnieniach statystyki, które odgrywają istotną rolę w data science. Wyjaśniono, które koncepcje są ważne i przydatne z tej perspektywy, a które mniej istotne i dlaczego. Co ważne, poszczególne koncepcje i zagadnienia praktyczne przedstawiono w sposób przyswajalny i zrozumiały również dla osób nienawykłych do posługiwania się statystyką na co dzień. W książce między innymi: analiza eksploracyjna we wstępnym badaniu danych próby losowe a jakość dużych zbiorów danych podstawy planowania eksperymentów regresja w szacowaniu wyników i wykrywaniu anomalii statystyczne uczenie maszynowe uczenie nienadzorowane a znaczenie danych niesklasyfikowanych Statystyka: klasyczne narzędzia w najnowszych technologiach!

Streamlit for Data Science. Create interactive data apps in Python - Second Edition

Tyler Richards, Adrien Treuille

If you work with data in Python and are looking to create data apps that showcase ML models and make beautiful interactive visualizations, then this is the ideal book for you. Streamlit for Data Science, Second Edition, shows you how to create and deploy data apps quickly, all within Python. This helps you create prototypes in hours instead of days!Written by a prolific Streamlit user and senior data scientist at Snowflake, this fully updated second edition builds on the practical nature of the previous edition with exciting updates, including connecting Streamlit to data warehouses like Snowflake, integrating Hugging Face and OpenAI models into your apps, and connecting and building apps on top of Streamlit databases. Plus, there is a totally updated code repository on GitHub to help you practice your newfound skills.You'll start your journey with the fundamentals of Streamlit and gradually build on this foundation by working with machine learning models and producing high-quality interactive apps. The practical examples of both personal data projects and work-related data-focused web applications will help you get to grips with more challenging topics such as Streamlit Components, beautifying your apps, and quick deployment.By the end of this book, you'll be able to create dynamic web apps in Streamlit quickly and effortlessly.

Supervised Machine Learning with Python. Develop rich Python coding practices while exploring supervised machine learning

Taylor Smith

Supervised machine learning is used in a wide range of sectors, such as finance, online advertising, and analytics, to train systems to make pricing predictions, campaign adjustments, customer recommendations, and much more by learning from the data that is used to train it and making decisions on its own. This makes it crucial to know how a machine 'learns' under the hood.This book will guide you through the implementation and nuances of many popular supervised machine learning algorithms, and help you understand how they work. You’ll embark on this journey with a quick overview of supervised learning and see how it differs from unsupervised learning. You’ll then explore parametric models, such as linear and logistic regression, non-parametric methods, such as decision trees, and a variety of clustering techniques that facilitate decision-making and predictions. As you advance, you'll work hands-on with recommender systems, which are widely used by online companies to increase user interaction and enrich shopping potential. Finally, you’ll wrap up with a brief foray into neural networks and transfer learning.By the end of this book, you’ll be equipped with hands-on techniques and will have gained the practical know-how you need to quickly and effectively apply algorithms to solve new problems.