| data engineering with python paul crickard: Data Engineering with Python Paul Crickard, 2020-10-23 Build, monitor, and manage real-time data pipelines to create data engineering infrastructure efficiently using open-source Apache projects Key Features Become well-versed in data architectures, data preparation, and data optimization skills with the help of practical examples Design data models and learn how to extract, transform, and load (ETL) data using Python Schedule, automate, and monitor complex data pipelines in production Book DescriptionData engineering provides the foundation for data science and analytics, and forms an important part of all businesses. This book will help you to explore various tools and methods that are used for understanding the data engineering process using Python. The book will show you how to tackle challenges commonly faced in different aspects of data engineering. You’ll start with an introduction to the basics of data engineering, along with the technologies and frameworks required to build data pipelines to work with large datasets. You’ll learn how to transform and clean data and perform analytics to get the most out of your data. As you advance, you'll discover how to work with big data of varying complexity and production databases, and build data pipelines. Using real-world examples, you’ll build architectures on which you’ll learn how to deploy data pipelines. By the end of this Python book, you’ll have gained a clear understanding of data modeling techniques, and will be able to confidently build data engineering pipelines for tracking data, running quality checks, and making necessary changes in production.What you will learn Understand how data engineering supports data science workflows Discover how to extract data from files and databases and then clean, transform, and enrich it Configure processors for handling different file formats as well as both relational and NoSQL databases Find out how to implement a data pipeline and dashboard to visualize results Use staging and validation to check data before landing in the warehouse Build real-time pipelines with staging areas that perform validation and handle failures Get to grips with deploying pipelines in the production environment Who this book is for This book is for data analysts, ETL developers, and anyone looking to get started with or transition to the field of data engineering or refresh their knowledge of data engineering using Python. This book will also be useful for students planning to build a career in data engineering or IT professionals preparing for a transition. No previous knowledge of data engineering is required. |
| data engineering with python paul crickard: DATA ENGINEERING WITH PYTHON PAUL. CRICKARD, 2020 |
| data engineering with python paul crickard: Mastering Geospatial Analysis with Python Silas Toms, Paul Crickard, Eric van Rees, 2018-04-27 Explore GIS processing and learn to work with various tools and libraries in Python. Key Features Analyze and process geospatial data using Python libraries such as; Anaconda, GeoPandas Leverage new ArcGIS API to process geospatial data for the cloud. Explore various Python geospatial web and machine learning frameworks. Book Description Python comes with a host of open source libraries and tools that help you work on professional geoprocessing tasks without investing in expensive tools. This book will introduce Python developers, both new and experienced, to a variety of new code libraries that have been developed to perform geospatial analysis, statistical analysis, and data management. This book will use examples and code snippets that will help explain how Python 3 differs from Python 2, and how these new code libraries can be used to solve age-old problems in geospatial analysis. You will begin by understanding what geoprocessing is and explore the tools and libraries that Python 3 offers. You will then learn to use Python code libraries to read and write geospatial data. You will then learn to perform geospatial queries within databases and learn PyQGIS to automate analysis within the QGIS mapping suite. Moving forward, you will explore the newly released ArcGIS API for Python and ArcGIS Online to perform geospatial analysis and create ArcGIS Online web maps. Further, you will deep dive into Python Geospatial web frameworks and learn to create a geospatial REST API. What you will learn Manage code libraries and abstract geospatial analysis techniques using Python 3. Explore popular code libraries that perform specific tasks for geospatial analysis. Utilize code libraries for data conversion, data management, web maps, and REST API creation. Learn techniques related to processing geospatial data in the cloud. Leverage features of Python 3 with geospatial databases such as PostGIS, SQL Server, and SpatiaLite. Who this book is for The audience for this book includes students, developers, and geospatial professionals who need a reference book that covers GIS data management, analysis, and automation techniques with code libraries built in Python 3. |
| data engineering with python paul crickard: Leaflet.js Essentials Paul Crickard III, 2014-08-18 If you are a web developer working with geospatial concepts and mapping APIs, and you want to learn Leaflet to create mapping solutions, this book is for you. You need to have a basic knowledge of working with JavaScript and performing web application development. |
| data engineering with python paul crickard: Data Pipelines Pocket Reference James Densmore, 2021-02-10 Data pipelines are the foundation for success in data analytics. Moving data from numerous diverse sources and transforming it to provide context is the difference between having data and actually gaining value from it. This pocket reference defines data pipelines and explains how they work in today's modern data stack. You'll learn common considerations and key decision points when implementing pipelines, such as batch versus streaming data ingestion and build versus buy. This book addresses the most common decisions made by data professionals and discusses foundational concepts that apply to open source frameworks, commercial products, and homegrown solutions. You'll learn: What a data pipeline is and how it works How data is moved and processed on modern data infrastructure, including cloud platforms Common tools and products used by data engineers to build pipelines How pipelines support analytics and reporting needs Considerations for pipeline maintenance, testing, and alerting |
| data engineering with python paul crickard: Data Engineering with Apache Spark, Delta Lake, and Lakehouse Manoj Kukreja, Danil Zburivsky, 2021-10-22 Understand the complexities of modern-day data engineering platforms and explore strategies to deal with them with the help of use case scenarios led by an industry expert in big data Key FeaturesBecome well-versed with the core concepts of Apache Spark and Delta Lake for building data platformsLearn how to ingest, process, and analyze data that can be later used for training machine learning modelsUnderstand how to operationalize data models in production using curated dataBook Description In the world of ever-changing data and schemas, it is important to build data pipelines that can auto-adjust to changes. This book will help you build scalable data platforms that managers, data scientists, and data analysts can rely on. Starting with an introduction to data engineering, along with its key concepts and architectures, this book will show you how to use Microsoft Azure Cloud services effectively for data engineering. You'll cover data lake design patterns and the different stages through which the data needs to flow in a typical data lake. Once you've explored the main features of Delta Lake to build data lakes with fast performance and governance in mind, you'll advance to implementing the lambda architecture using Delta Lake. Packed with practical examples and code snippets, this book takes you through real-world examples based on production scenarios faced by the author in his 10 years of experience working with big data. Finally, you'll cover data lake deployment strategies that play an important role in provisioning the cloud resources and deploying the data pipelines in a repeatable and continuous way. By the end of this data engineering book, you'll know how to effectively deal with ever-changing data and create scalable data pipelines to streamline data science, ML, and artificial intelligence (AI) tasks. What you will learnDiscover the challenges you may face in the data engineering worldAdd ACID transactions to Apache Spark using Delta LakeUnderstand effective design strategies to build enterprise-grade data lakesExplore architectural and design patterns for building efficient data ingestion pipelinesOrchestrate a data pipeline for preprocessing data using Apache Spark and Delta Lake APIsAutomate deployment and monitoring of data pipelines in productionGet to grips with securing, monitoring, and managing data pipelines models efficientlyWho this book is for This book is for aspiring data engineers and data analysts who are new to the world of data engineering and are looking for a practical guide to building scalable data platforms. If you already work with PySpark and want to use Delta Lake for data engineering, you'll find this book useful. Basic knowledge of Python, Spark, and SQL is expected. |
| data engineering with python paul crickard: Architecting High-Performance Embedded Systems Jim Ledin, 2021-02-05 Explore the complete process of developing systems based on field-programmable gate arrays (FPGAs), including the design of electronic circuits and the construction and debugging of prototype embedded devices Key FeaturesLearn the basics of embedded systems and real-time operating systemsUnderstand how FPGAs implement processing algorithms in hardwareDesign, construct, and debug custom digital systems from scratch using KiCadBook Description Modern digital devices used in homes, cars, and wearables contain highly sophisticated computing capabilities composed of embedded systems that generate, receive, and process digital data streams at rates up to multiple gigabits per second. This book will show you how to use Field Programmable Gate Arrays (FPGAs) and high-speed digital circuit design to create your own cutting-edge digital systems. Architecting High-Performance Embedded Systems takes you through the fundamental concepts of embedded systems, including real-time operation and the Internet of Things (IoT), and the architecture and capabilities of the latest generation of FPGAs. Using powerful free tools for FPGA design and electronic circuit design, you'll learn how to design, build, test, and debug high-performance FPGA-based IoT devices. The book will also help you get up to speed with embedded system design, circuit design, hardware construction, firmware development, and debugging to produce a high-performance embedded device – a network-based digital oscilloscope. You'll explore techniques such as designing four-layer printed circuit boards with high-speed differential signal pairs and assembling the board using surface-mount components. By the end of the book, you'll have a solid understanding of the concepts underlying embedded systems and FPGAs and will be able to design and construct your own sophisticated digital devices. What you will learnUnderstand the fundamentals of real-time embedded systems and sensorsDiscover the capabilities of FPGAs and how to use FPGA development toolsLearn the principles of digital circuit design and PCB layout with KiCadConstruct high-speed circuit board prototypes at low costDesign and develop high-performance algorithms for FPGAsDevelop robust, reliable, and efficient firmware in CThoroughly test and debug embedded device hardware and firmwareWho this book is for This book is for software developers, IoT engineers, and anyone who wants to understand the process of developing high-performance embedded systems. You'll also find this book useful if you want to learn about the fundamentals of FPGA development and all aspects of firmware development in C and C++. Familiarity with the C language, digital circuits, and electronic soldering is necessary to get started. |
| data engineering with python paul crickard: Practical Data Science with Python Nathan George, 2021-09-30 Learn to effectively manage data and execute data science projects from start to finish using Python Key FeaturesUnderstand and utilize data science tools in Python, such as specialized machine learning algorithms and statistical modelingBuild a strong data science foundation with the best data science tools available in PythonAdd value to yourself, your organization, and society by extracting actionable insights from raw dataBook Description Practical Data Science with Python teaches you core data science concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, probability theory, machine learning, and Python programming, helping you build a solid foundation to gain proficiency in data science. The book starts with an overview of basic Python skills and then introduces foundational data science techniques, followed by a thorough explanation of the Python code needed to execute the techniques. You'll understand the code by working through the examples. The code has been broken down into small chunks (a few lines or a function at a time) to enable thorough discussion. As you progress, you will learn how to perform data analysis while exploring the functionalities of key data science Python packages, including pandas, SciPy, and scikit-learn. Finally, the book covers ethics and privacy concerns in data science and suggests resources for improving data science skills, as well as ways to stay up to date on new data science developments. By the end of the book, you should be able to comfortably use Python for basic data science projects and should have the skills to execute the data science process on any data source. What you will learnUse Python data science packages effectivelyClean and prepare data for data science work, including feature engineering and feature selectionData modeling, including classic statistical models (such as t-tests), and essential machine learning algorithms, such as random forests and boosted modelsEvaluate model performanceCompare and understand different machine learning methodsInteract with Excel spreadsheets through PythonCreate automated data science reports through PythonGet to grips with text analytics techniquesWho this book is for The book is intended for beginners, including students starting or about to start a data science, analytics, or related program (e.g. Bachelor’s, Master’s, bootcamp, online courses), recent college graduates who want to learn new skills to set them apart in the job market, professionals who want to learn hands-on data science techniques in Python, and those who want to shift their career to data science. The book requires basic familiarity with Python. A getting started with Python section has been included to get complete novices up to speed. |
| data engineering with python paul crickard: Big Data Architect’s Handbook Syed Muhammad Fahad Akhtar, 2018-06-21 A comprehensive end-to-end guide that gives hands-on practice in big data and Artificial Intelligence Key Features Learn to build and run a big data application with sample code Explore examples to implement activities that a big data architect performs Use Machine Learning and AI for structured and unstructured data Book Description The big data architects are the “masters” of data, and hold high value in today’s market. Handling big data, be it of good or bad quality, is not an easy task. The prime job for any big data architect is to build an end-to-end big data solution that integrates data from different sources and analyzes it to find useful, hidden insights. Big Data Architect’s Handbook takes you through developing a complete, end-to-end big data pipeline, which will lay the foundation for you and provide the necessary knowledge required to be an architect in big data. Right from understanding the design considerations to implementing a solid, efficient, and scalable data pipeline, this book walks you through all the essential aspects of big data. It also gives you an overview of how you can leverage the power of various big data tools such as Apache Hadoop and ElasticSearch in order to bring them together and build an efficient big data solution. By the end of this book, you will be able to build your own design system which integrates, maintains, visualizes, and monitors your data. In addition, you will have a smooth design flow in each process, putting insights in action. What you will learn Learn Hadoop Ecosystem and Apache projects Understand, compare NoSQL database and essential software architecture Cloud infrastructure design considerations for big data Explore application scenario of big data tools for daily activities Learn to analyze and visualize results to uncover valuable insights Build and run a big data application with sample code from end to end Apply Machine Learning and AI to perform big data intelligence Practice the daily activities performed by big data architects Who this book is for Big Data Architect’s Handbook is for you if you are an aspiring data professional, developer, or IT enthusiast who aims to be an all-round architect in big data. This book is your one-stop solution to enhance your knowledge and carry out easy to complex activities required to become a big data architect. |
| data engineering with python paul crickard: The Art of R Programming Norman Matloff, 2011-10-11 R is the world's most popular language for developing statistical software: Archaeologists use it to track the spread of ancient civilizations, drug companies use it to discover which medications are safe and effective, and actuaries use it to assess financial risks and keep economies running smoothly. The Art of R Programming takes you on a guided tour of software development with R, from basic types and data structures to advanced topics like closures, recursion, and anonymous functions. No statistical knowledge is required, and your programming skills can range from hobbyist to pro. Along the way, you'll learn about functional and object-oriented programming, running mathematical simulations, and rearranging complex data into simpler, more useful formats. You'll also learn to: –Create artful graphs to visualize complex data sets and functions –Write more efficient code using parallel R and vectorization –Interface R with C/C++ and Python for increased speed or functionality –Find new R packages for text analysis, image manipulation, and more –Squash annoying bugs with advanced debugging techniques Whether you're designing aircraft, forecasting the weather, or you just need to tame your data, The Art of R Programming is your guide to harnessing the power of statistical computing. |
| data engineering with python paul crickard: AWS Cookbook John Culkin, Mike Zazon, 2021-12-02 This practical guide provides over 100 self-contained recipes to help you creatively solve issues you may encounter in your AWS cloud endeavors. If you're comfortable with rudimentary scripting and general cloud concepts, this cookbook will give you what you need to both address foundational tasks and create high-level capabilities. AWS Cookbook provides real-world examples that incorporate best practices. Each recipe includes code that you can safely execute in a sandbox AWS account to ensure that it works. From there, you can customize the code to help construct your application or fix your specific existing problem. Recipes also include a discussion that explains the approach and provides context. This cookbook takes you beyond theory, providing the nuts and bolts you need to successfully build on AWS. You'll find recipes for: Organizing multiple accounts for enterprise deployments Locking down S3 buckets Analyzing IAM roles Autoscaling a containerized service Summarizing news articles Standing up a virtual call center Creating a chatbot that can pull answers from a knowledge repository Automating security group rule monitoring, looking for rogue traffic flows And more. |
| data engineering with python paul crickard: Data Pipelines with Apache Airflow Bas P. Harenslak, Julian de Ruiter, 2021-04-27 This book teaches you how to build and maintain effective data pipelines. Youll explore the most common usage patterns, including aggregating multiple data sources, connecting to and from data lakes, and cloud deployment. -- |
| data engineering with python paul crickard: Python Data Cleaning Cookbook Michael Walker, 2020-12-11 Discover how to describe your data in detail, identify data issues, and find out how to solve them using commonly used techniques and tips and tricks Key FeaturesGet well-versed with various data cleaning techniques to reveal key insightsManipulate data of different complexities to shape them into the right form as per your business needsClean, monitor, and validate large data volumes to diagnose problems before moving on to data analysisBook Description Getting clean data to reveal insights is essential, as directly jumping into data analysis without proper data cleaning may lead to incorrect results. This book shows you tools and techniques that you can apply to clean and handle data with Python. You'll begin by getting familiar with the shape of data by using practices that can be deployed routinely with most data sources. Then, the book teaches you how to manipulate data to get it into a useful form. You'll also learn how to filter and summarize data to gain insights and better understand what makes sense and what does not, along with discovering how to operate on data to address the issues you've identified. Moving on, you'll perform key tasks, such as handling missing values, validating errors, removing duplicate data, monitoring high volumes of data, and handling outliers and invalid dates. Next, you'll cover recipes on using supervised learning and Naive Bayes analysis to identify unexpected values and classification errors, and generate visualizations for exploratory data analysis (EDA) to visualize unexpected values. Finally, you'll build functions and classes that you can reuse without modification when you have new data. By the end of this Python book, you'll be equipped with all the key skills that you need to clean data and diagnose problems within it. What you will learnFind out how to read and analyze data from a variety of sourcesProduce summaries of the attributes of data frames, columns, and rowsFilter data and select columns of interest that satisfy given criteriaAddress messy data issues, including working with dates and missing valuesImprove your productivity in Python pandas by using method chainingUse visualizations to gain additional insights and identify potential data issuesEnhance your ability to learn what is going on in your dataBuild user-defined functions and classes to automate data cleaningWho this book is for This book is for anyone looking for ways to handle messy, duplicate, and poor data using different Python tools and techniques. The book takes a recipe-based approach to help you to learn how to clean and manage data. Working knowledge of Python programming is all you need to get the most out of the book. |
| data engineering with python paul crickard: Learning PySpark Tomasz Drabas, Denny Lee, 2017-02-27 Build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2.0 About This Book Learn why and how you can efficiently use Python to process data and build machine learning models in Apache Spark 2.0 Develop and deploy efficient, scalable real-time Spark solutions Take your understanding of using Spark with Python to the next level with this jump start guide Who This Book Is For If you are a Python developer who wants to learn about the Apache Spark 2.0 ecosystem, this book is for you. A firm understanding of Python is expected to get the best out of the book. Familiarity with Spark would be useful, but is not mandatory. What You Will Learn Learn about Apache Spark and the Spark 2.0 architecture Build and interact with Spark DataFrames using Spark SQL Learn how to solve graph and deep learning problems using GraphFrames and TensorFrames respectively Read, transform, and understand data and use it to train machine learning models Build machine learning models with MLlib and ML Learn how to submit your applications programmatically using spark-submit Deploy locally built applications to a cluster In Detail Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. This book will show you how to leverage the power of Python and put it to use in the Spark ecosystem. You will start by getting a firm understanding of the Spark 2.0 architecture and how to set up a Python environment for Spark. You will get familiar with the modules available in PySpark. You will learn how to abstract data with RDDs and DataFrames and understand the streaming capabilities of PySpark. Also, you will get a thorough overview of machine learning capabilities of PySpark using ML and MLlib, graph processing using GraphFrames, and polyglot persistence using Blaze. Finally, you will learn how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will have established a firm understanding of the Spark Python API and how it can be used to build data-intensive applications. Style and approach This book takes a very comprehensive, step-by-step approach so you understand how the Spark ecosystem can be used with Python to develop efficient, scalable solutions. Every chapter is standalone and written in a very easy-to-understand manner, with a focus on both the hows and the whys of each concept. |
| data engineering with python paul crickard: DAMA-DMBOK Dama International, 2017 Defining a set of guiding principles for data management and describing how these principles can be applied within data management functional areas; Providing a functional framework for the implementation of enterprise data management practices; including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics; Establishing a common vocabulary for data management concepts and serving as the basis for best practices for data management professionals. DAMA-DMBOK2 provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure, based on these principles: Data is an asset with unique properties; The value of data can be and should be expressed in economic terms; Managing data means managing the quality of data; It takes metadata to manage data; It takes planning to manage data; Data management is cross-functional and requires a range of skills and expertise; Data management requires an enterprise perspective; Data management must account for a range of perspectives; Data management is data lifecycle management; Different types of data have different lifecycle requirements; Managing data includes managing risks associated with data; Data management requirements must drive information technology decisions; Effective data management requires leadership commitment. |
| data engineering with python paul crickard: English advanced vocabulary and structure practice Maciej Matasek, 2003 |
| data engineering with python paul crickard: 97 Things Every Data Engineer Should Know Tobias Macey, 2021-06-11 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, host of the popular Data Engineering Podcast, this book presents 97 concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data. Data engineers, data architects, data team managers, data scientists, machine learning engineers, and software engineers will greatly benefit from the wisdom and experience of their peers. Topics include: The Importance of Data Lineage - Julien Le Dem Data Security for Data Engineers - Katharine Jarmul The Two Types of Data Engineering and Data Engineers - Jesse Anderson Six Dimensions for Picking an Analytical Data Warehouse - Gleb Mezhanskiy The End of ETL as We Know It - Paul Singman Building a Career as a Data Engineer - Vijay Kiran Modern Metadata for the Modern Data Stack - Prukalpa Sankar Your Data Tests Failed! Now What? - Sam Bail |
| data engineering with python paul crickard: Azure Data Factory Cookbook Dmitry Anoshin, Dmitry Foshin, Roman Storchak, Xenia Ireton, 2020-12-24 Solve real-world data problems and create data-driven workflows for easy data movement and processing at scale with Azure Data Factory Key FeaturesLearn how to load and transform data from various sources, both on-premises and on cloudUse Azure Data Factory’s visual environment to build and manage hybrid ETL pipelinesDiscover how to prepare, transform, process, and enrich data to generate key insightsBook Description Azure Data Factory (ADF) is a modern data integration tool available on Microsoft Azure. This Azure Data Factory Cookbook helps you get up and running by showing you how to create and execute your first job in ADF. You’ll learn how to branch and chain activities, create custom activities, and schedule pipelines. This book will help you to discover the benefits of cloud data warehousing, Azure Synapse Analytics, and Azure Data Lake Gen2 Storage, which are frequently used for big data analytics. With practical recipes, you’ll learn how to actively engage with analytical tools from Azure Data Services and leverage your on-premise infrastructure with cloud-native tools to get relevant business insights. As you advance, you’ll be able to integrate the most commonly used Azure Services into ADF and understand how Azure services can be useful in designing ETL pipelines. The book will take you through the common errors that you may encounter while working with ADF and show you how to use the Azure portal to monitor pipelines. You’ll also understand error messages and resolve problems in connectors and data flows with the debugging capabilities of ADF. By the end of this book, you’ll be able to use ADF as the main ETL and orchestration tool for your data warehouse or data platform projects. What you will learnCreate an orchestration and transformation job in ADFDevelop, execute, and monitor data flows using Azure SynapseCreate big data pipelines using Azure Data Lake and ADFBuild a machine learning app with Apache Spark and ADFMigrate on-premises SSIS jobs to ADFIntegrate ADF with commonly used Azure services such as Azure ML, Azure Logic Apps, and Azure FunctionsRun big data compute jobs within HDInsight and Azure DatabricksCopy data from AWS S3 and Google Cloud Storage to Azure Storage using ADF's built-in connectorsWho this book is for This book is for ETL developers, data warehouse and ETL architects, software professionals, and anyone who wants to learn about the common and not-so-common challenges faced while developing traditional and hybrid ETL solutions using Microsoft's Azure Data Factory. You’ll also find this book useful if you are looking for recipes to improve or enhance your existing ETL pipelines. Basic knowledge of data warehousing is expected. |
| data engineering with python paul crickard: Data Modeling for Azure Data Services Peter ter Braake, 2021-07-30 Choose the right Azure data service and correct model design for successful implementation of your data model with the help of this hands-on guide Key FeaturesDesign a cost-effective, performant, and scalable database in AzureChoose and implement the most suitable design for a databaseDiscover how your database can scale with growing data volumes, concurrent users, and query complexityBook Description Data is at the heart of all applications and forms the foundation of modern data-driven businesses. With the multitude of data-related use cases and the availability of different data services, choosing the right service and implementing the right design becomes paramount to successful implementation. Data Modeling for Azure Data Services starts with an introduction to databases, entity analysis, and normalizing data. The book then shows you how to design a NoSQL database for optimal performance and scalability and covers how to provision and implement Azure SQL DB, Azure Cosmos DB, and Azure Synapse SQL Pool. As you progress through the chapters, you'll learn about data analytics, Azure Data Lake, and Azure SQL Data Warehouse and explore dimensional modeling, data vault modeling, along with designing and implementing a Data Lake using Azure Storage. You'll also learn how to implement ETL with Azure Data Factory. By the end of this book, you'll have a solid understanding of which Azure data services are the best fit for your model and how to implement the best design for your solution. What you will learnModel relational database using normalization, dimensional, or Data Vault modelingProvision and implement Azure SQL DB and Azure Synapse SQL PoolsDiscover how to model a Data Lake and implement it using Azure StorageModel a NoSQL database and provision and implement an Azure Cosmos DBUse Azure Data Factory to implement ETL/ELT processesCreate a star schema model using dimensional modelingWho this book is for This book is for business intelligence developers and consultants who work on (modern) cloud data warehousing and design and implement databases. Beginner-level knowledge of cloud data management is expected. |
| data engineering with python paul crickard: Learning Geospatial Analysis with Python Joel Lawhead, 2019-09-27 Learn the core concepts of geospatial data analysis for building actionable and insightful GIS applications Key Features Create GIS solutions using the new features introduced in Python 3.7 Explore a range of GIS tools and libraries such as PostGIS, QGIS, and PROJ Learn to automate geospatial analysis workflows using Python and Jupyter Book DescriptionGeospatial analysis is used in almost every domain you can think of, including defense, farming, and even medicine. With this systematic guide, you'll get started with geographic information system (GIS) and remote sensing analysis using the latest features in Python. This book will take you through GIS techniques, geodatabases, geospatial raster data, and much more using the latest built-in tools and libraries in Python 3.7. You'll learn everything you need to know about using software packages or APIs and generic algorithms that can be used for different situations. Furthermore, you'll learn how to apply simple Python GIS geospatial processes to a variety of problems, and work with remote sensing data. By the end of the book, you'll be able to build a generic corporate system, which can be implemented in any organization to manage customer support requests and field support personnel.What you will learn Automate geospatial analysis workflows using Python Code the simplest possible GIS in just 60 lines of Python Create thematic maps with Python tools such as PyShp, OGR, and the Python Imaging Library Understand the different formats that geospatial data comes in Produce elevation contours using Python tools Create flood inundation models Apply geospatial analysis to real-time data tracking and storm chasing Who this book is forThis book is for Python developers, researchers, or analysts who want to perform geospatial modeling and GIS analysis with Python. Basic knowledge of digital mapping and analysis using Python or other scripting languages will be helpful. |
| data engineering with python paul crickard: Practical Data Analysis Using Jupyter Notebook Marc Wintjen, 2020-06-19 Understand data analysis concepts to make accurate decisions based on data using Python programming and Jupyter Notebook Key FeaturesFind out how to use Python code to extract insights from data using real-world examplesWork with structured data and free text sources to answer questions and add value using dataPerform data analysis from scratch with the help of clear explanations for cleaning, transforming, and visualizing dataBook Description Data literacy is the ability to read, analyze, work with, and argue using data. Data analysis is the process of cleaning and modeling your data to discover useful information. This book combines these two concepts by sharing proven techniques and hands-on examples so that you can learn how to communicate effectively using data. After introducing you to the basics of data analysis using Jupyter Notebook and Python, the book will take you through the fundamentals of data. Packed with practical examples, this guide will teach you how to clean, wrangle, analyze, and visualize data to gain useful insights, and you'll discover how to answer questions using data with easy-to-follow steps. Later chapters teach you about storytelling with data using charts, such as histograms and scatter plots. As you advance, you'll understand how to work with unstructured data using natural language processing (NLP) techniques to perform sentiment analysis. All the knowledge you gain will help you discover key patterns and trends in data using real-world examples. In addition to this, you will learn how to handle data of varying complexity to perform efficient data analysis using modern Python libraries. By the end of this book, you'll have gained the practical skills you need to analyze data with confidence. What you will learnUnderstand the importance of data literacy and how to communicate effectively using dataFind out how to use Python packages such as NumPy, pandas, Matplotlib, and the Natural Language Toolkit (NLTK) for data analysisWrangle data and create DataFrames using pandasProduce charts and data visualizations using time-series datasetsDiscover relationships and how to join data together using SQLUse NLP techniques to work with unstructured data to create sentiment analysis modelsDiscover patterns in real-world datasets that provide accurate insightsWho this book is for This book is for aspiring data analysts and data scientists looking for hands-on tutorials and real-world examples to understand data analysis concepts using SQL, Python, and Jupyter Notebook. Anyone looking to evolve their skills to become data-driven personally and professionally will also find this book useful. No prior knowledge of data analysis or programming is required to get started with this book. |
| data engineering with python paul crickard: Learning Python Mark Lutz, 2009-10-06 Google and YouTube use Python because it's highly adaptable, easy to maintain, and allows for rapid development. If you want to write high-quality, efficient code that's easily integrated with other languages and tools, this hands-on book will help you be productive with Python quickly -- whether you're new to programming or just new to Python. It's an easy-to-follow self-paced tutorial, based on author and Python expert Mark Lutz's popular training course. Each chapter contains a stand-alone lesson on a key component of the language, and includes a unique Test Your Knowledge section with practical exercises and quizzes, so you can practice new skills and test your understanding as you go. You'll find lots of annotated examples and illustrations to help you get started with Python 3.0. Learn about Python's major built-in object types, such as numbers, lists, and dictionaries Create and process objects using Python statements, and learn Python's general syntax model Structure and reuse code using functions, Python's basic procedural tool Learn about Python modules: packages of statements, functions, and other tools, organized into larger components Discover Python's object-oriented programming tool for structuring code Learn about the exception-handling model, and development tools for writing larger programs Explore advanced Python tools including decorators, descriptors, metaclasses, and Unicode processing |
| data engineering with python paul crickard: Google Cloud Platform for Data Engineering Alasdair Gilchrist, 2019-10-23 Google Cloud Platform for Data Engineering is designed to take the beginner through a journey to become a competent and certified GCP data engineer. The book, therefore, is split into three parts; the first part covers fundamental concepts of data engineering and data analysis from a platform and technology-neutral perspective. Reading part 1 will bring a beginner up to speed with the generic concepts, terms and technologies we use in data engineering. The second part, which is a high-level but comprehensive introduction to all the concepts, components, tools and services available to us within the Google Cloud Platform. Completing this section will provide the beginner to GCP and data engineering with a solid foundation on the architecture and capabilities of the GCP. Part 3, however, is where we delve into the moderate to advanced techniques that data engineers need to know and be able to carry out. By this time the raw beginner you started the journey at the beginning of part 1 will be a knowledgable albeit inexperienced data engineer. However, by the conclusion of part 3, they will have gained the advanced knowledge of data engineering techniques and practices on the GCP to pass not only the certification exam but also most interviews and practical tests with confidence. In short part 3, will provide the prospective data engineer with detailed knowledge on setting up and configuring DataProc - GCPs version of the Spark/Hadoop ecosystem for big data. They will also learn how to build and test streaming and batch data pipelines using pub/sub/ dataFlow and BigQuery. Furthermore, they will learn how to integrate all the ML and AI Platform components and APIs. They will be accomplished in connecting data analysis and visualisation tools such as Datalab, DataStudio and AI notebooks amongst others. They will also by now know how to build and train a TensorFlow DNN using APIs and Keras and optimise it to run large public data sets. Also, they will know how to provision and use Kubeflow and Kube Pipelines within Google Kubernetes engines to run container workloads as well as how to take advantage of serverless technologies such as Cloud Run and Cloud Functions to build transparent and seamless data processing platforms. The best part of the book though is its compartmental design which means that anyone from a beginner to an intermediate can join the book at whatever point they feel comfortable. |
| data engineering with python paul crickard: Learning Spark Jules S. Damji, Brooke Wenig, Tathagata Das, Denny Lee, 2020-07-16 Data is bigger, arrives faster, and comes in a variety of formats—and it all needs to be processed at scale for analytics or machine learning. But how can you process such varied workloads efficiently? Enter Apache Spark. Updated to include Spark 3.0, this second edition shows data engineers and data scientists why structure and unification in Spark matters. Specifically, this book explains how to perform simple and complex data analytics and employ machine learning algorithms. Through step-by-step walk-throughs, code snippets, and notebooks, you’ll be able to: Learn Python, SQL, Scala, or Java high-level Structured APIs Understand Spark operations and SQL Engine Inspect, tune, and debug Spark operations with Spark configurations and Spark UI Connect to data sources: JSON, Parquet, CSV, Avro, ORC, Hive, S3, or Kafka Perform analytics on batch and streaming data using Structured Streaming Build reliable data pipelines with open source Delta Lake and Spark Develop machine learning pipelines with MLlib and productionize models using MLflow |
| data engineering with python paul crickard: Graphics of Large Datasets Antony Unwin, Martin Theus, Heike Hofmann, 2007-06-12 This book shows how to look at ways of visualizing large datasets, whether large in numbers of cases, or large in numbers of variables, or large in both. All ideas are illustrated with displays from analyses of real datasets and the importance of interpreting displays effectively is emphasized. Graphics should be drawn to convey information and the book includes many insightful examples. New approaches to graphics are needed to visualize the information in large datasets and most of the innovations described in this book are developments of standard graphics. The book is accessible to readers with some experience of drawing statistical graphics. |
| data engineering with python paul crickard: Data Science from Scratch Joel Grus, 2015-04-14 Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases |
| data engineering with python paul crickard: Data Science John D. Kelleher, Brendan Tierney, 2018-04-13 A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges. The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges. It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects. |
| data engineering with python paul crickard: Python for Data Analysis Wes McKinney, 2017-09-25 Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples |
| data engineering with python paul crickard: Learning XML Erik T. Ray, 2003-09-22 This second edition of the bestselling Learning XML provides web developers with a concise but grounded understanding of XML (the Extensible Markup Language) and its potential-- not just a whirlwind tour of XML.The author explains the important and relevant XML technologies and their capabilities clearly and succinctly with plenty of real-life projects and useful examples. He outlines the elements of markup--demystifying concepts such as attributes, entities, and namespaces--and provides enough depth and examples to get started. Learning XML is a reliable source for anyone who needs to know XML, but doesn't want to waste time wading through hundreds of web sites or 800 pages of bloated text.For writers producing XML documents, this book clarifies files and the process of creating them with the appropriate structure and format. Designers will learn what parts of XML are most helpful to their team and will get started on creating Document Type Definitions. For programmers, the book makes syntax and structures clear. Learning XML also discusses the stylesheets needed for viewing documents in the next generation of browsers, databases, and other devices.Learning XML illustrates the core XML concepts and language syntax, in addition to important related tools such as the CSS and XSL styling languages and the XLink and XPointer specifications for creating rich link structures. It includes information about three schema languages for validation: W3C Schema, Schematron, and RELAX-NG, which are gaining widespread support from people who need to validate documents but aren't satisfied with DTDs. Also new in this edition is a chapter on XSL-FO, a powerful formatting language for XML. If you need to wade through the acronym soup of XML and start to really use this powerful tool, Learning XML, will give you the roadmap you need. |
| data engineering with python paul crickard: Beginning R Mark Gardener, 2012-05-24 Conquer the complexities of this open source statistical language R is fast becoming the de facto standard for statistical computing and analysis in science, business, engineering, and related fields. This book examines this complex language using simple statistical examples, showing how R operates in a user-friendly context. Both students and workers in fields that require extensive statistical analysis will find this book helpful as they learn to use R for simple summary statistics, hypothesis testing, creating graphs, regression, and much more. It covers formula notation, complex statistics, manipulating data and extracting components, and rudimentary programming. R, the open source statistical language increasingly used to handle statistics and produces publication-quality graphs, is notoriously complex This book makes R easier to understand through the use of simple statistical examples, teaching the necessary elements in the context in which R is actually used Covers getting started with R and using it for simple summary statistics, hypothesis testing, and graphs Shows how to use R for formula notation, complex statistics, manipulating data, extracting components, and regression Provides beginning programming instruction for those who want to write their own scripts Beginning R offers anyone who needs to perform statistical analysis the information necessary to use R with confidence. |
| data engineering with python paul crickard: Practical QlikView Mark O'donovan, 2012 http://www.techstuffybooks.com What does QlikView actually do? Although QlikView is becoming more and more popular and even being requested in job advertisements many people might wonder what QlikView actually does. With QlikView you can : -Analyse data in sources such as Excel Spreadsheets, Databases, or text files. -Combine data easily from a variety of sources.-Create charts from your data. -Search through your data very quickly, explore your data easily which can help you make decisions or may just confirm what you thought. QlikView is part of a category of software called 'Business Intelligence'. This is not to say that it cannot be used by people in their everyday lives. This book will cover examples of how you can use QlikView at home or in business. Why should I buy this book? This book will: -Teach you how to create QlikView documents from scratch in easy to understand steps with plenty of screenshots. -Explain how to get data into a QlikView document from a variety of sources such as Excel, text files and databases. -Show you how to create various charts and tables (such as pivot tables) in QlikView. Once you have covered the basics what do you do then? This book provides examples of how you can apply QlikView to do something useful and practical such as analysing computer performance, information from a sql server database or tracking your spending habits. We also provide tips to help in the development of QlikView documents. Finally we look at more advanced topics in QlikView and discuss how to can take the knowledge you have gained further to improve your future whether it is monitoring your own spending or to start using QlikView in your job. The examples in this book use QlikView version 11. |
| data engineering with python paul crickard: Tiny Python Projects Ken Youens-Clark, 2020-07-21 ”Tiny Python Projects is a gentle and amusing introduction to Python that will firm up key programming concepts while also making you giggle.”—Amanda Debler, Schaeffler Key Features Learn new programming concepts through 21-bitesize programs Build an insult generator, a Tic-Tac-Toe AI, a talk-like-a-pirate program, and more Discover testing techniques that will make you a better programmer Code-along with free accompanying videos on YouTube Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About The Book The 21 fun-but-powerful activities in Tiny Python Projects teach Python fundamentals through puzzles and games. You’ll be engaged and entertained with every exercise, as you learn about text manipulation, basic algorithms, and lists and dictionaries, and other foundational programming skills. Gain confidence and experience while you create each satisfying project. Instead of going quickly through a wide range of concepts, this book concentrates on the most useful skills, like text manipulation, data structures, collections, and program logic with projects that include a password creator, a word rhymer, and a Shakespearean insult generator. Author Ken Youens-Clark also teaches you good programming practice, including writing tests for your code as you go. What You Will Learn Write command-line Python programs Manipulate Python data structures Use and control randomness Write and run tests for programs and functions Download testing suites for each project This Book Is Written For For readers familiar with the basics of Python programming. About The Author Ken Youens-Clark is a Senior Scientific Programmer at the University of Arizona. He has an MS in Biosystems Engineering and has been programming for over 20 years. Table of Contents 1 How to write and test a Python program 2 The crow’s nest: Working with strings 3 Going on a picnic: Working with lists 4 Jump the Five: Working with dictionaries 5 Howler: Working with files and STDOUT 6 Words count: Reading files and STDIN, iterating lists, formatting strings 7 Gashlycrumb: Looking items up in a dictionary 8 Apples and Bananas: Find and replace 9 Dial-a-Curse: Generating random insults from lists of words 10 Telephone: Randomly mutating strings 11 Bottles of Beer Song: Writing and testing functions 12 Ransom: Randomly capitalizing text 13 Twelve Days of Christmas: Algorithm design 14 Rhymer: Using regular expressions to create rhyming words 15 The Kentucky Friar: More regular expressions 16 The Scrambler: Randomly reordering the middles of words 17 Mad Libs: Using regular expressions 18 Gematria: Numeric encoding of text using ASCII values 19 Workout of the Day: Parsing CSV files, creating text table output 20 Password strength: Generating a secure and memorable password 21 Tic-Tac-Toe: Exploring state 22 Tic-Tac-Toe redux: An interactive version with type hints |
| data engineering with python paul crickard: Designing Data-Intensive Applications Martin Kleppmann, 2017-03-16 Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords? In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse 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. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures |
| data engineering with python paul crickard: Mastering Flask Web Development Daniel Gaspar, Jack Stouffer, 2018-10-31 Learn to build modern, secure, highly available web MVC applications and API’s using Python`s Flask framework. Key FeaturesCreate production-ready MVC and REST API with the dynamic features of FlaskUtilize the various extensions like Flask-JWT and Flask-SQLAlchemy to develop powerful applicationsDeploy your flask application on real-world platforms like AWS and Heroku on VM’s or Docker containersBook Description Flask is a popular Python framework known for its lightweight and modular design. Mastering Flask Web Development will take you on a complete tour of the Flask environment and teach you how to build a production-ready application. You'll begin by learning about the installation of Flask and basic concepts such as MVC and accessing a database using an ORM. You will learn how to structure your application so that it can scale to any size with the help of Flask Blueprints. You'll then learn how to use Jinja2 templates with a high level of expertise. You will also learn how to develop with SQL or NoSQL databases, and how to develop REST APIs and JWT authentication. Next, you'll move on to build role-based access security and authentication using LDAP, OAuth, OpenID, and database. Also learn how to create asynchronous tasks that can scale to any load using Celery and RabbitMQ or Redis. You will also be introduced to a wide range of Flask extensions to leverage technologies such as cache, localization, and debugging. You will learn how to build your own Flask extensions, how to write tests, and how to get test coverage reports. Finally, you will learn how to deploy your application on Heroku and AWS using various technologies, such as Docker, CloudFormation, and Elastic Beanstalk, and will also learn how to develop Jenkins pipelines to build, test, and deploy applications. What you will learnDevelop a Flask extension using best practicesImplement various authentication methods: LDAP, JWT, Database, OAuth, and OpenIDLearn how to develop role-based access security and become an expert on Jinja2 templatesBuild tests for your applications and APIsInstall and configure a distributed task queue using Celery and RabbitMQDevelop RESTful APIs and secure REST API'sDeploy highly available applications that scale on Heroku and AWS using Docker or VMsWho this book is for The ideal target audience for this book would be Python developers who want to use Flask and its advanced features to create Enterprise grade and lightweight applications. The book is for those who have some exposure of Flask and want to take it from introductory to master level. |
| data engineering with python paul crickard: Signal Design for Good Correlation Solomon W. Golomb, Guang Gong, 2005-07-11 This book provides a comprehensive treatment of methodologies and applications including CDMA telephony, coded radar, and stream cipher generation. |
| data engineering with python paul crickard: Spark: The Definitive Guide Bill Chambers, Matei Zaharia, 2018-02-08 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. Youâ??ll explore the basic operations and common functions of Sparkâ??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, Sparkâ??s scalable machine-learning library. Get a gentle overview of big data and Spark Learn about DataFrames, SQL, and Datasetsâ??Sparkâ??s core APIsâ??through worked examples Dive into Sparkâ??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, Sparkâ??s stream-processing engine Learn how you can apply MLlib to a variety of problems, including classification or recommendation |
| data engineering with python paul crickard: 40 Algorithms Every Programmer Should Know Imran Ahmad, 2020-06-12 Learn algorithms for solving classic computer science problems with this concise guide covering everything from fundamental algorithms, such as sorting and searching, to modern algorithms used in machine learning and cryptography Key Features Learn the techniques you need to know to design algorithms for solving complex problems Become familiar with neural networks and deep learning techniques Explore different types of algorithms and choose the right data structures for their optimal implementation Book DescriptionAlgorithms have always played an important role in both the science and practice of computing. Beyond traditional computing, the ability to use algorithms to solve real-world problems is an important skill that any developer or programmer must have. This book will help you not only to develop the skills to select and use an algorithm to solve real-world problems but also to understand how it works. You’ll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, such as searching and sorting, with the help of practical examples. As you advance to a more complex set of algorithms, you'll learn about linear programming, page ranking, and graphs, and even work with machine learning algorithms, understanding the math and logic behind them. Further on, case studies such as weather prediction, tweet clustering, and movie recommendation engines will show you how to apply these algorithms optimally. Finally, you’ll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks. By the end of this book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.What you will learn Explore existing data structures and algorithms found in Python libraries Implement graph algorithms for fraud detection using network analysis Work with machine learning algorithms to cluster similar tweets and process Twitter data in real time Predict the weather using supervised learning algorithms Use neural networks for object detection Create a recommendation engine that suggests relevant movies to subscribers Implement foolproof security using symmetric and asymmetric encryption on Google Cloud Platform (GCP) Who this book is for This book is for programmers or developers who want to understand the use of algorithms for problem-solving and writing efficient code. Whether you are a beginner looking to learn the most commonly used algorithms in a clear and concise way or an experienced programmer looking to explore cutting-edge algorithms in data science, machine learning, and cryptography, you'll find this book useful. Although Python programming experience is a must, knowledge of data science will be helpful but not necessary. |
| data engineering with python paul crickard: Architecture Patterns with Python Harry Percival, Bob Gregory, 2020-03-05 As Python continues to grow in popularity, projects are becoming larger and more complex. Many Python developers are now taking an interest in high-level software design patterns such as hexagonal/clean architecture, event-driven architecture, and the strategic patterns prescribed by domain-driven design (DDD). But translating those patterns into Python isn’t always straightforward. With this hands-on guide, Harry Percival and Bob Gregory from MADE.com introduce proven architectural design patterns to help Python developers manage application complexity—and get the most value out of their test suites. Each pattern is illustrated with concrete examples in beautiful, idiomatic Python, avoiding some of the verbosity of Java and C# syntax. Patterns include: Dependency inversion and its links to ports and adapters (hexagonal/clean architecture) Domain-driven design’s distinction between entities, value objects, and aggregates Repository and Unit of Work patterns for persistent storage Events, commands, and the message bus Command-query responsibility segregation (CQRS) Event-driven architecture and reactive microservices |
| data engineering with python paul crickard: Snowflake Cookbook Hamid Mahmood Qureshi, Hammad Sharif, 2021-02-25 Develop modern solutions with Snowflake's unique architecture and integration capabilities; process bulk and real-time data into a data lake; and leverage time travel, cloning, and data-sharing features to optimize data operations Key Features Build and scale modern data solutions using the all-in-one Snowflake platform Perform advanced cloud analytics for implementing big data and data science solutions Make quicker and better-informed business decisions by uncovering key insights from your data Book Description 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. What you will learn Get to grips with data warehousing techniques aligned with Snowflake's cloud architecture Broaden your skills as a data warehouse designer to cover the Snowflake ecosystem Transfer skills from on-premise data warehousing to the Snowflake cloud analytics platform Optimize performance and costs associated with a Snowflake solution Stage data on object stores and load it into Snowflake Secure data and share it efficiently for access Manage transactions and extend Snowflake using stored procedures Extend cloud data applications using Spark Connector Who this book is for This book is for data warehouse developers, data analysts, database administrators, and anyone involved in designing, implementing, and optimizing a Snowflake data warehouse. Knowledge of data warehousing and database and cloud concepts will be useful. Basic familiarity with Snowflake is beneficial, but not necessary. |
| data engineering with python paul crickard: Managing Your Data Science Projects Robert de Graaf, 2019-06-07 At first glance, the skills required to work in the data science field appear to be self-explanatory. Do not be fooled. Impactful data science demands an interdisciplinary knowledge of business philosophy, project management, salesmanship, presentation, and more. In Managing Your Data Science Projects, author Robert de Graaf explores important concepts that are frequently overlooked in much of the instructional literature that is available to data scientists new to the field. If your completed models are to be used and maintained most effectively, you must be able to present and sell them within your organization in a compelling way. The value of data science within an organization cannot be overstated. Thus, it is vital that strategies and communication between teams are dexterously managed. Three main ways that data science strategy is used in a company is to research its customers, assess risk analytics, and log operational measurements. These all require different managerial instincts, backgrounds, and experiences, and de Graaf cogently breaks down the unique reasons behind each. They must align seamlessly to eventually be adopted as dynamic models. Data science is a relatively new discipline, and as such, internal processes for it are not as well-developed within an operational business as others. With Managing Your Data Science Projects, you will learn how to create products that solve important problems for your customers and ensure that the initial success is sustained throughout the product’s intended life. Your users will trust you and your models, and most importantly, you will be a more well-rounded and effectual data scientist throughout your career. Who This Book Is For Early-career data scientists, managers of data scientists, and those interested in entering the field of data science |
Data Engineering with Python
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Data Engineering with Python
By the end of this Python book, you’ll have gained a clear understanding of data modeling techniques, and will be able to confidently build data engineering pipelines for tracking data, …
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This course will examine the typical Data Engineering pipeline includes architecting data platforms, designing data stores, ETL, data collection, importing, wrangling, querying, and …
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Data Engineering with Python Paul Crickard,2020-10-23 Build monitor and manage real time data pipelines to create data engineering infrastructure efficiently using open source Apache …
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Data Engineering with Python: Work with massive datasets to design data models and automate data pipelines using Python, Paul Crickard, Packt publishing Birmingham –MUMBAI, 2020.
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1.Understand the key concepts and drivers of sustainable finance. Apply Python to conduct basic data analysis on sustainability metrics. 2.Analyze different ESG scoring methodologies using …
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CO3 Analyze and evaluate data workflows by integrating Python with databases to perform CRUD operations and leveraging tools like Apache NiFi for data ingestion and transformation.
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Data Engineering with Python Paul Crickard,2020-10-23 Build monitor and manage real time data pipelines to create data engineering infrastructure efficiently using open source Apache …
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Data Engineering with Python Paul Crickard,2020-10-23 Build monitor and manage real time data pipelines to create data engineering infrastructure efficiently using open source Apache …
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Data Engineering with Python Paul Crickard,2020-10-23 Build monitor and manage real time data pipelines to create data engineering infrastructure efficiently using open source Apache …
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• Paul Crickard (2020). Data Engineering with Python. Birmingham, UK: PacktPublishing Ltd. • https://www.stitchdata.com/resources/data-transformation/ • https://analyticsindiamag.com/top …
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How To Learn Python For Data Engineering: Data Engineering with Python Paul Crickard,2020-10-23 Build monitor and manage real time data pipelines to create data engineering …
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Data Engineering with Python Paul Crickard,2020-10-23 Build monitor and manage real time data pipelines to create data engineering infrastructure efficiently using open source Apache …
