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|a eng
|2 639-2
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|a Aubury, Simon.
|4 aut.
|e Auteur
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| 245 |
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|a Getting Started with DuckDB :
|b A practical guide for accelerating your data science, data analytics, and data engineering workflows
|c Simon Aubury, Ned Letcher ; [Foreword by Kris Jenkins].
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|a Birmingham :
|b Packt Publishing.
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|a Paris :
|b Cyberlibris,
|c 2024.
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|a Couverture (https://static2.cyberlibris.com/books_upload/136pix/9781803232539.jpg).
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|a L'accès en ligne est réservé aux établissements ou bibliothèques ayant souscrit l'abonnement
|e Cyberlibris
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|a Analyze and transform data efficiently with DuckDB, a versatile, modern, in-process SQL databaseKey FeaturesUse DuckDB to rapidly load, transform, and query data across a range of sources and formatsGain practical experience using SQL, Python, and R to effectively analyze dataLearn how open source tools and cloud services in the broader data ecosystem complement DuckDB's versatile capabilitiesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionDuckDB is a fast in-process analytical database. Its ease of use, versatile feature set, and powerful analytical capabilities make DuckDB a valuable addition to the data practitioner's toolkit. Getting Started with DuckDB offers a practical overview of DuckDB's fundamentals and guidance for effectively using its powerful capabilities. Through extensive hands-on examples, you'll learn how to use DuckDB to load, transform, and query a variety of data sources and formats, including CSV, JSON, and Parquet files, semi-structured data, remotely-hosted files, and external databases. You'll also find out how to leverage DuckDB's performance optimizations and friendly SQL enhancements. You'll explore how to use DuckDB's extensions for specialized applications, such as geospatial analysis and text search over document collections. In addition to working through examples in SQL, Python, and R, you'll also dive into using DuckDB for analyzing public datasets and discover the wider ecosystem of open-source tools and cloud services that supercharge DuckDB-powered workflows and applications. Whether you're a seasoned data practitioner or new to working with analytical data, this book will rapidly get you up to speed with DuckDB's versatile and powerful capabilities, enabling you to apply them in your analytical workflows and projects.What you will learnUnderstand the properties and applications of a columnar in-process databaseUse SQL to load, transform, and query a range of data formatsDiscover DuckDB's rich extensions and learn how to apply themUse nested data types to model semi-structured data and extract and model JSON dataIntegrate DuckDB into your Python and R analytical workflowsEffectively leverage DuckDB's convenient SQL enhancementsExplore the wider ecosystem and pathways for building DuckDB-powered data applicationsWho this book is forIf you're interested in expanding your analytical toolkit, this book is for you. It will be particularly valuable for data analysts wanting to rapidly explore and query complex data, data and software engineers looking for a lean and versatile data processing tool, along with data scientists needing a scalable data manipulation library that integrates seamlessly with Python and R. You will get the most from this book if you have some familiarity with SQL and foundational database concepts, as well as exposure to a programming language such as Python or R
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|a Letcher, Ned.
|4 aut.
|e Auteur
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|a Jenkins, Kris.
|4 aui.
|e Préface
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| 856 |
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