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Michael Conterio,
Martin O'Hanlon,
Ross Exton,
and
Eirini K
Topics Covered
- Creating, reading from, and writing to files using Python
- The importance of data persistence
- Structuring data using CSV files, Python dictionaries, and JSON files
- How data structures aid compatibility between systems
- Interacting with databases using SQL and Python
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Reading list
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Covers data persistence in the cloud using various cloud computing platforms. It teaches the basics of cloud computing and how to use it for data persistence.
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Covers using Spring Data, a framework for data persistence in Java, for data persistence. It teaches the basics of Spring Data and how to use it in application development.
Covers best practices for data persistence, including how to choose the right data persistence method and how to design and implement a data persistence layer. It also covers common pitfalls and how to avoid them.
Focuses on using Hibernate, an object-relational mapping (ORM) framework, for data persistence. It teaches the basics of Hibernate and how to map objects to a relational database.
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Covers using JPA, a standard for object-relational mapping (ORM) in Java, for data persistence. It teaches the basics of JPA and how to use it in application development.
Covers designing and building data-intensive applications. It discusses various types of data persistence, including file-based, database, and cloud-based, and how to choose the right type for an application.
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Covers using Redis, an in-memory data structure store, for data persistence. It teaches the basics of Redis and how to use it in application development.
Covers using NHibernate, an ORM framework for .NET, for data persistence. It teaches the basics of NHibernate and how to use it in application development.
Covers data warehousing, a specific type of data persistence used for business intelligence and data analysis. It teaches the basics of data warehousing and how to design and implement a data warehouse.
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Covers the internals of databases, including how data is stored and retrieved. It teaches the basics of database internals and how to use this knowledge to optimize database performance.
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Provides a comprehensive guide to CSV file management with JavaScript. It covers topics such as reading, writing, parsing, and validating CSV files. It valuable resource for anyone who needs to work with CSV files in JavaScript.
Provides a comprehensive guide to CSV file processing with C++. It covers topics such as reading, writing, parsing, and validating CSV files. It valuable resource for anyone who needs to work with CSV files in C++.
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Provides a comprehensive guide to CSV file processing with R and dplyr. It covers topics such as reading, writing, parsing, and validating CSV files. It valuable resource for anyone who needs to work with CSV files in R and dplyr.
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Provides a comprehensive guide to CSV file processing with PHP. It covers topics such as reading, writing, parsing, and validating CSV files. It valuable resource for anyone who needs to work with CSV files in PHP.
Shows how to use JSON in Node.js, a popular JavaScript runtime environment. It good choice for developers who want to use JSON in web applications.
Quick reference guide to the JSON specification. It good resource for developers who need to quickly look up information about JSON.
Is written by the creator of JSON, Douglas Crockford. It provides a definitive guide to the JSON specification, as well as insights into the design and implementation of JSON.
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Collection of articles from experts in the field of JSON. It covers a wide range of topics, from the basics of JSON to advanced techniques for using JSON in web applications.
Provides a comprehensive guide to CSV file management with Python and Pandas. It covers topics such as reading, writing, parsing, and validating CSV files. It valuable resource for anyone who needs to work with CSV files in Python and Pandas.
Provides a comprehensive guide to CSV data analysis with Python. It covers topics such as data cleaning, data exploration, and data visualization. It valuable resource for anyone who needs to analyze CSV data in Python.
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