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Time Series Analysis with Python Cookbook

You're reading from   Time Series Analysis with Python Cookbook Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation

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Product type Paperback
Published in Jun 2022
Publisher Packt
ISBN-13 9781801075541
Length 630 pages
Edition 1st Edition
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Author (1):
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Tarek A. Atwan Tarek A. Atwan
Author Profile Icon Tarek A. Atwan
Tarek A. Atwan
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Table of Contents (18) Chapters Close

Preface 1. Chapter 1: Getting Started with Time Series Analysis 2. Chapter 2: Reading Time Series Data from Files FREE CHAPTER 3. Chapter 3: Reading Time Series Data from Databases 4. Chapter 4: Persisting Time Series Data to Files 5. Chapter 5: Persisting Time Series Data to Databases 6. Chapter 6: Working with Date and Time in Python 7. Chapter 7: Handling Missing Data 8. Chapter 8: Outlier Detection Using Statistical Methods 9. Chapter 9: Exploratory Data Analysis and Diagnosis 10. Chapter 10: Building Univariate Time Series Models Using Statistical Methods 11. Chapter 11: Additional Statistical Modeling Techniques for Time Series 12. Chapter 12: Forecasting Using Supervised Machine Learning 13. Chapter 13: Deep Learning for Time Series Forecasting 14. Chapter 14: Outlier Detection Using Unsupervised Machine Learning 15. Chapter 15: Advanced Techniques for Complex Time Series 16. Index 17. Other Books You May Enjoy

Chapter 5: Persisting Time Series Data to Databases

It is very common that, after completing a data analysis task, in which data is extracted from a source system, processed, transformed, and possibly modeled, the output is stored in a database for persistence. You can always store the data in a flat file or export to a CSV, but when dealing with a large amount of corporate data (including proprietary data), you will need a more robust and secure way to store it. Databases offer several advantages, including security (encryption at rest), concurrency (allowing many users to query the database without impacting performance), fault tolerance, ACID compliance, optimized read-write mechanisms, distributed computing, and distributed storage.

In a corporate context, once data is stored in a database, it can be shared across different departments; for example, finance, marketing, sales, and product development can now access the data stored for their own needs. Furthermore, the data can...

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