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F# 4.0 Design Patterns

You're reading from   F# 4.0 Design Patterns Solve complex problems with functional thinking

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Product type Paperback
Published in Nov 2016
Publisher Packt
ISBN-13 9781785884726
Length 318 pages
Edition 1st Edition
Languages
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Author (1):
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Gene Belitski Gene Belitski
Author Profile Icon Gene Belitski
Gene Belitski
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Table of Contents (14) Chapters Close

Preface 1. Begin Thinking Functionally 2. Dissecting F# Origins and Design FREE CHAPTER 3. Basic Functions 4. Basic Pattern Matching 5. Algebraic Data Types 6. Sequences - The Core of Data Processing Patterns 7. Advanced Techniques: Functions Revisited 8. Data Crunching – Data Transformation Patterns 9. More Data Crunching 10. Type Augmentation and Generic Computations 11. F# Expert Techniques 12. F# and OOP Principles/Design Patterns 13. Troubleshooting Functional Code

Data parsing

The parsing of data is absolutely essential for the enterprise. As an enterprise F# developer at Jet.com, I come across this data transformation pattern on a daily basis. Every case of LOB applications' integration with a third-party system - ERP, Bank, or Carrier - involves data parsing on the ingesting edges. Despite a plethora of integration technologies around that promise great data quality, timeliness, integrity, you name it...time and again, I am forced by my contractors to deal with flat fixed format files, CSV files, and Excel files. This is the boring reality of today.

On this battlefield, the weaponry varies from case-by-case hand-coded solutions based on Regex and F# active patterns to fairly generic solutions targeting whole classes of incoming data with F# type providers. Some typical examples of semi-generic solutions are invoices in the form of CSV files and Excel files that are to be persisted in the SQL server for further processing, reconciliation...

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