Understanding Data Import Fundamentals

1. Understanding data import fundamentals

Welcome to this course on importing data in Java using Tablesaw!

2. Meet your instructor!

I'm Anthony, a VP Quant Developer and Analytics Lead at an investment bank, and I'll be your instructor. In this first video, we'll cover the basics of handling data files in Java, skills that lay the groundwork for using Tablesaw's powerful analysis tools later on.

3. Data import fundamentals

Importing data is essential as most useful data lives outside our program, in files, databases, or web services. We often work with formats like CSV for tables, JSON for structured data, or Excel files. The import process follows five steps: identify the data source, access it, read the data, validate it, and store it. Java offers great support for this via the java.io and java.nio packages, which we'll explore in this video.

4. File handling basics

Let's start with Java's File class, which represents a file or directory path. We can create a file object, then use methods like .exists(), .length(), and isDirectory(), which help us avoid runtime errors before reading data. In the code below we use these three methods, first we use exists(), which checks if our file exists, then we use length() to check the length, and finally we check if the file is a directory using isDirectory().

5. The Path interface and Files class

Modern Java applications often use the newer Path interface and Files class from java.nio. Here, we create a Path object instead of using the Files class. Then, we use static methods like Files.exists() and Files.size(). This approach offers more flexibility, better exception handling, and performance - ideal for complex data imports. So when you are working with simple file operations, use java.io, and if you are working with more complex, high-performance input/output operations, use java.nio.

6. Reading text files

After locating the file, we need to read its contents. The Files class offers two helpful methods: .readAllLines() reads the entire file into a List of lines - perfect for CSV files where each line typically represents a data record. .readString() reads everything into a single String, useful for certain parsing approaches. Both methods are convenient, but have a limitation - they load the entire file into memory, which isn't suitable for large datasets. For those cases, we'll explore buffered reading later in this chapter.

7. Data validation

Data validation is critical but an often overlooked step. Before processing, we must check data quality and structure, perform common validations, and handle any exceptions.

8. Data validation

Our example shows two essential checks: first, we verify the file isn't empty by checking if the lines list has content; second, we examine the header row for required columns like "id" and "name". We also wrap the code in a try-catch block to handle Exceptions, making the import process more robust and error-tolerant.

9. Summary

Here is a summary of the syntax we have covered. We've covered file handling using both the traditional File class and the modern Path interface. We've learned to read files with methods like readAllLines() and readString(), and implemented basic validation techniques to ensure data quality.

10. Let's practice!

Now, let's import some files!

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