Intro to Bulk Data Loading

Imagine someone sent you a CSV file with info about students, maybe from another database or some third-party app. Copying every single record by hand? No way, thanks. That’s exactly where bulk data loading comes in handy.

Here are a few situations where bulk data loading totally saves your day:

• Data migration: you need to move data from one system to another. For example, from Excel or another database into PostgreSQL.
• Database initialization: filling your database with starter data, like a list of cities, courses, or product categories.
• Integration with external systems: regularly adding new data, like updating info from third parties via a file.
• Updating large volumes of data: you need to swap out old data for updated stuff.

All clear so far? If yes, let’s keep going!

Main Approaches to Bulk Data Loading

Before we dive into the details, let’s quickly talk about the ways you can add not just one or two rows, but a whole bunch at once. We’ll check out how to do it with SQL queries, and then look at a more convenient and modern way—loading data from files.

SQL Queries for Inserting Data

You already know that to add data, you use the INSERT INTO command. That works great if you only have a few records. For example:

INSERT INTO students (id, name, age, course)
VALUES (1, 'Otto Lin', 20, 'Programming');

But if we’re talking dozens, hundreds, or thousands of rows, this way gets pretty inefficient. Sure, you could automate it in code, but doing that much manual inserting is a pain and not fun at all.

Loading from Files

That’s why people often use file-based data loading, like from CSV (Comma-Separated Values) files. It’s a simple text format where each line is a separate record, and values are split by commas or semicolons.

Example of what’s inside a CSV file:

id,name,age,course
1,Otto Lin,20,Programming
2,Maria Chi,21,Design
3,Alex Ming,19,Mathematics

This format is easy to read for both humans and programs. You can open CSVs in Excel, process them in Python or another language, and import them into your database without much hassle. Bulk loading from a file is way faster than manual inserts and cuts down on data entry mistakes.

Getting Ready for Bulk Loading

The secret to a smooth load is in the prep. We’ll get the database ready to accept data, check that the structure matches, and make sure the data is good to go.

Checking Table Structure

First off, make sure your database has a table that matches the file’s structure. For example, if you’re loading students, you need a table with the right columns.

Example table for loading student data:

CREATE TABLE students (
    id SERIAL PRIMARY KEY,
    name TEXT NOT NULL,
    age INTEGER NOT NULL,
    course TEXT
);

Key things to watch for:

• Make sure the column data types match what’s in your file. If age is a number, then only numbers should be in that CSV column.
• Columns with constraints (NOT NULL, UNIQUE) need extra careful data prep.

Checking the Data

Now, a quick word about the data. Here’s what you should check before loading:

1. No empty rows or bad values. For example, a row like 12,,20,Programming will throw an error, since name is a required field.

2. Matching delimiters. If your CSV uses commas (,), make sure it’s set up right. If it uses semicolons (;) instead, you’ll need to specify that when loading the data.

3. Encoding. PostgreSQL expects files in UTF-8. If your file is in another encoding (like Windows-1251), you’ll need to convert it.

A Little Programmer Joke to Lighten the Mood

Why do programmers love using CSVs? Because after hours of wrangling JSON, they finally see “simple” data... until they hit a comma in the text.

Bulk Loading in Practice

In real projects, bulk loading is everywhere. For example:

• When updating a product catalog for an online store. A CSV file with thousands of products can be loaded in seconds.
• When migrating clients from one CRM system to another. All the client data, their addresses, and orders get loaded from CSV into PostgreSQL.
• In analytics, where you need to load sales data for a month to analyze it.

Now that you get why this matters and how to prep for it, we can move on to the actual data loading tools, like the COPY command. But that’s for the next lecture.

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SQL SELF, level 23, lesson 0

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Manual Data Insertion Using INSERT INTO

Manual Data Insertion Using INSERT INTO

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