Скачать или смотреть How to Parse Data in Oracle 11g for Effective SQL Queries

Learn how to efficiently parse single column data in Oracle 11g and split it into multiple columns using SQL queries.
---
This video is based on the question https://stackoverflow.com/q/76450025/ asked by the user 'Md. Sajjad Hussain' ( https://stackoverflow.com/u/11063450/ ) and on the answer https://stackoverflow.com/a/76451251/ provided by the user 'Littlefoot' ( https://stackoverflow.com/u/9097906/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Parsing data in Oracle 11g

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
How to Parse Data in Oracle 11g for Effective SQL Queries

When working with databases, you may occasionally find yourself with data in a format that isn't immediately useful. This often happens when you have multiple pieces of information stored in a single column. In this guide, we'll tackle the challenge of parsing data in Oracle 11g and converting it into a structured format that facilitates analysis and further processing.

The Problem

Imagine you have a column in your Oracle 11g database that contains various pieces of data, such as age, city, university name, subject name, match percentage, and phone number, all mixed in a single entry. Here's a sample of what that data might look like:

[[See Video to Reveal this Text or Code Snippet]]

In essence, you need to take this unstructured data and split it into discrete columns for better clarity and usability:

[[See Video to Reveal this Text or Code Snippet]]

The Solution

To effectively parse and organize this data in Oracle 11g, we can use an SQL query that involves the following steps:

Step 1: Create a Temporary Table

For demonstration purposes, let’s assume we have the raw data stored in a temporary table called test. Here’s how you can structure the data to make it useful:

[[See Video to Reveal this Text or Code Snippet]]

You should see results like:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Split the Column Values

To split the values and extract the necessary details, we can rely on a Common Table Expression (CTE) that separates the values based on their position in the dataset.

Here's the SQL query you can use:

[[See Video to Reveal this Text or Code Snippet]]

Step 3: Understanding the Query

This query works by performing the following tasks:

Common Table Expression (CTE): It creates a virtual table tsplit, which separates the values into rows based on line breaks.

Case Statements: For each potential row, a MAX function paired with a CASE statement captures the relevant information. The SUBSTR and INSTR functions are employed to extract the data following the colon (:), isolating the necessary values from the string.

Result Extraction

After running the SQL script, you'll generate an output that clearly delineates between the age, city, university, subject, match percentage, and phone number, much like this:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

Parsing data in Oracle 11g might initially seem daunting, especially when dealing with mixed strings and different data formats. However, by following the outlined approach, you can transform unstructured data into a well-organized format with just a few SQL commands.

If you find that the assumptions made in this query don't fit your data as you expected, don't hesitate to adjust the query. The principles of splitting, case expressions, and string manipulation remain the same.

By applying these techniques, you'll enhance your data management skills and streamline your SQL queries for more effective database operations.