Normalize and denormalize data

Normalization and denormalization are crucial concepts in data engineering, particularly when working with large datasets in Microsoft Azure. In this article, we will explore what normalization and denormalization are, their benefits, and how they can be implemented using various Azure services.

What is Normalization?

Normalization is the process of organizing data in a database to eliminate redundancy and improve efficiency. It involves breaking down a large table into smaller, more manageable tables while maintaining data integrity. Normalization ensures that each table in a database contains unique and non-redundant information.

What is Denormalization?

On the other hand, denormalization involves combining multiple tables into a single table to optimize query performance. Denormalization can improve read performance by reducing the number of joins required to retrieve data. However, data redundancy may increase, and updates and inserts can become more complex.

Implementing Normalization and Denormalization in Azure

Let’s explore how normalization and denormalization can be implemented in Azure using various services.

1. Azure SQL Database:

Azure SQL Database is a fully managed relational database service provided by Azure. It supports normalization and denormalization techniques. Normalization can be achieved through proper table design and normalization rules. By creating separate tables for related data, we can eliminate redundant information. Denormalization can be accomplished by creating views or materialized views that combine data from multiple tables into a single denormalized view. This denormalized view can then be queried for improved performance.

Example of normalization using Azure SQL Database:

CREATE TABLE Orders (
OrderID INT PRIMARY KEY,
CustomerID INT,
OrderDate DATETIME
);

CREATE TABLE Customers (
CustomerID INT PRIMARY KEY,
CustomerName VARCHAR(100),
CustomerEmail VARCHAR(100)
);

Example of denormalization using Azure SQL Database:

CREATE VIEW OrderDetails AS
SELECT O.OrderID, O.OrderDate, C.CustomerName, C.CustomerEmail
FROM Orders O
JOIN Customers C ON O.CustomerID = C.CustomerID;

2. Azure Cosmos DB:

Azure Cosmos DB is a globally distributed, multi-model database service. It supports both normalized and denormalized data models. Normalization in Azure Cosmos DB involves creating containers (similar to tables) for different types of entities and establishing relationships between them using foreign keys. Denormalization can be achieved by embedding related data within a single document or using the JOIN operator in queries.

Example of normalization using Azure Cosmos DB:

{
"id": "1",
"name": "John Doe",
"orders": [
{"orderId": "101", "orderDate": "2022-01-01"},
{"orderId": "102", "orderDate": "2022-01-05"}
]
}

{
"id": "2",
"name": "Jane Smith",
"orders": [
{"orderId": "201", "orderDate": "2022-01-02"},
{"orderId": "202", "orderDate": "2022-01-06"}
]
}

Example of denormalization using Azure Cosmos DB:

SELECT c.name, o.orderId, o.orderDate
FROM c
JOIN o IN c.orders
WHERE c.name = "John Doe"

3. Azure Data Lake Storage:

Azure Data Lake Storage is a massively scalable and secure data lake service. It is used for storing and analyzing large amounts of unstructured and structured data. While Data Lake Storage itself doesn’t enforce any specific data model, normalization and denormalization can still be applied when processing and analyzing data using Azure Data Lake Analytics or Azure Databricks.

Example of normalization using Azure Data Lake Storage:

// Normalize data using U-SQL in Azure Data Lake Analytics
@normalizedData =
SELECT OrderID, CustomerID, OrderDate
FROM @input
WHERE ...;

OUTPUT @normalizedData
TO "/output/normalizedData.csv"
USING Outputters.Csv();

Example of denormalization using Azure Data Lake Storage:

// Denormalize data using Spark in Azure Databricks
ordersDF = spark.read.format("csv").load("/input/orders.csv")
customersDF = spark.read.format("csv").load("/input/customers.csv")

Normalization and denormalization techniques are essential for efficient data storage and retrieval in data engineering. The choice between normalization and denormalization depends on the specific use case and performance requirements. With Azure services like Azure SQL Database, Azure Cosmos DB, and Azure Data Lake Storage, you have flexible options to implement normalization and denormalization based on your data engineering needs.