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Data-driven decision-making is now an integral part of the business landscape. Every organization needs an integrated view of data from various sources for in-depth analytics and research. However, data integration becomes more challenging as data grows exponentially. To overcome these challenges, traditional data warehousing methods have evolved into more efficient data management approaches such as data mesh and data fabric. 

Understanding data integration architecture patterns and best practices is essential when implementing advanced data integration across your organization. This article explores various data integration approaches in detail and includes several examples. Let’s explor these design patterns in greater detail. 

Summary of data integration architecture design patterns

The table includes some of the most common design patterns organizations use today.

Design Pattern Description 
Extract, Transform, Load (ETL) Extracts data from multiple sources, transforms it for analytics and loads it into a database or data warehouse.
Extract, Load, Transform (ELT) Extracts data from the source system, loads the raw data into a data lake or warehouse and then transforms the data within these environments.
Change Data Capture (CDC) Records a history of real-time data changes in the source system for near-to-real-time data updates in the target environment.
Application Programming Interface (API) Uses a set of rules and parameters to enable communication between different software, allowing data to be integrated and shared in real time.
Federated Data Mesh Federated data meshing platforms like Nexla allow you to create customized data products from data collected from pre-built connectors for a no-code end-user experience.
Event-Driven Architecture  An architectural approach where specific changes in the state of a software system trigger the transmission of ‘events,’ that are then picked up and processed by other services within the system. You get real-time responses, enhancing system dynamism and flexibility.

Now that you have a general understanding of each design pattern, let’s look deeper into these concepts. 

Pattern #1: Extract, transform, load 

The Extract, Transform, Load (ETL) pattern is most commonly used for data integration between multiple sources. This process involves extracting data from various sources, transforming it for analytics, and then loading the transformed data into the target environment—typically a data warehouse. You can use several ETL tools to load data to a centralized warehouse.

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Code example

The following code snippet demonstrates the ETL data integration architecture pattern. We extract data from a CSV file, transform the data, and load it into a PostgreSQL database using Python and Pandas 

import pandas as pd
from sqlalchemy import create_engine

# Extract data from CSV file
data = pd.read_csv('data.csv')

# Transform data
data['date'] = pd.to_datetime(data['date'])
data['sales'] = data['units_sold'] * data['unit_price']

# Load data into database
engine = create_engine('postgresql://user:password@localhost/mydatabase')
data.to_sql('sales_data', engine, if_exists='replace')

In this example, we use the Pandas library to extract data from a CSV file and transform the data using various functions. Finally, we use the SQLAlchemy library to load the transformed data into a PostgreSQL database.

ETL with No-code and low-code platforms 

If you want to skip the heavy coding exercise, no-code or low-code platforms simplify the ETL process by providing pre-built connectors, intuitive data mapping interfaces, data cleansing features, and seamless integration with data warehouses. Think of a retail company that wants to integrate sales data from various sources, such as online stores, point-of-sale systems, and customer surveys. These advanced platforms can easily facilitate the ETL process:


Having a platform with pre-built connectors for popular data sources – including online stores, databases, and survey platforms – streamlines the data extraction process. You can use these connectors to connect with each source and extract relevant data. You can also set up automated schedules or triggers to extract data at specific intervals or based on predefined events. 


You can utilize intuitive visual programming interfaces to map the extracted data fields to a standardized schema or format for consistency. Imagine if you could map different product codes to a unified product identifier using a simple drag and drop feature. You can also apply data cleansing techniques within platforms like Nexla to remove duplicates, correct inconsistencies between data schemas, and handle missing values. Optionally, you can enhance extracted data by integrating additional information. For instance, improve customer data with demographic data from a third-party source.


You can connect no-code and low-code platforms to any data warehouse, such as Amazon Redshift, Google BigQuery, or Snowflake. For instance, these platforms provides connectors to facilitate seamless integration with multiple platforms. All you have to do is define the mapping rules to load the transformed data into appropriate data structures within the warehouse. These platforms can handle both batch and streaming loading scenarios.

Pattern #2 Extract, load, transform 

The Extract, Load, Transform (ELT) method involves extracting raw data from various sources and loading it into a target data lake or warehouse without transforming it. Instead, you can transform the unprocessed data within the target environment using tools like SQL, Hadoop, or Spark. This approach allows for faster data loading and enables data processing and transformation to occur more flexibly. In contrast to the ETL method, the ELT data integration architecture pattern:

  • Handles large volumes of data more efficiently.
  • Allows for semi-structured and unstructured data processing.

Code example

Here is a code snippet demonstrating how to perform data quality checks and transformations using ELT data integration architecture.

In this example, we:

  1. Create a staging table to hold the raw data from our source.
  2. Load the data into the staging table using the COPY command.
  3. Perform data quality checks on the staging table to ensure complete and accurate data. 

In this case, we check the number of rows in the table and count the number of unique ids to check for duplicate records. Finally, we perform data transformations on the staging table by creating a new table called transformed_table and selecting the id, name (transformed to uppercase), and address columns from the staging table.

-- create a staging table for the source data
CREATE TABLE staging_table (
  id INT,
  name VARCHAR(50),
  address VARCHAR(100)

-- load the raw data into the staging table
COPY staging_table FROM 's3://mybucket/mydata.csv' CSV DELIMITER ',' HEADER;

-- perform data quality checks on the staging table
SELECT COUNT(*) AS num_rows FROM staging_table; -- check the number of rows in the table
SELECT COUNT(DISTINCT id) AS num_unique_ids FROM staging_table; -- check for duplicate records based on the id column

-- perform data transformations on the staging table
CREATE TABLE transformed_table AS
SELECT id, UPPER(name) AS name, address
FROM staging_table;

This is just a simple example. In a real-world scenario, you might perform more complex data transformations and manipulations to get the data into the desired format for your downstream analytics or business intelligence applications.

ELT with no-code and low-code platforms 

Next, let’s explore how to simplify the ELT process when utilizing advanced data operations platforms like Nexla. Leveraging these platforms for building ELT processes simplifies the entire data integration workflow. It provides intuitive interfaces for mapping and transformation, ensures data quality, and streamlines data loading. These factors make the process significantly more straightforward than hardcoding the entire ELT process.

Let’s consider a scenario where a retail company wants to integrate sales data from various sources, such as online stores, point-of-sale systems, and customer surveys, into a centralized data warehouse for analysis. Using Nexla’s ELT capabilities, here is a high-level overview. As the ELT process shares similarities with the previously discussed ETL process, we have only given a summary below.


Pre-built connectors connect with different data sources, enabling seamless extraction.

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Integrates with the data warehouse, such as Amazon Redshift or Google BigQuery, allowing direct loading of extracted data.


Provides intuitive interfaces to map and transform the data during loading, ensuring data consistency and quality.

Pattern #3 Change data capture 

The Change Data Capture (CDC) data integration architecture pattern allows for real-time tracking of changes made to data sources. With CDC, you record and store changes in a separate database, enabling quick and efficient analysis of data modifications over a given period. Capturing changes in near real-time allows for timely decision-making and ensures that data is always up-to-date. 

Code example

Below is a code snippet for implementing the CDC data integration architecture. This code creates a table to hold changes and a trigger to log changes to the table. The IF statements in the trigger determine whether an insert, update or delete operation has occurred and set the @Operation variable accordingly. The INSERT statement then inserts the relevant information (table name, operation, record ID, and change date) into the ChangeLog table. You can then easily track and monitor changes to the data in the source table.

--Create a table to hold the changes
CREATE TABLE dbo.ChangeLog (
    ID int IDENTITY(1,1) NOT NULL,
    TableName varchar(128) NOT NULL,
    Operation char(1) NOT NULL,
    RecordID int NOT NULL,
    ChangeDate datetime NOT NULL

--Create a trigger to log changes to the table
CREATE TRIGGER dbo.trg_LogChanges
ON dbo.MyTable
    DECLARE @Operation char(1);

    IF EXISTS(SELECT * FROM inserted)
        IF EXISTS(SELECT * FROM deleted)
            SET @Operation = 'U';
            SET @Operation = 'I';
        SET @Operation = 'D';

    INSERT INTO dbo.ChangeLog (TableName, Operation, RecordID, ChangeDate)
    SELECT 'MyTable', @Operation, COALESCE(i.ID, d.ID), GETDATE()
    FROM inserted i
    FULL OUTER JOIN deleted d ON i.ID = d.ID;

CDC with no-code and low-code platforms

Next, let’s discover how you can leverage a no-code and low-code platform’s built-in Change Data Capture (CDC) features to make the data integration process significantly more accessible and efficient.

Consider a financial institution that tracks real-time changes in transaction data from various sources, such as banking systems, payment gateways, and online transactions. Here’s how it can leverage CDC features.

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Data source integration

Connects various data sources, including databases, cloud services, and streaming platforms. You can configure these connectors to establish connections with your transactional data sources, such as your core banking system, payment gateways, or online transaction platforms.

CDC configuration

Once the connectors are set up, you can configure CDC for the relevant data sources. For example, you can capture changes in transaction records, including updates, inserts, or deletes, from your banking system’s transaction tables.

Real-time change detection

Continuously monitors the defined data sources for changes. When changes occur in the specified tables or data collections, the platform captures the relevant data and identifies the nature of the change. For instance, if a new transaction is recorded or an existing transaction is updated or deleted in your banking system, the platform detects and captures that change in real-time.

Data transformation

Enables you to apply data transformations and enrichments to the captured data after capturing the changes. You can define rules and mappings to format the data, extract specific fields, or enrich it with additional information from other sources. For example, you can transform and enhance transaction data by adding customer details from your CRM system or performing currency conversions based on exchange rate data.

Routing and integration

Allows you to route the captured and transformed data to various destinations or downstream systems. You can configure destinations such as data warehouses, analytics platforms, or other systems that require real-time transaction data. For instance, you can route the transformed transaction data to a data warehouse for further data analysis or to a fraud detection system for real-time monitoring.

Pattern #4 Application programming interface

An Application Programming Interface(API) is a data integration architecture pattern that enables two different systems to communicate and exchange data in a standardized and efficient manner. APIs define a set of protocols for building software applications that allow for the seamless integration of data from different sources, whether internal or external, into an organization. 

APIs allow real-time, secure, and reliable data exchange between systems. You can access and integrate data from various sources, including web services, databases, and cloud-based applications. This flexibility and ease of integration make APIs a critical design pattern for data integration in modern applications.

Code example

Here’s an example of how the API data integration architecture pattern could be implemented using Python and the requests library. In this example, we:

  1. Specify the API endpoint URL and request it using the requests library. 
  2. If the request succeeds, we extract the JSON data from the response and perform data quality checks and transformations.

The data quality checks ensure that each JSON record has a ‘name’, ‘age’, and ’email’ field. If any of these fields are missing, we print out an error message specifying which record is missing the field.

import requests

# API endpoint URL
url = ""

# Request data from API
response = requests.get(url)

# Check if the request was successful
if response.status_code == 200:
    # Extract JSON data from response
    json_data = response.json()
    # Perform data quality checks
    for record in json_data:
        if 'name' not in record:
            print('Error: Missing name field for record:', record)
        if 'age' not in record:
            print('Error: Missing age field for record:', record)
        if 'email' not in record:
            print('Error: Missing email field for record:', record)
        # Perform data transformations on the data
        record['age'] = int(record['age'])
    print('Error: Request failed with status code', response.status_code)

The data transformations involve converting each record’s ‘age’ field from a string to an integer. You can accomplish a data type change using Python’s int() function. Of course, the specific data quality checks and transformations that you perform depend on the requirements of your particular use case.

API integration with no-code and low-code platforms

Platforms like Nexla make it easy for anyone to work with data APIs and get the data they need where they need it. You can choose from a wide range of API templates—from specific API types like Facebook Ads API and Shopify to fully customizable ones for connecting with any API like APIs developed internally in your company.

API integration in no-code and low-code platforms follow a three-step process:

  1. Select the API template
  2. Authenticate the API with the required credentials.
  3. Configure the API as needed.

Add access credentials for your API. The platform supports all authentication types for SOAP and REST APIs such as OAUTH1, OAUTH2 and refreshing token-based authentication. You have to input information like URLs, usernames, passwords, and token refresh settings in the platforms API interface and save the credential. After configuring your API source to your liking, the platform automatically detects and creates a Nexset that encapsulates a complete understanding of your API data. Any new data with a similar schema is added to this same flow.

Pattern #5 Federated data mesh

Federated Data Meshing is a modern and innovative data integration architecture pattern incorporating a data product approach. It democratizes data through a self-service platform that provides

  • Pre-built data connectors to popular data sources.
  • No-code and low code= approach to data transformation in a graphical user interface. 

The concept of federated governance enables more efficient management and data control across the organization. For example, Nexla’s data mesh platform makes data integration accessible to less technical users. Business users can become self-sufficient and not rely on software developers to access and analyze data. This approach allows organizations to define and implement data policies, enforce standards and ensure compliance while safely democratizing data access.

Data Integration Architecture Example (Source)

Data Integration Architecture Example (Source)

Pattern #6 Event-Driven Architecture

Event-driven architectures are a cutting-edge and responsive approach to data integration, primarily dependent on real-time data operations. It works by triggering ‘events’ based on specific changes within a software system, which are then captured and processed by other components in the system. 

Event-driven architectures have different components or modules that work together and respond to changes in real-time. When a specific event occurs within the system, it acts as a trigger, alerting other components to process the event and perform their tasks accordingly. It ensures that data is integrated and processed efficiently, enabling the system to respond quickly and adapt to dynamic situations.

This model offers:

  • Real-time data processing capabilities allow for an immediate response to changes.
  • Enhanced system agility, can quickly adapt to fluctuating data inputs and conditions.
  • Facilitation of decoupled systems, allowing individual components to operate independently, increasing overall system adaptability.

This architecture pattern is at the core of systems used in real-time analytics, IoT devices, and dynamic web applications. The event-driven approach boosts operational efficiency and supports reactive, user-focused applications. It empowers businesses to respond instantaneously to customer interactions or market changes, promoting accelerated data-driven decision-making and improved customer engagement.

Additionally, event-driven architecture can facilitate integrations with third-party systems, such as analytics platforms or customer relationship management (CRM) tools like Salesforce, by triggering events and sharing relevant data for further analysis or business insights.

High-level example of event-driven architecture (Source)

High-level example of event-driven architecture (Source)

Data integration best practices

The key to success in any data project is following best practices. Below are some of the data integration best practices to follow across any data integration architecture pattern.

Define data goals

Defining data goals helps align your data integration project with business objectives and provides a clear roadmap. It involves understanding the business requirements and identifying the critical data elements you must integrate.

Build a data catalog

A data catalog contains information about all your data sources—such as their location, format, and quality. Automated discovery tools identify new data sources, analyze the data, and determine its relevance to the project. You can prioritize and sort your data sets for security, confidentiality, and integration value. The catalog guides the what and when of data integration.

Data Mapping and Transformation

A standardized data model ensures that all data is integrated consistently and can be used effectively. You map existing data sources into the data model by identifying the relationships between data elements. This process ensures that data transforms correctly and integrates effectively.

Cleanse & prepare your data

Data cleansing ensures that data is accurate, consistent, and error-free. You identify changes to the data’s structure, format, and content to fit your target data model. Data cleansing and transformation help to ensure that the data is of high quality and can be used effectively.

Run validation checks

Validation involves checking the accuracy and completeness of the integrated data. Automated data validation at various stages of integration improves accuracy. In addition, validation identifies and corrects data errors early in the process, reducing the risk of downstream issues.

Monitoring data quality issues and alerting

Establish policies and procedures to maintain the data over time by leveraging data quality monitoring and alerting capabilities. Monitoring data quality and performance in real-time improves data accuracy, consistency, and high quality. You also ensure that the integrated data meets business requirements and is suitable for reporting. 

You can ensure effective monitoring and alerting of data integration processes by implementing robust mechanisms. For instance, you can configure notifications to promptly detect and address failures, delays, or any irregular data patterns, facilitating prompt troubleshooting and resolution.

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Data integration has become increasingly essential in today’s data-driven business landscape. Traditional data management methods have evolved into more efficient approaches such as data lakes, data fabrics, and several design patterns. You should use advanced data integration architecture patterns and best practices to utilize your data effectively.

Nexla’s cutting-edge platform allows for streamlined data integration and modeling through its innovative Nexset feature. With Nexsets, users can quickly and easily standardize their data by leveraging standard data schemas, saving time and effort in the modeling process. By utilizing Nexsets, businesses ensure data consistency across all their sources, resulting in higher-quality insights and better decision-making.

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