Data Integration Process – Key Architectural Patterns And Concepts

Data integration is the process of unifying data from multiple disparate sources and making it accessible to other applications or for reporting and analytics. Implementing any data integration from zero can be a herculean task. Data integration tools help to reduce this effort by automating most of the work. However, many types of data integration tools exist, with many different architectural patterns, design choices, and limitations to consider.

This article explores various data integration techniques, the factors to consider in tool selection, and best practices for effectively adopting modern, low-code data integration techniques.

Summary of key concepts in the data integration process

Importance of data integration in modern enterprises

Data’s ever-increasing volume, variety, and velocity have made manual integration impractical. It is essential to adopt a scalable and automated data integration process that breaks down data silos and integrates various data types, such as APIs and IoT data sources, for several different reasons.

A unified data view

Data integration combines data from various sources into a single view, eliminating inconsistencies and redundancies. The unified view democratizes access by providing all stakeholders with consistent data across different tools. Consistent data leads to consistent results and decisions, not to mention fewer errors. 

Enhanced strategic decision-making

With the right data, executives can identify trends, uncover correlations, and spot potential issues early. Comprehensive dashboards and reports support proactive decision-making that improves efficiency and helps align departments around common goals. Data integration also helps make real-time data available to the executives for quicker decision-making. 

Data synchronization for operations

Data integration facilitates quick and reliable access to the data required for advanced AI, machine learning, and predictive analytics. Integrated data systems streamline data flows for real-time analytics, crucial for personalized marketing, predictive maintenance, supply chain optimization, and other applications.

Real-time data access for analytics and generative AI applications

Data integration is also used to support generative AI (GenAI), feed data into vector databases, support queries for retrieval-augmented generation (RAG), and model training and fine-tuning. A data integration platform ensures that the underlying data for GenAI is timely and relevant.

Data integration process—implementation techniques 

An organization can implement its data integration process using various techniques depending on the scope, needs, and resources defined by the business. 

ETL

Extract, Transform, Load (ETL) is the process of extracting raw data from disparate sources, transforming it, and loading it into a data warehouse, lakehouse, or other unified store to meet analytical requirements. The steps in the process are as follows:

1. Extract data from various sources, such as databases, on-premises or SaaS applications, flat files, APIs, and other systems. Depending on the use case, this extraction can be done in batches or in real-time. 
2. Transform data by cleaning and formatting to meet destination system requirements. 
3. Load the transformed data into the destination system, typically a data warehouse or data lake. Depending on the organization’s needs, this loading can be done incrementally, meaning only new or updated data is loaded or in bulk.

ETL tools like Informatica, Talend, and Microsoft SQL Server Integration Services are often used for implementation. By transforming and validating data up front, ETL ensures only quality-assured data enters the warehouse. Data enters downstream systems in a pre-defined, consistent format, making it easier to maintain quality and manageability across complex, distributed architectures.

Example

A real-world example of an ETL process is a retail company’s data pipeline consolidating sales data from various sources, such as e-commerce platforms, CRM systems, and on-premise databases like SQL Server. This data is then transformed through enrichment and aggregation in a Spark environment using advanced analytics platforms like Databricks. It is loaded into a centralized data warehouse on-premises, such as Teradata or Oracle, or cloud data warehouses, lakehouses, or data lakes, such as Snowflake, BigQuery, Amazon Redshift, Databricks, or just cloud storage for analytics or data science. Finally, dashboards and reports are created using tools like Tableau or Power BI to give managers insights into customer behavior, sales trends, and overall business performance.

ETL Process

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ELT

Extract, Load, Transform (ELT) is another data integration technique that extracts data from various sources. Still, instead of loading it to a staging area for transformation, it loads the raw data directly into the target data store or data warehouses to be transformed as needed. ELT is useful for delivering a high volume of raw and unstructured data to the target store faster if the priority is to retrieve the data quickly. ELT is much more common with cloud data warehouses, lakehouses, or data lakes that support elastic computing in the cloud. 

The steps taken to perform ELT are as follows: 

• Extract: Extract the raw structured or unstructured data from various external systems, such as SQL/NoSQL Databases, files, websites, and APIs.
• Load: Load the extracted data as it is into a data lake (e.g., AWS S3, Azure Data Lake Storage (ADLS)) or a centralized storage system such as a cloud data warehouse (Snowflake) using tools such as AWS Glue, Azure Data Factory, standalone ELT or change data capture (CDC) vendors.
• Transform: Once your data reaches your data lake or warehouse, implement transform tasks such as cleaning, aggregation, and enrichment processes. ELT scales with the cloud data warehouse.

Example

A real-life example of ELT is seen in an e-commerce company analyzing customer purchasing behavior. Using a no-code ETL tool like Nexla, the company extracts raw data from Shopify, including orders, customer details, and product information, and loads it directly into Snowflake without any changes. Once the data is in Snowflake, SQL transformations are performed to clean, join, and aggregate the data. This transformed data power dashboards in Tableau to visualize sales trends and customer segmentation. It is also a recommendation engine that suggests products to customers based on their purchase history. By leveraging ELT, the company quickly ingests data, scales transformations, and efficiently supports multiple analytical use cases.

Reverse ETL

Reverse ETL is moving data out of a data warehouse and into applications or operational tools, such as Customer Relationship Management (CRM) platforms, marketing automation systems, sales enablement tools, or other destinations your teams use every day. This approach reverses the traditional ETL process, enabling businesses to leverage their centralized data for actionable insights, personalized customer experiences, and better operational decision-making.

How it works

Reverse ETL queries data from a data warehouse, transforms it if necessary, and then writes the results to the chosen downstream tool. These destinations can include:

• CRMs: like Salesforce, HubSpot, or Zendesk.
• Ad platforms like Google Ads, Facebook Ads
• Marketing automation platforms like Marketo, Klaviyo, or Braze.

Example

Reverse ETL is beneficial when businesses have use cases beyond visualizations and dashboards and are focused on leveraging transformed and cleansed data for sales and marketing efforts. This approach is instrumental in enhancing marketing strategies, such as analyzing customer behavior and creating personalized marketing campaigns, customer 360 views, or just making sure operational data is properly cleansed and enriched. 

Change data capture (CDC) and data streaming

Change Data Capture (CDC) is used in ETL and ELT to capture changes in source databases and update downstream systems. Rather than batch-loading entire databases, CDC transfers only the change data. This technique was used for database mirroring and replication and later for supporting real-time analytics and data warehousing. It ensures that data in the target systems is always current and in sync with the source. CDC is one of the least invasive ways to replicate data from source databases. 

Log-based CDC 

When a new transaction enters a database, it is first written to a transaction log file before being committed into the target system. Log-based CDC is the most efficient method as it adds the least load to the source database and is reliable and fast.

Trigger-based CDC

When the raw data in the source changes due to INSERT, UPDATE, or DELETE, a trigger can be used to create a log that tracks the changes in all transactions. Using a trigger adds a load to the database.

Query-based CDC

It detects and captures data changes by running queries that compare the current data state with a previous snapshot. This method requires a timestamp column. It is generally used when transaction logs aren’t accessible or when the source system doesn’t support log-based CDC. The approach has several shortcomings, including high latency between updates and slow database performance due to the polling of tables in every query run.

CDC approaches 

• Debezium is the most widely used open-source CDC option, which utilizes a log-based method. Its architecture revolves around connectors for various databases, such as MySQL, PostgreSQL, SQL Server, and MongoDB. These help capture data changes as streams from the source system and sync the data into the target system.
• Nexla natively supports CDC for all leading databases and data warehouses.

API integration 

API  integration enables different software applications to communicate and share data seamlessly through a well-defined contract. APIs, or application programming interfaces, are rules and protocols that define how an application must connect to another to fetch data. APIs come in different forms, including REST, SOAP, Remote procedure calls, and WebSockets. 

Example

A real-life example of API integration is consolidating customer data into a unified CRM (Customer Relationship Management) system. It pulls data from e-commerce, marketing, and other sources. Middleware or ETL processes standardize the data structure for accurate storage. The integration enables real-time or scheduled data synchronization. For example, if a new order is placed on the e-commerce platform, the integration updates the CRM in real time to reflect this purchase, enhancing data freshness and accuracy.

Nexla offers simple yet powerful connectors that can be tailored in minutes without coding. It seamlessly manages authentication methods, headers, and tokens, allowing users to access the needed data without dealing with complex API calls or coding. Once configured, Nexla effortlessly handles data updates, including schema changes, and handles scalability and updates, making API integration and creating new APIs that expose data (data services) easy.

Data virtualization 

Data virtualization delivers a unified view of federated data across different systems in near real-time, making it available on demand. It creates a virtual or logical layer that combines data from various systems, presents it to users in real-time, and ensures that the data is always up to date. It is well suited for situations where data needs to remain in its source systems. 

Virtualizing data enables businesses to leverage their existing data from internal and external sources on demand. AI and analytics initiatives streamline data management for cutting-edge applications like predictive maintenance, fraud detection, and demand forecasting.

Example

An example of data virtualization is a business intelligence (BI) tool that accesses customer data from multiple sources, such as a CRM system, ERP database, and cloud storage. This tool provides a near-real-time “Customer 360” dashboard of customer interactions, order history, and inventory levels without physically moving or replicating the data. It lets customer service representatives instantly access up-to-date information, improving response times and the overall customer experience.

Key factors while choosing a data integration platform

Choosing the right data integration platform for your organization depends on various factors, including its features, such as connector support, the type of data integration it offers, whether batch-based or real-time and the integration patterns it supports. Additionally, it is essential to consider the data integration techniques the platform supports, such as ETL, ELT, Reverse ETL, CDC, or API integration, based on your business use case. Other key considerations include whether the platform is cloud-based or on-premises, which may better suit traditional databases, and the pricing plans each data integration platform offers.

Core platform capabilities

Here is a comparison table between data integration tools, from open source to enterprise, based on their cost of building, maintaining, switching, and other system-level concerns.

Ease of use

Organizations should opt for a user-friendly tool that reduces the learning curve, especially for teams with varying technical expertise. Features like drag-and-drop interfaces and low-code approaches to implementation can speed up deployment and make the tool accessible to a broader audience. 

For example, Nexla’s 300+ connectors allow you to create flexible, automated data flows that consistently move your data source to its target system. This low-code/no-code approach reduces the required build time and enables inspection of the output when requested. 

Nexla leverages GenAI and Autogen to create production-grade agentic workflows, enabling users to build complex data transformations through natural language prompts. Nexla Orchestrated Versatile Agent automates Python or SQL generation, allowing users to handle complex tasks like metric aggregation without coding expertise, empowering them to focus on insights.

Comprehensive connector support

Data integration platforms must have comprehensive connector support to seamlessly connect to various data sources, systems, and formats. This includes native support for diverse databases, cloud storage, APIs, file systems, and third-party applications, enabling users to effortlessly access, integrate, and process data from multiple endpoints. 

Nexla’s expansive connector capabilities offer a powerful and flexible solution for integrating diverse data sources and destinations. A key feature is the Universal Bidirectional Connectors, which allow data to flow seamlessly in both directions—pulling data from various sources and pushing it to multiple destinations. These connectors support hundreds of data sources, from traditional databases and cloud storage to modern APIs and streaming platforms, streamlining data integration across systems.

AI ready platform

With Gen AI becoming a key success factor in any enterprise, it is essential to ensure that the data integration platform is also AI-ready. An AI-ready platform provides features required for quick training and deployment of modern Gen AI architectures. RAG-based workflows and LLM-based chatbots are common in contemporary data enterprises.

• On a high level, building a RAG requires the following steps: 
• Data Ingestion and Chunking: Seamlessly pull data from APIs, relational databases, SFTP servers, or other sources and automatically split the data into manageable chunks.
• Create Embeddings: Transform the data by converting text into embeddings using OpenAI’s API or similar tools.
• Write to Vector Database: Store the embeddings directly in a vector database for instant retrieval and efficient processing.

Hence, the data integration platform must seamlessly connect to vector databases like Pinecone, Weaviate, Chroma, etc., and support LLM endpoints like Open AI, Gemini, Bedrock, etc. Nexla’s agentic RAG features provide a prebuilt production-grade RAG implementation that supports NVIDIA NIMs.

Transformation logic support

Templated transformations can be beneficial in implementing an ETL process. They provide reusable patterns for everyday data manipulation tasks such as data cleaning, filtering, and normalization. This reduces development time and effort for data engineers by eliminating the need to write custom code for each specific use case. In more complex tasks, modifying the transformation logic by quickly updating the custom code to accommodate new data formats or business requirements can ensure that the data pipelines remain efficient and effective in dynamic environments without starting from scratch. 

Nexla allows engineers to integrate custom and pre-built templates and write logic tailored to their unique data needs. This makes the engineers more efficient in quickly building their ETL pipelines by making the transformation stage quick, allowing organizations to quickly access data in their BI dashboards or data warehouses for analytics. 

Governance and compliance support

An enterprise data integration platform handles the organization’s and customers’ data. Such data requires strict adherence to compliance standards like GDPR, SOC, etc. The platform must also support encryption, PII masking, and record-level lineage tracking. Another key element of ensuring compliance is role-based access control. Platforms with granular role-based access control make it easy to adhere to data governance policies.  

Error handling, testing, and validation

Data pipelines encounter errors for various reasons, such as data type changes or schema evolution. Data integration platforms must be able to log errors globally, generate notifications, and automatically retry them. Platforms with built-in AI assistance can also automate data mapping and schema changes, apply data quality checks automatically, and detect anomalies, such as unusual data patterns or inconsistencies across integrated data. 

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Platform	Data Extraction	Data Warehousing	No-Code Automation	Auto-Generated Connectors	Data as a Product	Multi-Speed Data Integration
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Conclusion

Data integration is a foundational aspect of modern data management. It enables organizations to unify data from disparate sources, transform it, and make it accessible for analysis and decision-making. Implementing effective data integration poses several challenges, such as handling diverse data formats, maintaining data quality, ensuring real-time synchronization, and balancing extensibility with operational costs. Choosing the right platform requires evaluating capabilities such as ease of use, connector support, transformation flexibility, and compatibility with modern AI workflows.

Nexla addresses these challenges through its robust, AI-ready platform. Its extensive connector ecosystem, support for many different integration patterns, low-code/no-code approach, and reusable transformation templates drastically reduce integration time and effort. Nexla’s automation features and GenAI-powered enhancements make it an ideal choice for organizations looking to streamline data integration while maintaining agility and scalability. By leveraging Nexla, businesses can enhance the quality and speed of their data workflows, empowering better insights and driving innovation across their operations.