Database to Database Integration: A Practical Guide

As your business grows, data can get scattered across various isolated systems. Different departments use varied database systems that cater to their specific needs. This fragmentation of data makes it difficult for your teams to access and manage information effectively, which may lead to misaligned decisions.

Integrating databases directly allows you to optimize the data flow, streamline data operations, and ensure everyone has access to the most accurate information.

In this article, you will discover how database to database integration can help you streamline your workflows and improve your decision-making process.

What is Database to Database Integration?

Database-to-database integration is the process of connecting two or more databases. This integration allows you to automate the data transfer process and ensures critical information flows smoothly across the organization.

You can implement integration between databases on different environments, including on-premises cloud or hybrid setups. For instance, you can integrate data between the on-premises MySQL and Oracle cloud databases to scale your data storage and processing capabilities.

Why Integrate Two or More Databases?

 Here are some advantages of integrating two or more databases:

• Streamlined Operations: Data sharing becomes easy when you integrate databases. The operational teams no longer have to rely on manual data requests, which slows down the workflow. For example, you can integrate a database that manages transactional data with another database containing customer interaction. This helps you generate reports on customer transactions and improve service delivery.
• ‍Optimizing Unique Database Capabilities: By integrating databases, you can leverage their distinct strengths and improve how you handle data. For example, you can move MySQL data into a vector database, but first, you’ll need to convert it into vector embeddings. These embeddings are designed to handle tasks like similarity searches in NLP.
• ‍Minimized Manual Efforts: A database-to-database integration automates data flow, reducing the likelihood of human errors. Automation also minimizes the delays caused by team members when entering data, improving operational efficiency.

Types of Database Integration

You can implement any of the following database integration methods for effective data management:

Real-time vs. Batch Database Integration

Real-time integration is a method where data is extracted and transferred from the source to the destination database as soon as it is collected. This type of integration is ideal for applications that require up-to-the-minute information, such as reservations and online transactions.

Conversely, in batch integration, you can collect data over time and process it in groups or batches at scheduled intervals. It is suitable for scenarios where you can wait to receive and analyze information, such as monthly report generation, maintaining index files, or subscription systems.

Full vs. Incremental Data Synchronization

Full data synchronization is copying all data from the source database to the target database. It is used when a complete refresh of data is needed.

Alternatively, in incremental data synchronization, only the data that has changed after the last synchronization is transferred from the source to the destination database. This helps keep the records up-to-date.

Unidirectional vs. Bidirectional Database Integration

Unidirectional integration is the method by which data is transferred from database A to database B. The flow of data is one way. This setup is commonly used for scenarios where data is primarily extracted for analysis.

On the other hand, in bidirectional integration, the flow of data is two-way. The data can be transferred from database A to database B and vice versa. This type of integration is helpful for real-time updates and for maintaining data consistency across systems.

Common Database to Database Integration Scenarios

Here are some common database integration scenarios that are used to address the specific needs of organizations:

Migrating from Legacy to Modern Databases

Legacy systems refer to the old technologies or systems that an organization still uses for performing day-to-day operations. These systems often lack scalability and modern features to handle modern data needs. You can migrate data from these legacy database systems to modern databases to implement real-time analytics and improve performance efficiency while preserving historical data.

Combining Data from Multiple Sources

Your organization likely stores valuable information in various database systems, including CRM, ERP, and other applications. A database to database integration enables you to merge diverse datasets into one centralized location, making it easier to manage and analyze data.

Syncing Databases Across Different Locations

If your organization operates across multiple offices, syncing data between database systems ensures that all teams can access up-to-date and exact information. This helps maintain data consistency across different time zones or regions and enables efficient collaboration at a global scale.

Critical Considerations While Integrating a Database to another Database

Here are some critical steps to consider while planning database to database integration:

• Assess the Scope: You must evaluate the scope of your project before implementing a database to database integration. This involves understanding the data you will be working with. You must also assess data complexity factors, such as whether the data structure is plain text or nested structure and whether it will require transformation.‍
• Select Data Integration Tool: You must choose a data integration tool that will support the data sources involved in the integration. It would be beneficial if the tool had automation capabilities.‍
• Data Transformation: The data transformation process involves converting data into a format compatible with the target database by implementing techniques like data cleansing, aggregation, and normalization. These aspects ensure the data is transferred accurately and improve its usability for downstream applications.‍
• Handling Schema Differences: A database has its own schema and data structure, which it uses to store the data. You need to address these differences so that the destination database can accommodate incoming data smoothly.
• ‍Managing Data Conflicts: During data integration, overlapping or inconsistent data can be merged, which can cause inaccuracies, duplication, or loss of critical information. You can address these data conflicts by implementing techniques like timestamps and version controls. These techniques help you keep track of changes and maintain data accuracy throughout the integration process.‍
• Data Governance: You must implement data governance practices to ensure that your data is secure, private, and available for use. Some of these practices include encryption algorithms, access controls, and monitoring. These ensure that your data is protected from unauthorized access.‍
• Data Loading: This step involves loading the transformed data from the source database to the destination database. It is important to monitor this process for any interruptions or errors.‍
• Data Validation: Data validation helps ensure consistency during the synchronization process. It involves performing validation checks, such as checking data types and data formats' accuracy and implementing transaction-based validation to maintain data integrity.

Integration Methods

Below are some common approaches that you can use to implement a database to database integration:

ETL (Extract, Transform, Load) Processes

ETL integration method involves extracting data from a source, transforming it, and then loading it into the destination database. For example, a retail company can extract sales data from the different store databases, transform it, and load it into a centralized database for analytics or reporting purposes.

API-Based Integration

API-based integration is the process of using an application programming interface to connect two or more software or applications and transfer data between them. It provides a standard way to communicate and share data between systems without direct database access. For example, the food delivery platform may integrate with Google Maps API to give users visibility of their orders.

Database Replication Techniques

Database replication is the process of copying data from a primary database and storing it in multiple databases. Data distribution across multiple systems enhances data availability and accessibility across your organization. Some of the common data replication techniques include full table replication, snapshots, and transactional replication.

For example, a retail company may use full table replication to copy its product inventory table in multiple regional databases. This ensures each sales team across different locations has access to the latest inventory information.

Middleware Solutions

Middleware solution facilitates communication between applications and databases using an intermediary. The intermediary handles data translation, formatting, and routing. This is specifically useful when different systems have varying formats or protocols. For example, a company can use middleware to integrate data from API to database.

How does Airbyte Help with Database to Database Integration?

Airbyte is a robust AI-powered data movement platform that simplifies data integration through automation. With 170,000+ deployments, Airbyte has become the most used data movement solution in the world. It offers a wide range of database connectors, which you can use to build a database-to-database pipeline without extensive coding.

Along with this, Airbyte offers connector builder options, such as Python CDK, Java CDK, and low-code connectors, to build custom connectors tailored to your needs. Almost 10,000+ connectors have been built through the Airbyte connector builder.

Key Features of Airbyte

• AI Assist: The newly launched AI assist feature of Airbyte helps to speed up the process of building custom connectors. It helps you pre-fill configuration fields, such as URL Path and Authentication, when you are building a custom connector.
• ‍Support to Vector Databases: Airbyte supports various vector databases such as Pinecone, Chroma, and Weaviate. By integrating a vector database into your Gen AI workflow, you can efficiently process and retrieve vectorized data for various NLP tasks. This improves data handling and makes it easier to build and scale Gen AI applications.‍
• Change Data Capture: Change data capture allows you to identify the incremental changes within the source data and then replicate them in the destination database.
• ‍Self-Managed Enterprise Service: Airbyte’s self-managed service provides features like multi-tenancy, role-based access, PII masking, and support for SLAs. These facilitate complete control over data and enhance data management and security.

Step-by-Step Database to Database Integration Process

Here is an example of integrating data from MySQL to Qdrant using Airbyte. As you are migrating data from MySQL to a vector database, you might have to convert the data into vector embeddings to be stored in Qdrant. Airbyte provides features like automatic chunking and indexing that allow you to transform raw data and store it in a vector database. Airbyte is also compatible with various LLMs, such as Open AI, Cohere, and others, which can be utilized to generate vector embeddings.

‍Prerequisites:

• An Airbyte Cloud account.
• A MySQL account. You must create a user with read-only permissions to replicate data.
• An account with access to API access for Open AI, depending on which embedding method you want to use.
• A Qdrant database instance and its API credentials. (This can be for local or cloud environments).

Follow the steps below for connecting MySQL to Qdrant.

1. Set up MySQL Source Connector:

Login to your Airbyte cloud and select Sources. On the Set up New Source page, search for and select MySQL connector. Next, provide mandatory details like Source name, Host, Port, Database, Username, and Password.

2. Set up Qdrant Destination Connector:

Navigate to the Destinations on the homepage. On the Set up a new destination page, search and choose Qdrant. Provide all the necessary configuration details for Processing, Embeddings, and Endpoint access. Choose a sync mode and set up additional options.

3. Set up the connection:‍

Click on the Connections. On the New Connection page, select MySQL as the source and Qdrant as the destination. Provide a name for your connection and set the replication frequency.‍

4. Start sync to transfer data from MySQL to Qdrant.

You just need to follow these three easy steps to migrate your data between any databases.

Practical Use Cases for Database to Database Integration

Here are some practical use cases of database to database integration:

• Cross-Database Analytics: Cross-database analytics helps you combine data from different sources and run queries upon it to identify correlations that might be missed otherwise. For example, a retail company can integrate sales and CRM database systems. The integration facilitates analysis of relationships between various data entities, such as custom demographics, purchasing patterns, and marketing campaign effectiveness.‍
• Real-Time Synchronization: In the healthcare sector, you can link EHR systems with lab testing databases. This integration ensures that updates in patient information or test results are instantly reflected across both systems. It allows healthcare providers to access the latest patient information in real time.

Conclusion

Database to database integration helps ensure seamless data flow, improve accessibility, and maintain data consistency across various systems within your organization. There are many different ways to implement database integration, including real-time, batch, incremental, and bidirectional. Selecting the appropriate approach will help you synchronize data between databases and manage data effectively.