Data integration glossary: all you need to know

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In today’s data-driven environment, a growing number of businesses are realizing the importance of integrating data to optimize their potential. The global data integration market size is expected to reach $39.25 billion by 2032. Power is slowly shifting from the hands of those who simply hold data to those who can consolidate their data to create a single source of truth. 

Given the many variations in data types, architecture, systems, sources, etc. data integration technologies and strategies are constantly evolving. To keep pace, one must understand key data integration terminology. Do you know what an ETL is and how it impacts data integration? 

A shared vocabulary is fundamental to creating and maintaining sustainable integration solutions. It simplifies communication between teams involved in different stages of the integration process, reduces the risk of error, and ensures that everyone involved is aligned with the same organizational objectives. 

Including all existing data integration terms in a single glossary would be extremely lengthy. This glossary looks at the 15 most talked-about terms in terms of their definition and significance. Let’s begin. 

API (Application Programming Interface)

An API (Application Programming Interface) enables different software applications to communicate and interact with each other. An “interface contract” delineates methods and protocols for software system components to interact, ensuring smooth data exchange, access to functionalities, and seamless integration. APIs abstract the underlying complexities of software systems, providing a standardized way for developers to access the capabilities of another application or service without needing to understand its internal workings or to write everything from scratch. 

APIs facilitate the development of software by allowing developers to leverage existing functionalities and services provided by other applications, leading to increased efficiency, interoperability, and scalability of software systems.

App (Application)

In a data integration context and especially in iPaaS context, applications refer to software solutions or systems that facilitate the flow of data between different sources, databases, and solutions. They are designed to gather data from multiple sources, transform it to meet standardized formats and generate a centralized view for all systems. 

The key roles played by applications can be understood as follows:

  • Facilitating data transformation to ensure compatibility with source and target systems
  • Establish communication between systems through connectors, APIs and middleware
  • Design and execute complex data workflows and automations
  • Capture changes in source data and facilitate updates
  • Remove duplicates

BPM (Business Process Management)

Business process management (BPM) refers to end-to-end processes used for data discovery, modeling, analysis, measurement, improvement and optimization. Integration-centric BPM mostly focuses on repeatable processes that can be automated to integrate data across systems. For example, it can be used to integrate and manage Customer Relationship Data. BPM solutions make data integration more efficient, cost-effective and scalable while reducing dependency on development teams. BPM also goes farther than data integration alone; it also manages and improves the processes of a company and can include automated as well as manual actions.

BPM tools break down large processes into smaller, more easily manageable tasks and make it easier to prioritize tasks. This supports automation to make the integration process more efficient and quicker. It also ensures consistency across systems.


The build phase is the foundation of any data integration project. It’s where the initial planning, design, and construction of your automated data workflow takes place. This crucial stage involves defining your goals, choosing the right tools, and setting up the data pipelines that will seamlessly connect your data sources.

Key Steps in the Build Phase:

  • Define your needs and objectives:

What are the business problems the data integration is solving? What data do you need to integrate?

  • Choose your integration approach:

Consider your technical expertise and data complexity. There are various options of Ipaas platforms, ideal for fast integrations and no coding experience, or Enterprise solutions for building data pipelines to support a wider range of data volumes and transformations. Another solution is custom scripting. It provides flexibility and control but requires programming expertise.

  • Data mapping and transformation design:

Define how data will flow from source systems to the target system. 

  • Workflow development and error handling:

Now you can build your data integration workflow using your chosen solution. Implement robust error handling mechanisms to identify and address potential issues during data extraction, transformation, and loading.

  • Security and access control:

Establish necessary security measures to protect sensitive data throughout the integration process. This includes setting access controls and encryption protocols.

  • Testing and validation:

Test your data integration solution with various data scenarios. Validate data accuracy, consistency, and completeness across the entire workflow.

  • Deployment and documentation:

Once testing is successful, deploy your data integration solution to the production environment. Create comprehensive documentation outlining the workflow, data transformations, and troubleshooting procedures for future reference.

Simplifying the Build Phase:

Building a data integration solution doesn’t have to be complex. Modern tools, like no-code/low-code platforms and user-friendly tools, simplify the process by offering visual interfaces and pre-built components. This allows for faster development and reduces reliance on extensive coding expertise.


Connectors are tools or software components that allow data to be exchanged between different two or more systems and applications. Based on a set of protocols and rules, it extracts data from applications and systems, filters out irrelevant data, and transforms and loads data to the target system. 

Importance of connectors

This helps overcome differences in programming language, operating systems, database technology, etc. and enables seamless data integration and synchronization. In turn, this enables workflow automation and boosts efficiency. It also bridges the gap between systems that have differing architectures and protocols and promotes interoperability. 

Data connectors are available through third-party vendors or may be built in-house. 

EBO (Event-based Orchestration)

Event-Based Orchestration, or Event-Driven Orchestration, is an architectural model in which actions are initiated by events rather than by a predefined sequence. This enables actions to be triggered and the flow of data between services to be managed.

Event-based orchestration facilitates coordinating and managing interactions among various components of a distributed system in response to particular events. This includes implementing automated workflows, error handling, and ensuring data consistency in distributed and large-scale environments. This approach finds extensive application in microservices environments for efficiently handling complex business processes and dynamic workflows.

ESB (Enterprise Service Bus)

ESB or Enterprise Service Bus is a data architectural pattern where multiple applications can be integrated over a bus-like infrastructure by implementing a set of rules and principles. In essence, the ESB architecture acts as a communication bus between applications and allows them to communicate independently through the bus without dependency on other systems. 

ESB Benefits

An ESB can handle connectivity, transform data models, route messages and convert communication protocols. It increases organizational agility and provides a simple, plug-in system that can be easily scaled up to match the company’s growth. 

Using ESBs for data integration and the ability to reuse these applications allow developers to focus on improving applications and improves productivity.

ESB is most suitable for situations that involve integrating data between 3 or more applications and services. It improves visibility and control, connects legacy systems to cloud-based systems and provides a single point of access but can be complex to maintain and increase challenges for cross-team collaboration. 

ETL (Extract, Transform, Load)

ETL, a reference to Extract, Transform, Load, is a process used to bring data from multiple sources together to create a singular, comprehensive data set for data warehouses, data lakes and other such systems. As the name suggests, it involves extracting data from the source, transforming it to a pre-defined format for consistency and loading in the data warehouse. 

Need for ETL

By cleansing and organizing data, ETL ensures that data in the data warehouse meets high quality standards and is easily accessible. It also makes data easier to work with and prepares it to address specific business intelligence needs. In addition, it improves data warehouse scalability by reducing data volume through cleaning and filtering during the transformation stage. 

Some of the best practices in ETL processes include:

  • Log all events before, during and after ETL processes. This aids in troubleshooting errors and tracking data lineage (origin and transformations).
  • Perform regular audits to assess data quality and identify potential issues within the ETL processes. This helps ensure ongoing data integrity.
  • Use incremental data updates. This approach focuses on loading only new or changed data since the last run, improving processing speed and efficiency.


Extension in the context of data integration refers to adding new feature or functionality dedicated to a task that improves the processes of existing integration systems. This is aimed at expanding the capabilities of the existing systems, accommodating changes in data sources or formats or meeting new business requirements. It can also support evolving communication protocols, increased data volumes, new applications, compliance updates and technology upgrades. 

For example, extensions can be incorporated into data integration solutions such as Marjory. The ETL extension offers the ability to work with high data volumes.


As the word suggests, integration refers to the process of collecting data from multiple sources to create a single, central database that may be accessed by different applications and business processes. This results in a unified view of data free from the risk of duplication, fragmentation, inconsistent formatting and errors. 

In today’s data-driven environment, integration breaks through silos and ensures that all departments have access to the same dataset. This enables collaboration, saves time, simplifies analysis and increases the value of these reports. 

Types of Integration

Integration processes may be executed manually or automated. There are 5 common approaches to data integration:

  • ETL

Extract, Transform and Load refers to extracting data from multiple sources, transforming it and combining it in a large, central data repository. 

  • EBO

Event-based optimization (EBO) is the process of improving performance or efficiency by triggering actions or adjustments based on specific events or occurrences within a system or process.

  • ESB

An Enterprise Service Bus (ESB) is a centralized software architecture model facilitating integrations between applications. It acts as a communication system enabling interaction among software applications in a service-oriented architecture.

  • Streaming

Streaming data integration is a continuous process of collecting data, processing it, monitoring the transformation and enrichment processes and uploading it to the target database. 

  • Data Virtualization

Data virtualization helps create a unified view of data from multiple systems without moving it from its original location. 

iPaaS (Integration Platform as a Service)

iPaaS refers to a self-service cloud-based solution with the potential to standardize and simplify integration with real-time updates across on-premise and cloud environments. They usually have low-code visual interfaces making them easy to use. 

Typical iPaaS platforms have pre-built connectors that integrate data, processes, applications, services and more across departments in an organization or across companies. This may be used to create and automate workflows. For example, it could be set up to extract data from an ERP, CRM and marketing applications, format it and share it with business intelligence platforms.

Benefits of iPaaS

The best iPaaS solutions can target several prospects simultaneously and hence save time. It also minimizes the risk of error when transferring data between applications and provides real-time updates. Further, by offering a centralized view of the ecosystem, it makes it easier to identify and troubleshoot issues as well as manage compliance. 


In terms of data integration, observability refers to the process of monitoring data for quality and usefulness as well as managing it to be available across processes and systems. This is a proactive stance to identifying quality issues before they can impact analytics. 

At its core, it can be broken down into gathering data about where it is stored, profiling it and monitoring its usage. 

A good data observability strategy makes it easier to monitor and manage data flows. It aids in the early detection of issues related to accuracy, completeness, duplication and inconsistencies to minimize troubleshooting downtime and costs. It also encourages collaboration, simplifies compliance and increases efficiency

To find out more, read our article

RPA (Robotic Process Automation)

RPA or Robotic Process Automation is a data optimization method that leverages technology to automate repetitive, rule-based tasks such as extracting data from forms, data entry, verification and so on. RPA may be integrated with other forms of technology, such as Natural Language Processing and Machine Learning. 

Importance of RPA

In the case of data integration, it improves productivity and efficiency. By automating tasks, it frees human resources to work on tasks that add strategic value. The use of RPA also optimizes data integration costs, reduces the risk of error and eases compliance. That said, implementing RPA can be more challenging compared to other productivity solutions and may require organizational restructuring. 


The ‘run’ phase in data integration is where the data integration solution is actively performing the tasks required. It involves a strategy of observability by monitoring the process to ensure it is run smoothly, managing errors and deviations, and auditing the system to ensure it functions smoothly.

Key considerations for ensuring smooth operation

Some of the key practices to maintain data integration solutions are:

  • Real-time monitoring with an alert mechanism
  • Regular performance and security audits
  • Detailed error handling and response plans
  • Feedback loops for improvement

TCO (Total cost of ownership)

The Total Cost of Ownership or TCO reflects the all-inclusive lifetime costs of owning and managing any investment. The TCO of data integration projects can be calculated as the sum of the initial purchase price of the system, cost of upgrades, maintenance and deployment and amount spent operating the asset. You will need to consider the number of sources data is extracted from, transformations required and data destinations. Hidden fees such as licensing fees, servers and storage, training costs, downtime and so on must also be budgeted for. 

Here are a few effective strategies to optimize TCO in data integration:

  • Have clearly defined objectives and requirements
  • Select an integration platform that is easy to use, scalable and aligned to your business needs
  • Leverage pre-built connectors and APIs


Data has immense potential but holding data in pockets does not do much good to an organization. To ensure a seamless flow of information between systems, applications and databases, organizations must invest in data integration solutions. 

Consolidating data from all sources drives operational efficiency, supports informed decision making and facilitates the adoption of emerging technologies. That said, data integration techniques are rapidly evolving. 

Keeping up with emerging trends and strategies is key to building a future-ready data integration solution. It begins with a foundation of understanding the basic data integration glossary. Having a good understanding of key terms related to data integration is essential for the success of such initiatives in any organization.

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