Data Integration FAQ

Welcome to Climber’s Data Integration FAQ. This page answers common questions such as ‘What is data integration?’, ‘How does it work?’, and ‘What tools and approaches are used?’.

We explain key concepts, types of integration, ETL vs ELT, and real-world examples to help you understand how organisations combine data from multiple systems to support reporting, analytics, and AI-ready data platforms.

Whether you’re modernising your data estate or building from scratch, clear data integration is essential for delivering accurate, trusted insights.

1. What is meant by data integration?

Data integration is the process of combining information from multiple systems into a single, consistent view. This allows organisations to analyse performance more effectively and gain clearer operational insight. The aim is to make data accessible, reliable, and usable for reporting and decision-making, regardless of where it originates.

2. What is data integration in simple words?

Data integration is the process of bringing scattered pieces of information together so they can be used in one place. Instead of disconnected datasets, organisations can see the full picture and make more informed decisions.

3. What exactly is data integration?

Data integration is the set of technical and business processes required to access, cleanse, transform, and move data from different sources into a target environment such as a data warehouse or data lake. These processes ensure data is consistent, accurate, and ready for analysis.

4. What is another name for data integration?

Another name for data integration is data consolidation. Related terms include data unification, and data harmonisation. While closely linked, each typically describes a specific aspect of the broader integration process.

5. Are there different types of data integration?

Data integration can be approached in several ways depending on architecture and use case. At a high level, organisations typically combine the following approaches:

• Data consolidation
  Collecting data from multiple systems and storing it in a central repository such as a data warehouse or data lake, typically using batch ETL or ELT processes.
• Data ingestion patterns
  Moving or synchronising data between systems using different techniques, including:
  • Replication
    Copying data from one system to another to keep environments aligned.
  • Change Data Capture (CDC)
    Capturing and propagating only data changes, enabling near real-time updates.
  • Streaming
    Processing data continuously as events occur, supporting real-time analytics and operational use cases.
• Data federation
  Creating a virtual view across systems without moving the data, typically used where physical consolidation is impractical.

In practice, these approaches are often combined within a single data platform.

6. How does data integration relate to application and process integration?

The focus is on moving, combining, and shaping data so it can be analysed and reused.

• Application integration focuses on how systems communicate in real time, often using APIs or messaging.
• Process integration is about automating business workflows that span multiple systems, for example approvals or order‑to‑cash processes.

Both are related, but this guide focuses on how data flows into warehouses, lakes, and analytics platforms.

7. What are some real-world examples of data integration?

Data integration appears in many practical scenarios, including:

• Enabling AI and predictive models
  Preparing high-quality, well-governed datasets from multiple systems so organisations can build machine learning models, forecasting solutions, or AI-driven automation with reliable inputs.
• Feeding a data warehouse or data lake
  Consolidating operational data into a central platform to support reporting, analytics, and performance tracking.
• Powering applications and dashboards
  Combining data from multiple backend systems so mobile apps, web portals, and BI dashboards can present a consistent and up-to-date view.

In practice, whenever information from multiple systems needs to be combined and reused, some form of data integration is involved.

8. Is data integration the same as ETL?

No, ETL (Extract, Transform, Load) is one method of data integration, but not the only one. Data integration is the broader discipline of combining and preparing data from multiple systems into a unified, usable form. ETL is a specific technical process used to achieve that goal. Other approaches include ELT, data virtualisation, APIs, and real-time streaming.

9. What is the difference between data integration and ETL?

• Data integration refers to the overall strategy of unifying data for reporting, analytics, or operational use.
• ETL is a traditional implementation pattern within that strategy extracting data from source systems, transforming it into the required structure, and loading it into a target system such as a data warehouse.

In simple terms, data integration defines the objective; ETL is one way of delivering it.

10. Is DBT for ETL or ELT?

Data Build Tool (DBT) is commonly used within ELT workflows. It focuses on transforming data after it has been loaded into a data warehouse, rather than extracting or loading it. As a result, it supports the “T” in ELT, enabling teams to build and manage transformation logic directly inside modern cloud platforms.

11. Which tool is used for data integration?

There is no single tool for data integration. The appropriate platform depends on factors such as data sources, volume, transformation complexity, real-time requirements, and target architecture. Organisations typically select tools based on scalability, connectivity, governance features, and alignment with their broader cloud strategy.

12. What are data integration tools?

Data integration tools are platforms designed to automate and manage the movement, transformation, and synchronisation of data between systems. They provide connectors to source and target systems, workflow orchestration, transformation capabilities, and monitoring to support reliable, repeatable data pipelines.

13. Is SQL a data integration tool?

Structured Query Language (SQL) is not a data integration tool. It is a language used to query and manage relational databases. While SQL can support data extraction and transformation tasks, it does not provide the orchestration, automation, or connectivity features of dedicated data integration platforms.

14. Is Excel an ETL tool?

Excel is not an ETL tool. Although it can be used for small-scale data manipulation, it is not designed to manage large volumes of data, automated pipelines, or enterprise-grade governance requirements.

15. What skills do you need for data integration?

Effective data integration requires a blend of technical expertise and architectural thinking. Core technical skills include strong SQL capability, understanding of data modelling, and experience with modern data integration platforms such as Qlik Talend Cloud (QTC), Microsoft Fabric, or metadata-driven tools like TimeXtender. Familiarity with cloud environments, APIs, and orchestration frameworks is increasingly important in modern architectures.

While many platforms offer low-code design environments, the ability to work with pro-code approaches (for example using Python (Microsoft Fabric), SQL-based transformations, or even Java in Talend) remains valuable for handling complex scenarios.

16. What are some common data integration use cases?

Data integration underpins many modern business initiatives by ensuring consistent, governed, and reliable data across systems. Common use cases include:

• Creating a unified customer view
  Combining sales, marketing, and support data into a consistent profile to improve service and decision-making.
• Supporting business intelligence and analytics
  Delivering structured, trusted data into warehouses, lakehouses, or analytics platforms for accurate reporting and advanced analysis.
• Enabling AI and machine learning
  Preparing high-quality, well-governed datasets that can be safely used for predictive modelling, automation, and generative AI use cases.
• Improving data governance and lineage
  Standardising definitions, tracking data movement, and maintaining visibility over how data is sourced, transformed, and consumed.
• Data migration and modernisation
  Moving data from legacy systems to cloud-based environments while maintaining integrity and continuity.
• Supporting near real-time operations
  Replicating or streaming operational data to power time-sensitive processes such as fraud detection or inventory management.