What is cloud data integration?

The goal of cloud data integration is to improve cloud data access and delivery across the organization, while ensuring data remains secure, governed and performant as part of a broader enterprise data management strategy. These foundational capabilities are especially critical as organizations seek to adopt AI, improve customer experience and scale real-time analytics amid the exploding volume, velocity and variety of data.

Under the umbrella of cloud data integration sits two subtypes: hybrid cloud data integration and multicloud data integration.

• Hybrid cloud data integration: Integrates data residing in public cloud, private cloud and on-premises infrastructure.
  
  
• Multicloud data integration: Integrates data residing in cloud services from more than one cloud provider.

Today, most enterprises operate in hybrid multicloud environments that span public and private cloud services from multiple providers. In this model, cloud data integration provides the foundation for keeping data accessible, trusted and usable wherever it resides.

Storing enterprise data in the cloud offers clear advantages, most notably the eradication of hard storage limits and the ability to easily store massive amounts of big data. Other common benefits include cost efficiency, scalability and improved business continuity.

Due to these advantages, organizations have moved data to the cloud at a rapid pace (while also keeping data on premises to meet performance or regulatory requirements). Some forecasts project enterprise cloud storage spending to reach USD 128 billion by 2028.¹ Others estimate that the amount of data being stored worldwide will double between 2024 and 2029.²

Now, enterprise cloud data—one of an organization’s most critical assets—is increasingly distributed across hybrid and multicloud environments in a wide range of structured and unstructured formats.

This disparate data has led to fragmented data landscapes with information siloed across teams, platforms and environments, making it a challenge for teams to use data. At the same time, the volume of data being generated by apps, Internet of Things (IoT) devices and transaction data continues to grow across both cloud and on-prem systems.

Cloud data integration can significantly help address this complexity. It combines and harmonizes data across cloud and on-prem environments. This unified view makes cloud data accessible and usable for analysis and decision-making. In an era of rapid innovation and increasingly fragmented data, this capability is essential.

Fragmentation can stifle innovation and lead to slow, inconsistent or inaccurate decisions, limiting an organization’s ability to innovate, adapt and achieve operational efficiency. In fact, according to data from the IBM Institute for Business Value, 68% of surveyed CEOs say integrated enterprise-wide data architecture is critical to enabling cross-functional collaboration and driving innovation.³

Artificial intelligence (AI) initiatives, in particular, depend on data that’s unified, trusted and consistent. Without a strong data integration strategy, organizations might struggle to operationalize AI at scale.

Cloud data integration follows typical data integration steps but can differ in operational order and technical specifics, specifically in how pipelines are designed to orchestrate data movement and processing across distributed cloud and hybrid environments.

1. Data source identification: In cloud data integration, data sources have cloud-specific characteristics. They commonly include cloud data warehouses, software as a service (SaaS) and cloud applications, cloud object storage systems and hybrid source systems that span on-premises legacy systems and cloud-based data stores.
   
   
2. Data extraction: Data is extracted from cloud and hybrid sources using cloud-native tools and processes that commonly support incremental and near-real-time data ingestion (in addition to traditional batch where necessary). Extraction typically involves application programming interfaces (APIs) or connectors, both managed and built in.
   
   
3. Data mapping: Mapping schemas define how data elements from different systems correspond to each other. This step helps ensure data alignment during integration. In cloud data integration, mapping processes must accommodate the schema drift common in cloud and SaaS sources.
   
   
4. Data loading: Extract, load, transform (ELT) is the dominant approach for cloud data integration, in which data is loaded into the target system (such as a data warehouse, data lake or data lakehouse) before transformation. This step uses scalable cloud storage and cloud computing to efficiently move large volumes of cloud data.
   
   
5. Data transformation: Data transformation converts and enriches data into a common format to support accuracy and downstream compatibility. Transformations typically use cloud-native services and follow an ELT approach, leveraging parallel processing and continuous operation for on-demand data access in cloud environments.
   
   
6. Data validation and quality assurance: Quality controls help ensure data accuracy and quality by checking for errors, inconsistencies and data integrity issues. Automated validation checks are used at scale to maintain consistency across data formats, codes, types and ranges.

Just like traditional data integration, cloud data integration offers a wide array of benefits, including:

• Unified data access
• Data quality and consistency
• Scalability and resilience
• Accelerated innovation

Organizations implementing cloud data integration can face a range of technical and operational challenges spanning governance, performance, real-time processing and deployment models.

• Governance, security and compliance
• Performance and scalability
• Real-time data integration
• Hybrid deployments

Integrating data across systems increases the number of potential attack vectors—and with it the risk of unauthorized access and exposure of sensitive information. Beyond data security concerns, customer data transfer across regions, jurisdictions or cloud environments may be subject to varying legal and data residency requirements. Organizations must ensure that data flows comply with applicable regulations such as GDPR, HIPAA and PCI DSS.

Data encryption (for data in transit and at rest), strong authentication, permissions and authorization at every integration point can help mitigate these risks. A robust data governance framework can help strengthen security too. Data integration platforms with built-in security features and compliance certifications can help reduce operational overhead, while client-managed or locally hosted platforms offer greater control over security protocols, compliance enforcement and infrastructure management.

Balancing performance, cost and complex data is a core challenge of cloud data integration. Unless data integration tools are designed to scale, they can struggle to handle large data volumes. Overloaded ingestion pipelines may slow data processing, introduce business process delays, create inconsistent outputs and drive up costs.

Organizations can prioritize solutions that support high-throughput connectors, parallel processing and partitioning to break down large datasets. Built-in monitoring and observability features can provide end-to-end visibility into data flows and storage resource utilization to prevent bottlenecks, ensuring high performance regardless of data volume fluctuations. Choosing the right integration approach is also critical. For example, ELT pipelines transform data after loading, leveraging the elastic compute power of cloud platforms or data warehouses to process data at scale.

Real-time or near-real-time data integration is increasingly critical for businesses. Immediate decision-making, AI workloads and other time-sensitive operations require continuous streams of fresh data. However, real-time data integration is technically challenging, especially at high data volumes where low-latency processing is required. Distributed cloud architectures can add additional latency and network reliability concerns.

Cloud data integration solutions that support event-driven architectures (EDAs) enable systems to communicate and exchange data in real time. The increased adoption of EDAs in cloud-native environments marks a major shift away from traditional batch-oriented architectures toward more dynamic, responsive architectures that process events (data records) as they occur.

Change data capture (CDC) is another real-time integration method many solutions support. It captures and delivers data changes as they occur to different target systems, enabling near-real-time data synchronization.

Many businesses have regulated on-premises workloads (for instance, datasets stored in Oracle Database, IBM Db2 or SQL Server) that exist outside the cloud. In these scenarios, a fully cloud-based data integration deployment isn’t practical as interoperability challenges can occur between on-premises systems and cloud platforms.

A hybrid deployment helps address these challenges by processing data where it already resides and running the pipelines in the same environment (whether in the cloud or on premises). These functionalities help reduce the complexity of integrating legacy and cloud-native systems. They can also prove cost-effective, helping to reduce tool sprawl. 

Hybrid data integration deployments use remote engine execution, a cloud-native pipeline development model that decouples design time and runtime. Pipelines are designed centrally and run in the target environment—cloud to cloud, cloud to on-premises, and on-premises-to-cloud workloads. This flexibility has compounding benefits including reduced data movement, lower egress costs and minimized network latency.

There are many use cases for leveraging AI to accelerate, streamline and optimize data integration processes. Examples include machine learning-assisted schema mapping, natural language processing (NLP) interfaces for data transformation, generative AI for creating synthetic data and AI-powered techniques to improve data replication.⁴

Agentic AI is also an emerging, modern data integration capability that allows data teams to express integration requirements using natural language. Based on these inputs, the agent can then autonomously propose integration design plans—and then continuously assist with optimizing workflows over time as data environments and business needs change.

These agentic capabilities help data engineers design and execute data pipelines more quickly and reduce time-consuming efforts, such as manual data entry and data migration. They can also reduce delays for non-technical users, who are often unable to access data without the help of data engineering teams.

As with other AI initiatives, successful adoption depends on keeping humans in the loop, alongside maintaining strong AI governance and continued transparency.