What are the most important data integration trends to watch in the coming years?

1 Cloud-based data integration

Cloud-based data integration is the use of cloud services and platforms to perform data integration tasks, such as extraction, transformation, and loading (ETL). Cloud-based data integration can offer several benefits, such as scalability, flexibility, cost-effectiveness, and performance. It can also enable data integration across different cloud environments, such as hybrid or multi-cloud, as well as on-premise and cloud sources. Cloud-based data integration can help you leverage the power and potential of the cloud for your data warehousing needs, as well as address some of the challenges of traditional data integration, such as complexity, latency, and security.

2 Data integration automation

Data integration automation is the use of artificial intelligence (AI) and machine learning (ML) to automate and optimize data integration processes, such as data discovery, mapping, cleansing, and validation. Data integration automation can help you save time, resources, and errors, as well as improve data quality and reliability. It can also help you adapt to the growing volume, variety, and velocity of data sources, as well as the dynamic and diverse needs of data consumers. Data integration automation can help you achieve faster and more accurate data integration results, as well as enhance your data warehousing capabilities and outcomes.

3 Real-time data integration

Real-time data integration is the process of capturing, transforming, and delivering data as soon as it is generated or updated, without any delay or batch processing. Real-time data integration can enable you to access and analyze data in near real-time, as well as react and respond to changing business conditions and opportunities. It can also help you support streaming data sources, such as sensors, devices, and applications, as well as streaming analytics and processing. Real-time data integration can help you gain more insights and value from your data, as well as improve your data warehousing performance and agility.

4 Data integration governance

Data integration governance is the set of policies, standards, and practices that ensure the quality, security, and compliance of data integration processes and outputs. Data integration governance can help you manage and monitor the data integration lifecycle, from source to destination, as well as the data integration architecture, components, and tools. It can also help you address the risks and challenges of data integration, such as data quality issues, data breaches, data privacy regulations, and data lineage and provenance. Data integration governance can help you establish trust and confidence in your data, as well as protect your data warehousing assets and reputation.