Data virtualization creates a unified view of data from different sources without physically moving it. It allows for real-time access and integration of diverse data.
Data management project sustainability and scalability are key factors in ensuring the long-term success of a project. By implementing proper data governance practices, utilizing scalable infrastructure, and leveraging advanced data management technologies, such as cloud platforms and data lakes, organizations can achieve sustainability and scalability in their data projects.
Data integration techniques are crucial in Big Data projects for combining and consolidating diverse data sources to provide a unified view. The commonly used techniques in Big Data projects include Extract, Transform, Load (ETL) processes, Change Data Capture (CDC), and data virtualization. ETL processes involve extracting data from multiple sources, transforming it to match the target system requirements, and loading it into a data warehouse or data lake. CDC techniques capture and replicate data changes in real time to keep the data synchronized across systems. Data virtualization enables access to data stored in different systems without physically moving or replicating it.
