Hybrid cloud environments, combining on-premises infrastructure with cloud services, are the preferred IT architecture for most organizations today. However, this model creates challenges, such as the inadvertent creation of data silos, making it difficult to share data across systems and applications. When mixing on-prem and cloud resources, it’s important to realize that different platforms will have unique data storage formats, access protocols, and management systems, creating interoperability issues.
Data Collaboration Techniques
Organizations must adopt strategies and technologies that enable data integration, interoperability, and governance across various platforms to reduce the risk of stranded data.
Reflect on the following optimal methods:
- Define clear data governance policies for handling, accessing, and sharing data across various environments.
- Ensure interoperability by standardizing data formats for different cloud providers and on-premises systems, such as JSON and XML.
- Create a standardized communication method for applications and services using cloud-agnostic APIs.
- Make applications easier to move across different cloud providers by containerizing them.
- Utilize data virtualization to present a unified view of data regardless of its location or format, instead of consolidating data into a central repository.
Embrace Automation for increased productivity:
It is preferable for organizations to automate as many data management tasks as possible to minimize the risk of errors and inconsistencies. Automation tools used for on-premises data pipelines can also enhance data sharing in a hybrid environment.
A data pipeline consists of automated processes and tools that collect, clean, and transport raw data to a storage or analytics environment. Tools like Apache NiFi, Luigi, Azkaban, Apache Oozie, and Control-M can automate data flow and management in hybrid cloud environments, improving efficiency and accuracy.
Manage expenses with automated data management tools. Reduce costs by implementing data compression techniques. Minimize additional costs due to data transfer failures with error-handling mechanisms
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