A hybrid cloud environment that combines on-premises infrastructure with cloud services is the preferred IT architecture for most organizations today. However, the model creates some challenges. In particular, the inadvertent creation of data silos often makes it difficult to share data across systems and applications. In one survey, more than half of organizations using hybrid cloud said finding data spread across on-premises and cloud environments is much harder than it should be.
When mixing on-prem and cloud resources, it’s important to realize that different platforms will have likely unique data storage formats, access protocols and management systems. Plus, different cloud providers usually have proprietary interfaces and tools that can create interoperability issues. Such inconsistencies make it difficult to seamlessly integrate and share data across the hybrid infrastructure.
Data Sharing Strategies
To mitigate the risk of stranded data, organizations need to implement strategies and technologies that facilitate data integration, interoperability and governance across diverse platforms.
Consider the following best practices:
- Establish clear data governance policies to define how data should be handled, accessed and shared across different environments.
- Standardize data formats to ensure interoperability across different cloud providers and on-premises systems. Formats such as JSON and XML are commonly supported.
- Use cloud-agnostic application programming interfaces to create a standardized way for applications and services to communicate.
- Containerize applications to make them easier to move across different cloud providers.
- Instead of consolidating data into a central repository, use data virtualization to create a virtual layer that presents a unified view of the data regardless of its location or format.
Opt for Automation
Ideally, organizations should automate as many data management tasks as possible. Manually performing tasks such as data entry, validation and reconciliation increases the risk of inaccuracies, duplications and misinterpretations that compromise data quality. Some organizations are discovering that the automation tools they use to orchestrate on-premises data pipelines can improve data sharing in a hybrid environment.
A data pipeline is a series of automated processes and tools that ingest raw data from various sources and transport it to a data warehouse, analytics platform or application. During this process, the data is cleaned, standardized and enriched to meet specific requirements. The transformed data is then loaded into a target storage or analytics environment, making it accessible for analysis, reporting or other business purposes.
For example, Apache NiFi is an open-source data integration tool that automates data flow between various systems. NiFi uses a graphical interface to design and manage data flows, making it accessible for users with different levels of technical expertise. NiFi processors are configured to ingest data from diverse sources, perform transformations as needed and then route the processed data to both on-premises and cloud-based destinations. Luigi, Azkaban, Apache Oozie and Control-M are other workload automation tools that can be used in hybrid cloud environments.
Controlling Costs
Automated data management tools also help control costs. Cloud providers typically charge egress fees for moving data out of the cloud to an on-premises source, and large data transfers can add thousands of dollars to a company’s monthly bill. Automated tools can implement data compression techniques to reduce the volume of data transferred during both ingress and egress. Automated systems can also implement error-handling mechanisms that reduce the chances of data transfer failures, which could incur additional costs due to retransmission or manual intervention.
Businesses today pull data from hundreds of different sources to help them identify and evaluate market trends, customer preferences and operational challenges. That becomes exceedingly difficult when those data sources are scattered across different cloud and on-premises repositories. Technologent can help you implement the tools and strategies you need to extract the maximum value from your data. Contact us to learn more.
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