Data Management for Hybrid and Multi-Cloud environment

A quick word on hybrid/multi-cloud solutions-

• Hybrid IT solutions combine private cloud architecture, public cloud solutions, and legacy in-house infrastructure to improve the overall cloud architecture. A hybrid cloud connects all these components to create a unified contained environment.
• Multi-cloud solutions on the other hand use at least two similar clouds of similar types. Some may include multiple private clouds, multiple public clouds, or even in some cases multiple hybrid clouds. Multi-cloud solutions have mostly sourced a variety of cloud service providers – such as AWS, Azure, or Google Cloud.

The new-found HMC or a Hybrid Multi-Cloud strategy is slowly gaining impetus with enterprises adopting it quickly; this drives agility and brings about cost efficiency with innovation taking the center stage. Hybrid Multi-Cloud utilizes various cloud computing services from multiple different cloud vendors, and private cloud deployment systematically distributes computing resources. It minimizes the risk of data loss since it accommodates an all-public, all-private, or a combination of both.

How should a business handle data in a Hybrid/Multi-cloud environment?

• Adopting an MDM or a master data management approach and a tool set attached to the distributed database. It also needs to be attached to any federated databases (multiple databases that appear to function as a single entity) abstracting the physical database.

• Effective use of multiple such federated databases; includes several virtual databases that abstract many distributed back-end databases. Now, this can help provide more logical views of the data such as a business view, analytic view, etc. It allows the business to leverage data in multiple ways without having to change its structure thus avoiding application disruption.

• Monitoring performance is critical as this approach is prone to network latency and dealing with platform differences. This is especially when federated databases render themselves unusable when accessed from a different cloud or platform.

• Security is key as data is presented in many places. Data-level security including security services such as encryption is imperative. Granular level data record security is also a must to ensure data is protected at the record, grouping/table, or database levels. Compliance issues for the data also need to be considered. 

• Avoid data redundancy issues as all stakeholders seek only one version of the truth from the data. Businesses shouldn’t use inconsistent formats of data for different functions.

• Data security needs to be addressed both in the database itself and in the cloud. It is a must to provide record- or object-level security, access can be allowed or disallowed based on who’s using the database. Data-level security can be a challenge with distributed data because some databases depend on the native security systems of the clouds where they’re hosted. This will need to be leveraged suitably.

• Balancing data governance and performance; the core objective of data governance on distributed data platforms is to ensure that decentralized policies govern data in a centralized manner. Enforce policies that deal with compliance and adhere to encryption norms. Govern the use of PII data and deal with recovery operations when a database fails or become corrupted. While data governance is inevitable it can slow down database performance as well. More so in the case of a hybrid/ multi-cloud environment.