Many organisations see the hybrid cloud and multi-cloud models as critical to their present business requirements, and to their future ones too. Organisations are paying more attention to the use of containers to help with simple and automated portability and scaling that can play an important role in accelerating cloud projects and deliverables; to cloud marketplaces that can present curated catalogs and e-stores for external clients; and to AWS and Azure’s part in cloud use cases, such as virtual data lakes, analytics, SaaS integration, cloud data warehouse modernization, and machine learning in the cloud.
But many questions remain: There are concerns about security and cost management for data migration to the cloud. But opportunities are under discussion too. How might re-factoring or re-architecting apps affect their infrastructures regarding their migration plans, potentially enhancing the value of their applications?
With so many organisations considering a hybrid cloud deployment model and strategy, including multi-cloud, they are also looking for answers on how to best make the change. This is why data virtualisation has now become a prominent discussion for so many organizations.
Goodbye to Complexity
With the prevalence of distributed environments, data warehouses in multiple public clouds, private clouds and data centers, data integration and management are critical. A modern integration platform and data architecture looks at combining the on-premise and cloud data world,everything from a Teradata warehouse on-site to Snowflake in the cloud.
Structured and unstructured data from enterprise, big data, and cloud sources are unified for batch and real-time operations. AWS Cloud and Azure, for instance, enables users to quickly leverage data virtualization with cloud computing capabilities and simplify their migration journey to the cloud.
It doesn’t matter to users where data comes from or the format it’s in. And it makes it possible to switch out data sources without affecting users. The data stays in place, and the data virtualisation layer can provide data to each type of user and application in the format that best suits their needs, which the company says results in lower costs compared to traditional approaches based on data replication.
It’s important to remember that the majority of data users are casual ones as opposed to data scientists, who love to play with raw data in data lakes in their own silos. A lot of work data scientists do is focused on cleaning and preparing data.
The platform supports virtual data marts in the virtual data layer through normalized views, which can be accessed through Excel. That means data users can analyze the data using pre-built templates within Excel, with which they might be comfortable. Thus, they can focus on analyzing the data to answer their queries rather than spending time accessing the data from disparate sources.
It’s also necessary to have the ability to run in containers like Docker, which among use cases includes simplicity for moving container configurations and workloads across the cloud, and scaling up or scaling out containers in terms of needed compute capacity.
GDPR Needs Data Virtualization, Too
Data virtualization can be used for a number of scenarios. Adhering to GDPR is one among them because data virtualization can be leveraged to manage data access from a single point.
For example, If you are in the U.S., you are not allowed to have a physical copy of EU citizens’ data in the U.S.. But if a U.S. corporation has customer data in the EU, it can use data virtualization to access it for analytics or physical reporting purposes. Data stays where it is, but through data virtualization, performing global analytics on all of it is still possible.
Hybrid cloud computing is slowly becoming the standard for businesses. The transition to hybrid can be challenging depending on the environment and the needs of the business. A successful move will involve using the right technology and seeking the right help. At the same time, multi-cloud strategies are on the rise. More enterprise organizations than ever before are analysing their current technology portfolio and defining a cloud strategy that encompasses multiple cloud platforms to suit specific app workloads, and move those workloads as they see fit.
--By Ravi Shankar SVP & CMO, Denodo