Cloud data platform definition
A cloud data platform is an online hub that offers tools for storing, managing, analyzing, and processing data from different sources. Many companies now prefer cloud data platforms over traditional, in-house storage systems. While they both serve similar purposes, the cloud option offers several advantages.
See also: cloud operating system, cloud archiving
Advantages of a cloud data platform
- Easy access. Data is accessible online, and remote team members can work with the same data from different locations.
- Cost savings. Instead of setting up and maintaining a complex and costly system, a business can use a pay-as-you-go model.
- Flexibility. If an organization’s data needs grow or reduce, it can easily scale its cloud data platform resources up or down.
- Safety net. Many cloud data platforms automatically back up data, protecting it from unexpected losses or system failures.
- Less IT overhead. A business doesn't need a big IT team to handle the technical management and maintenance. The cloud provider takes care of that.
History of cloud data platforms
- Late 20th century: Before cloud computing, companies stored data in on-premises databases and data warehouses. These systems were robust but costly, and every time a company needed more space, it had to make a big investment.
- Early 2000s: Companies like Amazon and Google started creating virtual storage spaces to handle the massive amounts of their own data. Recognizing the potential of renting this space out, Amazon launched AWS (Amazon Web Services) in 2006. That became the foundation for cloud computing.
- Late 2000s: With tools like AWS's S3 (Simple Storage Service), businesses began seeing the benefits of storing their data online. For example, it meant being able to grow without buying more hardware.
- Late 2000s – early 2010s: New cloud services, like Google App Engine (2008) and Microsoft Azure (2010), popped up. They allowed developers to create, run, and scale applications without worrying about the underlying infrastructure.
- 2010s: The internet exploded, and data was everywhere — from Facebook posts to smart fridge logs. Online tools like Amazon's Redshift and Google's BigQuery helped manage this data avalanche.
- Mid 2010s – late 2010s: Realizing that one size doesn't fit all, companies started using a mix of on-premises, private cloud, and public cloud solutions. Services like Azure Arc and Google Anthos came up to make switching between these platforms smoother.
- Late 2010s – early 2020s: Serverless computing models, like AWS Lambda, let developers run code without managing servers. At the same time, massive online storages called data lakes became popular. They allowed companies to store structured and unstructured data in its raw form.
- 2020s: Companies like Snowflake offered all-in-one platforms combining storage, processing, analysis, and AI capabilities. That enabled simple management of the entire data lifecycle in the cloud.
