3 Reasons Object Storage Analytics is So Complicated
There is huge growth in the amount of data that needs to be stored. By 2025, we will generate 163 zettabytes of data, an increase of 10x over current levels. On average, less than 1% of that data is stored, even less for business value.
Data needs to be saved and examined in order to create value and enable businesses to make decisions, compete, and innovate. This has led to a huge growth in object storage; but has also led to a dilemma. It’s easy and cheap to persist data in an object storage format. But historically, it has not been easy to identify, standardize, and analyze the object storage data to draw meaningful conclusions.
So, why is object storage analytics still so complicated and distant?
1. Object Storage is Not a Database
Although the concept of object storage has been around since the mid-1990s, it only began to gain true popularity after its adoption by Amazon Web Services (AWS) in 2006. AWS called it Simple Storage Service (S3) and is now massively popular e.g. if a storage region goes down, the internet virtually stops. Today, anything and everything is being sent to S3, and this deluge of data increases hourly. Some of the key factors that have contributed to growth and popularity of object storage include:
Read the entire article here, 3 Reasons Object Storage Analytics is So Complicated
via the fine folks at Chaos Sumo