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shutterstock When you work with large-scale data, it’s important to “…know when to hold ‘em, know when to fold ‘em…”. Most modern businesses rely on large-scale data, so it’s natural to focus on the best ways to quickly ingest and store huge amounts of data. But people often overlook how to delete data in very large-scale systems. It may sound simple, but it becomes a non-trivial task when data sets are in the tens of terabytes to petabyte or even exabyte scale. The challenge of deleting large-scale data is a widespread issue that can have significant consequences if not done properly. For example, think about the ramifications of not following auditing requirements concerning data deletions mandated in the GDPR or California’s equivalent, CCPA. Even when data is deleted for more mundane reasons, such as freeing up resources, the process can have a negative impact on critical operations if a system is not built with specific mechan ....
shutterstock Unlike Las Vegas, what happens at the edge doesn’t stay at the edge. And that s half the challenge. People commonly think of edge computing as a glorified form of data acquisition or a local digital process control. In reality, edge is a lot more than both of those. It’s true that edge involves many data sources, usually at geo-distributed locations. But keep in mind, it’s the aggregate of that data that holds the key to value and insights. Analysis of the combined data is carried out at core data centers, and actions guided by the resulting insights often need to be carried out at edge locations. Therefore, a surprising challenge of edge systems is efficient traffic not only ....