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Members of the Jewish community gathered on Sunday morning to show their solidarity and pray for friends, family and others more than 5,000 miles away in Israel. The distance did not diminish the conviction of the crowd of at least 200 people at Milton Votee Park in Teaneck. There was a palpable sense of reverence.
The service was organized by the Rabbinical Council of Bergen County. Songs and prayers, and at times clapping, reverberated in the park throughout the morning.
“It hits close to home for everyone here,” said Asher Rauzman, 18, of Teaneck. “I don’t know if anyone can say who started this, but right now with the rockets going back and forth, it is important to keep our thoughts together.”
Some things are just better together: Batman and Robin, apple pie and ice cream, AI and analytics. Did that last combination surprise you? It shouldn’t.
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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 mechanisms to deal
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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