Vela is in a tech and sales partnership with storage solutions vendor XenData.
XenData specialize in active archive systems based on LTO data tape, optical disc cartridges and hybrid cloud. Vela is a solutions provider based in Florida whose customers include more than half of the major station groups, 600 TV stations, all the major cable MSOs and satellite providers in the US.
“XenData has a flawless reputation for their product, its stability and their outstanding technical support, and the partnership between Vela and XenData will make the content monitoring, comparison, analytics, capture, recording, comparison and retrieval workflows seamlessly efficient, reliable and cost effective across multiple types of media”, said Kevin Grubbs, VP & CTO, Vela.
“Both XenData’s and Vela’s customers are a ‘Who’s Who’ of the Media & Entertainment industry, in addition to a number of leading government organizations and large global enterprises”, said Phil Storey, XenData’s CEO. “They have demanding use cases and requirements that necessitate their content creation, monitoring, verification, repurposing, streaming and monetization processes to be well integrated, efficient and highly reliable. Vela’s feature-rich and reliable solutions, and XenData’s emphasis on product superiority and support make this partnership a terrific match.”
You might also like...
Information theory can also be applied to loudspeakers, which are among the most difficult of transducers to design. Measuring the information capacity of loudspeakers is a useful tool.
In the previous article in this series, we looked at layer-2 switching and layer-3 routing. In this article, we look at Software Defined Networks and why they are so appealing to broadcasters.
It was late in 2018 when a major public broadcaster in the UK came to London-based 7FiveFive, a technology solutions provider, with a growth challenge. Their postproduction department had about 75 edit positions throughout the building working off a shared storage SAN…
Here we look at some practical results of transform theory that show up in a large number of audio and visual applications.
Machine learning is often compared to the human brain. But what do they really have in common?