#real-time

#real-time

In the fintech industry we can link latency directly to profit and money. If I have lower latency than the competition, I can get to the better deals, I can make the better deals.

"This is more likely to complement existing SIEMs than replace them. Early adoption will come from large enterprises already committed to Databricks, especially those seeking flexibility or cost control."

Apache Kafka has cemented itself as the de facto platform for event streaming, often referred to as the 'universal data substrate' due to its extensive ecosystem that enables connectivity and processing capabilities.

Weather impacts sales. Every retailer knows it. But for most, the likelihood that it might rain, snow, or sleet on the third of March somewhere in the Midwest is rarely used. Vendors such as Weather Trends have offered accurate, long-range forecasts for more than 20 years. But the opportunity is not predicting the weather; it's knowing what to do with the data. AI might change that.

Hyperscalers and major data platform vendors offer integrated services across storage, analytics, and model infrastructure. MariaDB's differentiation will likely depend on whether the combined platform can deliver operational speed and simplicity that organizations find easier to run than those larger stacks.

Kubernetes Horizontal Pod Autoscaler (HPA)'s delayed reactions might impact edge performance, while creating a custom autoscaler could achieve more stable scale-up and scale-down behavior based on domain-specific metrics and multiple signal evaluations. Startup time of pods should be included in the autoscaling logic because reacting only when CPU spiking occurs delays the increase in scale and reduces performance. Safe scale-down policies and a cooldown window are necessary to prevent replica oscillations, especially when high-frequency metric signals are being used.

What happens under the hood? How is the search engine able to take that simple query, look for images in the billions, trillions of images that are available online? How is it able to find this one or similar photos from all that? Usually, there is an embedding model that is doing this work behind the hood.

The company, which is based in San Francisco and has an office in Pune, India, is targeting up to $35 million this year as it builds a royalty-driven on-device AI business. That growth has buoyed the company, which now has post-money valuation of between $270 million and $300 million, up from around $100 million in its 2022 Series B, Kheterpal said.

In the early days, VAST Data's focus was primarily on storing enormous amounts of data. "Even before we talked about AI, data had to be stored somewhere," Pernsteiner notes. The company started out in the world of HPC (High Performance Computing). The choice of this sector was strategic: in that world, the scale and performance requirements are enormous. With this choice, VAST more or less forced itself to set the bar very high.