First, we collect data from an existing Kafka stream into an Iguazio time series table. Next, we visualize the stream with a Grafana dashboard; and finally, we access the data in a Jupyter notebook using Python code. We use a Nuclio serverless function to “listen” to a Kafka stream and then ingest its events into our time series table. Iguazio gets you started with a template for Kafka to time series.
As an ISV company selling a SaaS application, you have built analytics into your software because you know customers highly value insights into the data that's held within your application. Giving your customers business intelligence (BI) and analytics within your application offers them a window of insight into the data to help them optimize their business. You deliver more value which boosts end user adoption and means your client buys for longer.
We’re proud to share that the Iguazio MLOps Platform has been named a leader and outperformer in the GigaOm Radar for Data Science Platforms: Pure-Play Specialist and Startup Vendors report. The GigaOm Radar reports take a forward-looking view of the market and are geared towards IT leaders tasked with evaluating solutions with an eye to the future. GigaOm analysts emphasize the value of innovation and differentiation over incumbent market position.
In this series of demystifying the tech trends, my colleagues and I will be looking at busting the buzzwords to help you keep on track. Concerned about puzzling parlance, analytics argot, techie terminology – or plain old jargon? This series breaks down words and concepts to give you the deepest insight and understanding into how to talk the talk in the world of tech, so you can engage in conversations with the confidence of being data literate.
When I was working at Google back in the mid 2000’s, we dealt with tens of billions of ad impressions a day, trained several machine learning models on years worth of historic data, and used frequently-updated models in ranking ads. The whole system was an amazing feat of engineering and there was no system out there that was even close to handling this much data. It took us years and hundreds of engineers to make this happen, today, the same scale can be achieved in any enterprise.
Better HR analytics bring benefits to every business, but data integration must come first.