Time series data streams are often noisy and irregular. But it doesn’t matter if the cause of the irregularity is a network error, jittery sensor, or power outage – advanced analytical tools, machine learning, and artificial intelligence models require their data inputs to include data sets with fixed time intervals. This makes the process of filling in all missing rows and values a necessary part of the data cleaning and basic analysis process.
We’ve all read the headlines about spectacular data breaches and other security incidents, and the impact that they have had on the victim organisations. From LastPass to SolarWinds, “data security” seems to be the phrase on the lips of every CTO these days. And in some ways there’s no place more vulnerable to attack than a big data environment like a data lake.
In today’s world, digital transformation has become crucial for all types and sizes of organizations. It is no longer just a buzzword but a necessary step towards becoming impressive, customer-centric, and productive. This transition involves using technology to change the way people work, leading to improved customer and user experience, streamlined operations, and better business models for optimal outcomes.
This article was originally published in The New Stack and is reposted here with permission. Selecting the tools that best fit your IoT data and workloads at the outset will make your job easier and faster in the long run. Today, Internet of Things (IoT) data or sensor data is all around us. Industry analysts project the number of connected devices worldwide to be a total of 30.9 billion units by 2025, up from 12.7 billion units in 2021.
