I’m sure many of you have heard of our Machine Learning Toolkit (MLTK) app and may even have played around with it. Some of you might actually have production workloads that rely on MLTK without being aware of it, such as predictive analytics in Splunk IT Service Intelligence (ITSI) or MLTK searches in Splunk Enterprise Security.
When Rolls Royce Power Systems recently needed to improve its operational efficiency within its manufacturing plants, it didn’t expand its use of a legacy process historian or purchase historian connectors to export data to their business intelligence systems. Instead, it decided to go with a modern time series database, InfluxDB. Graphite Energy, another customer we featured in our recent IIoT announcement, also chose InfluxDB over the legacy process historian vendors. Why?
The key challenge with modern visibility on clouds like AWS is that data originates from various sources across every layer of the application stack, is varied in format, frequency, and importance and all of it needs to be monitored in real-time by the appropriate roles in an organization. An AWS centralized logging solution, therefore, becomes essential when scaling a product and organization.
The question of how to get data into a database is one of the most fundamental aspects of data processing that developers face. Data collection can be challenging enough when you’re dealing with local devices. Adding data from edge devices presents a whole new set of challenges. Yet the exponential increase in IoT edge devices means that companies need proven and reliable ways to collect data from them.
As we push more data to the cloud, avoidable mistakes are hampering migration. The biggest culprit: messy data with inadequate security and integration.Data transfers seem to be the easiest part of cloud migration. After all, migrating applications is the biggest pain in the neck. Data replication and migration should be simple, something that’s done during the last step of the application and data migration process. Right?
