AWS Lambda is AWS’s solution for highly portable, serverless computing. With Lambda functions, you can deploy and run business logic code without managing the underlying servers. Today, AWS announced that Lambda customers can now provision up to 10 GB of ephemeral storage for each of their functions, making them well-suited for new, data-intensive workloads—including machine learning inference, large media file processing, financial analysis, and more.
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 an early stage company, one of our biggest priorities is hiring. In fact, we have two company goals this year and one of them is.
Is your Central Processing Unit (CPU) maxing out and slowing your PC to a crawl? There's a good chance you have experienced this issue if you run high-demand software for things like graphic design, video editing, or gaming. It can be frustrating enough without factoring in how hard it can be to trace down the source of the problem. In this article, we'll look at how to remedy high CPU usage on your machine or machines.
This blog post is a companion piece to our Advanced Automation in NinjaOne webinar, providing step-by-step instructions for setting up the Auto-Install Applications example explored during that webinar. The full webinar recording provides multiple examples, tips, and suggestions. This blog post and webinar recording should be consumed together, click here to jump ahead to an in-depth explanation of the auto-install example or you can view the entire recording below.
Searching and visualizing logs is next to impossible without log parsing, an underappreciated skill loggers need to read their data. Parsing structures your incoming (unstructured) logs so that there are clear fields and values that the user can search against during investigations, or when setting up dashboards. The most popular log parsing language is Grok. You can use Grok plugins to parse log data in all kinds of log management and analysis tools, including the ELK Stack and Logz.io.
