“Lead time to deploy” means the interval from when the code gets written to when it’s been deployed to production. It has also been described as “how long it takes you to run CI/CD.” How important is it? It’s nigh-on impossible to have a high-performing team if you have a long lead time, and shortening your lead time makes your team perform better, both directly and indirectly.
If you’re using Solr, for sure there are times when you change the schema and need to reindex. Quite often the source of truth is a database, so you can use streaming expressions via the JDBC source to reindex. But sometimes that’s not possible or adds too much load to the DB. So how can we use Solr itself as a source?
We all know that cloud and SaaS adoption continues to grow rapidly, often outpacing budgets. In fact, spending on IaaS and SaaS exceeded budgets in more than 40% of organizations in 2021. As a result, network traffic is now spending much more time on the internet than in our own data centers. The internet has become the new enterprise network.
As more companies adopt SaaS services over on-premise delivery models, there is a natural concern around data security and platform availability. Words on a vendor’s website can provide insights to prospective customers on the process and policies that companies have in place to alleviate these concerns. However, the old adage of “actions speak louder than words” does apply. Trust in a website’s words only goes so far.
After a mortgage payment, energy costs are typically the largest household expense. In my case it was an easy decision to install solar panels, but I wanted to perform in-depth analyses with historical data. Deploying monitoring sensors was straightforward; collecting and processing the raw data became the main challenge. Telegraf and InfluxDB are ideal choices for managing time series data. Although I had no prior experience, a Docker instance of Telegraf was onboarded in no time.
Digital transformation has been foundational to any forward-looking business and organizational strategy for many years, but never more so than today. In the digital era, centering an innovative approach to technology at the heart of your business is essential to credibility, impact, growth and efficiency. However, ensuring readiness to adapt to future market, customer or employee needs demands continuous effort and re-appraisal.
AIOps (artificial intelligence in IT operations) in monitoring refers to the convergence of artificial intelligence, machine learning, and data analytics to make IT monitoring a responsive, intelligent, and agile business function. AIOps is not an alternative to DevOps but a great partner to it that provides intelligent insights when integrated with every stage of the cycle.
Infrastructure monitoring is used to collect health and performance data from servers, virtual machines, containers, databases, and other backend components in a tech stack.
Kafka is an open-source program for storing, reading, and analyzing streaming data. It is open-source, which means it’s free-to-use amongst a big community of users and developers contributing to new features, upgrades, and support on a regular basis. Kafka can run on multiple servers as a distributed system, allowing it to take advantage of each server’s processing power and storage capacity.
The Exoprise marketing team sat down with their CEO, Jason Lieblich, to discuss predictive analytics, market trends, and the technology’s usage in troubleshooting network issues.
