In this digital era, technology systems are becoming increasingly complex. No longer can a single SME (subject matter expert) understand every facet of the system they run. Instead, much of this knowledge is siloed and exists as tribal knowledge within certain teams. Additionally, the rate of change is faster than ever, with code deploying and new services shipping at a rate unimaginable a few years ago.
Although parts of life have been put on pause due to the pandemic, our relationship with work hasn’t stalled away from the office. But working from home has profoundly changed the structure and flow of work. Employers and employees are collaborating to define what “workplace” means in 2022 and beyond.
We’ve all experienced a bit of FOMO at one time or another, whether we stayed home sick the night of a party or failed to score tickets to a big concert. It stings to miss out on the fun, but we get over it. In the era of remote work, however, ‘fear of missing out’ has taken on a more consequential meaning – one that is troubling the minds of many young professionals.
Few computing concepts are as ubiquitous as identity and access management. There isn’t a single day that goes by without us being asked for credentials, passwords or pin codes. Yet very few know the origins and the evolution of the technologies behind them. This is the first of two blog posts where we will look at the history of open-source identity management. We will cover the main open-source protocols and standards that shaped it, from its origins to the modern days.
The capacity to scale and process high data traffic by monitoring appliances is a critical requirement for organizations aiming to enhance or improve their security and protection from external threats. Excessive incoming traffic demands high-monitoring capabilities as it overwhelms the monitoring tools and places computational bounds that increase exponentially.
After years of helping developers monitor and debug their production systems, we couldn’t help but notice a pattern across many of them: they roughly know that metrics and traces should help them get the answers they need, but they are unfamiliar with how metrics and traces work, and how they fit into the bigger observability world. This post is an introduction to how we see observability in practice, and a loose roadmap for exploring observability concepts in the posts to come.
In 2017, McAfee found that an average enterprise uses 464 custom applications. A large enterprise — a company with over 50,000 employees — uses 788 custom apps! The more applications you have, the more complex your application environment is. This means that you are more susceptible to outages. So, the tolerance for downtime is impossibly low. Mission-critical applications must be available at all times.
At 8:54 pm on November 1, 2020, a customer of HDFC bank complained on Twitter that the bank’s services like internet banking and ATMs were down. More customers started raising similar issues over the next couple of hours, saying that UPI, credit card, and debit card transactions weren’t working either. Finally, at 11:55 pm, the bank confirmed that one of their data centers faced an outage. “Restoration shouldn’t take long,” they promised.
