In part 1 of this series, we tried to outline what data retention is and why it is needed to overcome increasing requirements for various regulatory standards. As detailed, there are some clear guidelines for organizations to take what we called a “data retention approach for compliance”. In this follow up post, outline some specific technological and procedural challenges you might face as well as some practical guidelines and strategies to overcome them.

It’s that time of year again–the temperature is dropping, snow is falling, and a new year has arrived. On the bright side, the end of 2018 brings the beginning of 2019 and a whole new list of tech conferences to look forward to.

If you rely on Elasticsearch for centralized logging, you cannot afford to experience performance issues. Slow queries, or worse — cluster downtime, is not an option. Your Elasticsearch cluster needs to be optimized to deliver fast results.

Record keeping tasks such as data retention and disposal are an essential part of business management and regulatory compliance. At its core, data retention is about data control—meaning that an organization has taken steps to identify data throughout its organization, and then assess its importance, determine how long it will keep it, and then dispose of it.

What a year this has been for Logz.io! It’s been an event-packed year for both our users and our community, with a myriad of new capabilities and product features rolled out one after the other. We’ve done the best to update you on the major new additions and have also added a What’s New feature within the UI itself to make sure you don’t miss on the new goodies being introduced.

Jenkins is an extremely popular, open-source, continuous integration tool used for running tests, building code, and subsequently pushing to staging and then production. In a previous post, I outlined instructions for collecting, analyzing and visualizing Jenkins system logs.

As engineers, we are constantly bombarded with complex machine data. In order to better monitor and troubleshoot our environments, we must analyze and gain an accurate understanding of this data to best evaluate how our systems are performing and combat any issues that may occur. Yet, sifting through this data while managing the added intricacies of serverless architecture, microservices, containers, and other technologies make our jobs increasingly difficult to navigate.

Before I hop right in, it’s important to understand a bit about diabetes. Diabetes is what happens when your body cannot produce (type 1) or respond (type 2) to insulin effectively. The impact on the body is frequently quite severe — people who have difficulty controlling their blood sugar levels run the risk of losing feeling in their fingers and/or toes or even going into a coma if their blood sugar is either too high or too low.

A decade ago, an enterprise adopting DevOps was a rare event. In the last decade, some businesses dipped their toes in, others spent big money going all in. Now the question isn’t whether to make the culture shift, but how to be successful as it grows. It’s how to scale the adopted DevOps practices in the enterprise. This article is going to focus on highlighting common missteps and offer advice that can help you make decisions to handle the challenges ahead.

One of the biggest KPIs in the DevOps space is monitoring. There are so many tools to help any organization to complete their monitoring picture, but no tool does everything and most organizations use many tools to help complete their monitoring solution. Mashing tools together often creates a problem of its own — the tool sprawl problem.