Gaining visibility into modern IT environments is a challenge that an increasing number of organizations are finding difficult to overcome. Yes–the advent of cloud computing and virtually “unlimited” storage has made it much easier to solve some of the traditional challenges involved in gaining visibility. However, architecture has evolved into microservices, containers and scheduling infrastructure.
API access is available at the Enterprise tier of our product. With it, you can create a whole range of heavily customized use cases to further expand our suite of offerings. For example, you can hit our API to send customized query results to a third party service like Nagios, or you can automate the creation and deletion of sub-accounts.
In many product development workflows, there are three main concerns: building, testing, and deployment. In this scenario, every change that is made to the code means something could accidentally go wrong, so to lessen the likelihood of this happening, developers assume many strategies to reduce incidents and bugs. One strategy is to adopt continuous integration tools (CI): used together with a source version software to verify if something has gone wrong for every update.
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.
