Sometimes "getting data in” (or GDI) can be just as complicated as managing the data itself. From the early days of Splunk, getting data in has been a critical aspect of delivering the right data for visibility, investigation, and action. As data locality proliferates, formats evolve, and volumes grow, customers need easy access to their data, in the right place, in the right shape, and at the right time.
Mayank A, senior principal inbound product manager for Customer Workflows at ServiceNow, co-authored this blog. The way customers want to engage with organizations is radically changing. The pandemic and multiple shutdowns brought these changes to the forefront, forcing organizations to rethink their customer experience, both in person and online, and accelerate digital transformation projects to try to better align the customer journey with expectations.
In one of our previous articles, we discussed what an SRE is, what they do, and some of the common responsibilities that a typical SRE may have, like supporting operations, dealing with trouble tickets and incident response, and general system monitoring and observability. In this article, we will take a deeper dive into the various SRE principles and guidelines that a site reliability engineer practices in their role.
Tucker Callaway is the CEO of LogDNA. He has more than 20 years of experience in enterprise software with an emphasis on developer and DevOps tools. Tucker fosters a DevOps culture at LogDNA by tying technical projects to business outcomes, practicing extreme transparency, and empowering every person in the company to contribute.
Tracing has become essential for monitoring today’s increasingly distributed architectures. But complex production applications produce an extremely high volume of traces, which are prohibitively expensive to store and nearly impossible to sift through in time-sensitive situations. Most traditional tracing solutions address these operational challenges by making sampling decisions before a request even begins its path through your system (i.e., head-based sampling).
With the increasing adoption of artificial intelligence (AI) applications at the workplace, the debate about the future of work, workers, and the workplace has intensified. The polarised nature of debate ranges from job losses versus new-technology job creation through performance efficiency versus performance effectiveness to liberating humans from drudgery versus being controlled by machines. While several other polarities are evident in this debate, the truth always lies somewhere in between.
