In my previous post, we explored why Honeycomb is implemented as a distributed column store. Just as interesting to consider, though, is why Honeycomb is not implemented in other ways. So in this post, we’re going to dive into the topic of time series databases (TSDBs) and why Honeycomb couldn’t be limited to a TSDB implementation. If you’ve used a traditional metrics dashboard, you’ve used a time series database.
Relational databases are by far the most common type of database, and as software developers it’s safe to say that they are the kind of database most of us got started on, and probably still use on a regular basis. And one thing that they all have in common is the way they structure data. InfluxDB, however, structures data a little bit differently.
At ElasticON Global 2021, we shared a future view of Elastic Enterprise Search and how we’re continuing to build next-generation, machine learning-powered search experiences backed by the speed, scale, and relevance of Elasticsearch. We also highlighted the many ways we plan to keep building even more flexibility into our solutions.
In the first four parts of our series on correlation analysis, we discussed the importance of this capability in root cause analysis in a number of business use cases, and then specifically in the context of promotional marketing, telco and algorithmic trading. In this blog we walk through how to leverage correlation analysis to address the challenges in ensuring a seamless online payment experience by the end-user.
When I started as a data engineer almost 20 years ago, I designed, developed, and implemented a worldwide sales reporting system for my employer using an enterprise data warehouse. Using analytical packages, my team drove quantifiable sales by transforming the way our company leveraged data. Even at the start of the millennium, it seemed obvious that studying analytics was a game-changer.
CIOs can finally claim victory in information hide-and-seek with help of AI-powered search technology Why do companies still struggle to respond to a service complaint, recommend a product, or connect employees with the data they need to make critical decisions? These tasks are critical to retaining customers and engaging workers in a competitive marketplace, yet each relies on finding the right information, and that’s no easy feat.
We all want fast access to the information we need, when we need it. With a few simple clicks, we expect our questions to be answered and our problems to be solved. We want to be empowered to solve problems on our own, intuitively, and without friction. We expect seamless, quick, and easy self-service. According to a Gartner® report, 85% of customer service interactions will start with self-service by 2022, up from 48% in 2019.1.
