Our shared future has always been profoundly enigmatic. Hoary seers from days of yore would never have predicted everyday life as it is now. It would have been impossible to guess most of what has already happened in the 21st century. Peering into crystal balls would have proved equally futile. Even an attempt to make well-educated guesses about possible issues with big data would likely have been way off the mark.
Every government in the world is evaluating the steps necessary to radically reduce carbon emissions. The UK Government has been especially proactive, not just assessing these steps, but rolling out aggressive carbon-control strategies and legislation. Originally, the UK Government’s Climate Change Act 2008 set a goal of an 80 percent reduction in the country’s carbon emissions by 2050.
In today’s world of heterogeneous data ecosystems, managing and consuming data can be cumbersome. Organizations often have multiple systems of truth in corresponding to the applications managing the data. While data engineers dream of software that would make it easy to consume and digest different data streams from disparate systems, that scenario rarely comes to fruition.
The InfluxDB UI offers a wide variety of features for time series analysis, data lifecycle management, and time series visualization. The InfluxDB UI also shines when it comes to onboarding new users, whether they’re an InfluxDB OSS or free tier InfluxDB Cloud user. The InfluxDB UI allows you to easily leverage Telegraf, a plugin-driven collection agent for collecting, processing, and writing metrics and events.
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.
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.
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.
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.