Operations | Monitoring | ITSM | DevOps | Cloud

In this post, we will describe a simple way to share data from multiple InfluxDB 2.0 OSS instances with a central cloud account. This is something that community members have asked for when they have OSS running at different locations, but then they want to be able to visualize some of the data or even alert on the data in a central place. Please note that while the method presented here is simple and fast to set up, it has many limitations which may make it inappropriate for your product use case.

Big data analytics help organizations use data to explore both new and improvement opportunities. Whichever cloud data platform you choose, there are two data storage technologies you will want to understand. Data warehouses and data lakes are the two dominant data solutions commonly used for defining how an organization stores, queries, analyzes, and reports on big data. This post will define what a data warehouse and data lake are, how they work, and their differences.

Since the initial release of InfluxDB OSS 2.0 in November 2020, more than 10% of the community has successfully upgraded, and the pace of the upgrades continues at a steady rate. We have released a number of maintenance releases to address defects, expand platform coverage, and enhance the update experience based on feedback.

One of the things we needed to either adopt or build for InfluxDB IOx is a database catalog. If you haven’t heard us talk about it yet, InfluxDB IOx (pronounced eye-ox) is the new in-memory columnar database that uses object storage for persistence. We’re building it as the future core of InfluxDB. A database catalog usually contains the definitions of a database’s structure like schema and indexes.

This article will show how we kept cardinality under control with a few tweaks in the Telegraf configuration. If you’re not yet familiar with it, Telegraf is the native and open-source plugin-driver metrics collection agent of InfluxDB. As you may know, cardinality is the combination of measurements, tags, sets, fields, and values in a time-series database, and having high cardinality can be a challenge.

In this post we’ll explore the concepts of data lake, data hub and data lab. There are many opinions and interpretations of these concepts, and they are broadly comparable. In fact, many might say they’re synonymous and we’re just splitting hairs. But let’s look again carefully. We can discern some subtle trends in the way people are doing things, and find distinctions in these expressions.

It has been just over two years since we introduced the Elastic Common Schema (ECS), and what a journey it’s been. From categorization fields to request for comments to Threat Intelligence fields, ECS has evolved rapidly over the course of the last two years. In this blog post, I would like to reflect on the ECS journey so far, and look towards the future of ECS.

Today’s telecom engineers are expected to handle, manage, optimize, monitor and troubleshoot multi-technology and multi-vendor networks, in a competitive and unforgiving market with minimal time to resolution and high costs for errors. With the ongoing growth in operational complexities, effectively managing radio networks, current and legacy core networks, services, and transport and IT operations is becoming a radical challenge.