Operations | Monitoring | ITSM | DevOps | Cloud

Monitor all your Redshift clusters in Grafana with the new Amazon Redshift data source plugin

In collaboration with the AWS team, we have recently released the new Redshift data source plugin for Grafana. Amazon Redshift is the fastest and most widely used cloud data warehouse. It uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes by using AWS-designed hardware and machine learning.

MQTT Topic and Payload Parsing with Telegraf

Buckle up, this one isn’t short…but I’m hoping it will be thoroughly informative! This post is about Telegraf as a consumer of MQTT messages in the context of writing them to InfluxDB. If you are interested in and unfamiliar with Telegraf, you can view docs here. Unsure if Telegraf aligns with your needs? I make a case for it in the Optimizing Writes section of this blog post. It may also help to have an understanding of Line Protocol, InfluxDB’s default accepted format.

2021 Pepperdata Survey: The Reality of Kubernetes in Action

More companies than ever before are migrating to Kubernetes and seeing the results of Kubernetes in action. Kubernetes (K8s) is a key platform for big data users, and as such, we wanted to dive deeper and discover some new truths about current Kubernetes challenges and what the solutions might be. We surveyed 600 IT and big data professionals from various industries to determine which big data applications enterprises are moving or intending to move to Kubernetes.

Data Pipelines Overview

A Data Pipeline is a series of processes that collects raw data from various sources, filters the disqualified data, transforms them into the appropriate format, moves them to the places you want to store them, analyzes them, and finally presents them to your audience. As we can see in the chart above, a data pipeline is analogous to a water flow: data flows from one stage to another while being processed and reshaped.