In this webinar hosted by InfluxDB and HiveMQ, we focus on how you can create value for your business using new tools in the AI and database ecosystem to quickly deploy AI models to perform tasks like anomaly detection. The webinar starts with a high-level overview of how MQTT and time series data can be valuable in an industrial IoT environment.

The big data landscape is always changing to solve existing problems and continues to push the boundaries of performance and scale. Data lakehouses are a new architectural pattern that is rapidly gaining popularity by solving a variety of problems seen with previous solutions like data warehouses and data lakes. In this article, you will learn the following.

Various economic pressures on businesses have created a focus on new and innovative ways to manage operational costs. At the same time, businesses are looking at using IT to help manage overall business costs and increase income—for example, by supporting remote working, and in many cases, enabling e-commerce to replace closed retail outlets. Management of infrastructure to minimize downtime has two major benefits: reductions in support and maintenance costs and improvements in service levels.

Grafana is an open-source web application for visualizing data. You can query your data, create visuals, and receive alerts to better understand what you have. Some people think of Grafana as a Kubernetes-only tool, but in reality, it’s simply a data visualization tool that became popular within the Kubernetes ecosystem, especially when combined with Prometheus. In this post, I’ll focus on a very specific part of Grafana: the dashboards.

In the era of big data, choosing the right data storage solution is crucial for organizations to harness the power of their data. Understanding the differences and benefits of data lakes and data warehouses can help businesses make informed decisions on which option best suits their needs. In this blog post, we will explore data lakes and data warehouses, their architecture, and their key features, enabling you to make the right choice for your organization.

Any existing InfluxDB user will notice that InfluxDB underwent a transformation with the release of InfluxDB 3.0. InfluxDB v3 provides 45x better write throughput and has 5-25x faster queries compared to previous versions of InfluxDB.

For most of the past year, we here at InfluxData focused on shipping the latest version of InfluxDB. To date, we launched three commercial products (InfluxDB Cloud Serverless, InfluxDB Cloud Dedicated, and InfluxDB Clustered), with more open source options on the way. All the while, we claimed that this latest version of InfluxDB surpasses anything we built before.

With over 500,000 residential, business, and wholesale customers across multiple markets in the United States, WideOpenWest (WOW!) is one of the United States’ largest broadband providers. They aim to connect homes and businesses to the world with fast and reliable internet, TV, and phone services.

Industrial IoT (IIoT) machines and sensors generate valuable time series data. It’s impossible to derive the insights necessary to inform decisions as a company to produce or operate more efficiently without sending operational technology (OT) data to informational technology (IT) systems.

InfluxDB v3 allows users to write data at a rate of 4.3 million points per second. However, an incredibly fast ingest rate like this is meaningless without the ability to query that data. Apache DataFusion is an “extensible query execution framework, written in Rust, that uses Apache Arrow as its in-memory format.” It enables 5–25x faster query responses across a broad range of query types compared to previous versions of InfluxDB that didn’t use the Apache ecosystem.