Operations | Monitoring | ITSM | DevOps | Cloud

In a previous post, we described the technique that makes the ”most recent values” queries hundreds of times faster and has benefited many of our customers. The idea behind this technique is to progressively evaluate time-organized files until we reach the most recent values.

We have added a new function, tz, to InfluxDB 3.0 SQL to improve the ergonomics of working with timezones. This blog post will provide more details on how to use this function and when it makes sense to use it instead of the existing SQL timezone functions.

In the first blog in this series, Setting Up InfluxDB and Visualizing Data: Part 1, we built a data collection and visualization platform for time series data using InfluxDB Cloud Serverless. Inspired by the CSTR with PID controllers use case, the project showcased how to ingest real-time data and visualize it using InfluxDB and Grafana. This follow-up post focuses on InfluxDB’s 3.0 architecture, giving an in-depth look at the platform’s inner workings.

We've written several times about our data stack here incident, but never about our underlying data warehouse and the design principles behind it. This blog post will run through the high-level structure of our data warehouse and then will go in-depth into the underlying layers.

We are excited to announce the launch of User Groups, a major update that facilitates enhanced security through access control in InfluxDB Cloud Dedicated. This new feature allows for more granular access management by limiting limited access accounts. Giving customers more access control helps them implement PoLP (“Principle of Least Privilege”) for improved security.

SaaS has transformed from just a convenient cloud solution into the leading driver of the rapidly expanding cloud computing sector. With its market value projected to reach $716.52 billion by 2028, nearly all organizations globally will use SaaS in some capacity. Yet, SaaS without reporting is like running a business without a budget — you’re operating but have no idea where your resources are going.

It’s hard not to speak in cliches when we talk about artificial intelligence (AI). Today, AI seems to be all around us. And whatever its cultural impact, its rapid evolution is leading to widespread adoption across industries. Much of the discourse focuses on what machine intelligence can do to enrich our lives and businesses. But less has been said about data, and how every AI system relies on it to operate.

Optimizing data-intensive workloads typically takes months of planning and significant human effort to put cost-saving tools and processes in place. Every passing day increases the risk of additional expenditures—outlays that cost the business money and time, and that cause delays to new revenue-generating GenAI or AgenticAI projects. Remove the risk from optimization with Pepperdata Capacity Optimizer’s 100% ROI Guarantee.

Snowflake and Databricks are both robust data platforms. Both are built for speed, scale, and cloud-native applications. They also prioritize efficiency and adherence to strict data protection protocols. Yet each adopts a distinct approach and features suited to its architecture and various use cases. It is these differences that can make all the difference in the way your data is processed, analyzed, reported, and more.