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Learn how to visualize time series data from InfluxDB 3 Core using popular open source Apache Superset. This tutorial walks you through setting up both systems with Docker, writing sample IoT data, and creating your first visualization. For more information about Apache Superset, this article may be helpful.

As machines and systems grow more connected and complex, the traditional toolbox for managing them feels increasingly outdated. Engineers and operators need new approaches that match the realities of software-driven products and data-intensive environments. Digital twins provide that leap forward. By creating a virtual model of a physical asset and continuously feeding it with real-time data, digital twins reveal both current performance and likely future outcomes.

InfluxDB 3.6 is now available for both Core and Enterprise. This release introduces the 1.4 update to InfluxDB 3 Explorer, featuring the beta launch of Ask AI, along with new capabilities for simple startup and expanded functionality in the Processing Engine. InfluxDB 3 Core is free and open source, optimized for recent data, and licensed under MIT and Apache 2. InfluxDB 3 Enterprise extends Core with long-term data retention, clustering, fine-grained security, and management capabilities.

The Distinct Value Cache (DVC) available with InfluxDB 3 Core and InfluxDB 3 Enterprise lets you cache distinct values of specific columns and query those values in under 30ms. The DVC is an in-memory cache that stores distinct values of one or more columns in a table. It is typically used to cache distinct tag values, but you can also cache distinct field values.

The challenge of storing, processing, and alerting on your time series data is only part of the battle when it comes to deriving value from time-stamped data. While InfluxDB 3 addresses those hurdles with the database and Python processing engine, data analytics teams still need to be able to visualize their data and build dashboards to complete the time series story.

In 2025, the database ecosystem is more diverse and interconnected than ever before. From AI-assisted natural language queries that analyze your data to open table formats that make it easy to bridge systems, data infrastructure is moving towards openness, intelligence, and composability. Modern databases are no longer isolated systems; they are part of a broader ecosystem where interoperability is as important as performance.

Today, we’re announcing a major milestone for developers building the next generation of intelligent, real-time systems: InfluxDB 3 is available on Amazon Timestream for InfluxDB, now the default time series database offered directly in the AWS Management Console. This brings InfluxDB 3, our next-generation time series database, directly into the AWS ecosystem for the first time.

InfluxDB 3 Core and Enterprise bring real-time performance, unlimited cardinality, and low-cost storage to AWS developers running time series workloads at scale.

From pipelines to warehouses, modern operations generate more distributed data than ever, with equipment and connected devices spread across factories, grids, and remote sites. A single, centralized historian can no longer handle this volume or distribution. Without change, organizations risk fragmented visibility, higher costs, and slower responses.

Python developers often reach for Streamlit when they need to construct compelling web applications quickly. It provides a fast way to transform Python scripts into interactive applications without complex web frameworks. When paired with InfluxDB 3 Core, the leading time series database, engineers can build powerful real-time analytics dashboards entirely in Python.