InfluxData Unveils Future of Time Series Analytics with InfluxDB 3.0 Product Suite

• Open source project InfluxDB IOx becomes InfluxDB 3.0, the next-generation time series database powering all InfluxDB products

• InfluxDB Cloud Dedicated added to suite of enterprise-grade cloud services

InfluxData, creator of the leading time series platform InfluxDB, announced expanded time series capabilities across its product portfolio with the release of InfluxDB 3.0, its rebuilt database and storage engine for time series analytics. InfluxDB 3.0 is available today in InfluxData’s cloud products, including InfluxDB Cloud Dedicated, a new product for developers that delivers the performance, power, and flexibility of InfluxDB with the security of a fully managed service. InfluxData also announced InfluxDB 3.0 Clustered and InfluxDB 3.0 Edge to give developers next-gen time series capabilities in a self-managed database.

Developed as the open source project InfluxDB IOx, InfluxDB 3.0 now serves as the foundation for all InfluxDB products, both current and future, delivering significant gains in performance, high-volume ingestion and compression, real-time querying, and unlimited scale to the database. Built in Rust, a modern programming language designed for performance, safety, and memory management, InfluxDB 3.0 gives developers unprecedented scale and performance, supporting the full range of time series data, including metrics, events, and traces. Together, these enhancements raise the bar for time series analytics to power new use cases that rely on high-cardinality time series data for observability, real-time analytics, and IoT/IIoT.

“The ArchFX Platform enables manufacturers to collect complex data, including high density and high fidelity time series data, from both modern and legacy factory machines”

“InfluxDB 3.0 is a major milestone for InfluxData, developed with cutting-edge technologies focused on scale and performance to deliver the future of time series,” said Evan Kaplan, CEO, InfluxData. “Built on Apache Arrow, the most important ecosystem in data management, InfluxDB 3.0 delivers on our vision to analyze metric, event, and trace data in a single datastore with unlimited cardinality. InfluxDB 3.0 stands as a massive leap forward for both time series and real-time analytics, providing unparalleled speed and infinite scalability to large data sets for the first time.”

“Organizations increasingly rely on real-time data analytics, which drives demand for tools capable of handling time series data,” said Rachel Stephens, Senior Analyst with RedMonk. “Marketwide, it is increasingly important to consider the developer experience of working with the database. With InfluxDB 3.0, InfluxData is addressing this with its columnar engine designed to remove limits on cardinality, SQL support, and integration into the Apache Arrow ecosystem. These updates widen the practical use cases and ecosystem for time series workloads.”

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Rebuilt as a columnar database, InfluxDB 3.0 leverages the scale and performance of the Apache Arrow data structure to deliver real-time query response on leading-edge data. Customers can upgrade to InfluxDB 3.0 from 1.x and 2.x and run existing workloads faster and at a lower cost with minimal key changes, experiencing the following key benefits:

• Real-time query response: Columnar datastore built for scale and performance delivers 100x faster queries against high cardinality data for faster insights.
• Unlimited cardinality and high throughput: 10x ingest performance to continuously ingest, transform, and analyze billions of time series data points per second without limitations or caps.
• Low-cost object store: High compression reduces data storage costs to enable 10x more storage without sacrificing performance.
• SQL language support: Analyze data using SQL, the most popular data programming language, or unlock deeper time series analysis with InfluxQL’s time-based functions.

InfluxDB 3.0 is available today in InfluxData’s cloud products:

• InfluxDB Cloud Serverless: Currently available as an elastic, serverless, fully-managed time series database as a service running in the cloud.
• InfluxDB Cloud Dedicated: Available today as a fully managed time series database as a service running on cloud infrastructure resources dedicated to a single tenant, optimized for large-scale workloads.

InfluxDB 3.0 will be available in the following products later this year:

• InfluxDB 3.0 Clustered: A self-managed time series database cluster delivered as software for on-premises or private cloud deployment.
• InfluxDB 3.0 Edge: A self-managed time series database single node delivered as software for local or edge deployment.

InfluxData also announced the general availability of InfluxDB Cloud Dedicated, its latest cloud product based on InfluxDB 3.0 intended for large-scale time series workloads. With this launch, InfluxData expands the capabilities of its InfluxDB Cloud service, making it available on cloud infrastructure that is dedicated to a particular customer or workload. Now developers and enterprises alike have multiple options to run large-scale time series workloads in the cloud using InfluxDB, with multiple options for cloud providers, delivery methods, and pricing models.

“Skylab provides AI-driven photo and video editing services, a process that involves high volume cardinality data,” said Alireza Shafaei, CTO, Skylab Technologies. “InfluxDB was already a critical part of our infrastructure by analyzing large volumes of time series data in real-time. The introduction of InfluxDB 3.0 enables us to scale our processes even further by allowing us to ingest and act on all of our high-resolution and high-cardinality data without downsampling or dropping dimensions. ”

“The ArchFX Platform enables manufacturers to collect complex data, including high density and high fidelity time series data, from both modern and legacy factory machines,” said Tim Burke, co-founder and CTO, Arch Systems. “Unbounded cardinality and SQL support in InfluxDB 3.0 will allow our full team to analyze manufacturing data on any dimension and uncover actionable insights at an impressive scale.”

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