Time-series Data Management

AI Ready:

By contextualizing data through real scenario-aligned data modelling, unifying data structure and standardizing data units & names, OpenDataTechs' time-series data management product empowers LLM (large language model) to analyze data, detect anomaly, recommend charts, generate dashboards, explain charts, offer insight, forecast trends and figure out root cause of accidents.

Cloud Native (on CNCF Landscape)

Elastic resource scaling, high availability, containerized deployment and pay-as-you-go pricing. Recommended by CNCF (Cloud Native Computing Foundation, https://landscape.cncf.io/)

High-concurrency Read/Write

10x faster on high-concurrency read/write compared to general database products and stably support over a 1 billion of data collection points.

Ultra-high Data Compression

Reduce storage costs by up to 90% with multi-tier architecture. Seamlessly integrate with S3 and other object storage to enable automated hot/cold data layering.

Full-stack Design

Buffer, topic subscription/consumption, message queues, streaming, edge-cloud sync, time-series forecast - enjoy these native capabilities in all-in-one architecture without the overhead of multi-system integration and maintenance.

Open Eco-system

Support standard SQL and integrate seamlessly through standard connectors like JDBC/ODBC with industry-leading stacks, such as Grafana, Prometheus, Power BI, Looker. Zero coding and Configuration-only setups.

Bridging the IT/OT divide by supporting mainstream industrial interfaces such as PI System, MQTT and OPC....

Screenshot of a sleek dashboard displaying AI-driven analytics insights.

Applicable Industries

Manufacturing
Energy （power plants, wind/photovoltaic/hydro... power）
IT infrastructure (data center)
Petrochemical
Transportation
Logistics
Public utilities （water, gas, heating...)
Financing
...

Common and Typical Industrial Pain Points

Large Data Volume

Producing massive volumes of time-series data every second can overwhelm traditional general databases.

High-concurrency Read/Write and Ultra-low Storage Costs

Creative product design ensures 10x faster high-concurrency read/write even supporting over a 1 billion of data collection points and up to 90% storage costs reduction.

Simplify Maintenance

Buffer, topic subscription/consumption, message queues, streaming, edge-cloud sync, time-series forecast - all-in-one architecture greatly reduce complexity of integration and maintenance.

Industrial Data Foundation

Integrate with multiple heterogeneity data sources and embed ETL tool to import, parse, map and transform data.

Bridging the IT/OT divide by supporting mainstream industrial interfaces such as PI System, MQTT and OPC....

AI Ready

By contextualizing data, LLM (large language model) can be empowered to analyze data, detect anomaly, recommend charts, generate dashboards, explain charts, offer insight, forecast trends and figure out root cause of accidents.

Real-time Monitoring and Alert

7*24 real time monitor status of parameters of huge numbers of equipment.

Immediate alert upon anomaly detection.

Complex Analysis and Prediction

Analyzing massive volumes of time-series data to extract insights and make predictions is time-consuming and labor-intensive.

High Maintenance Costs

Maintaining multiple systems simultaneously needs many resources.

Multi-source heterogeneity data integration

Data integration from different sources and silos.

High Data Storage Costs

Massive amounts of time-series data leads to high storage cost.

Illustration of interconnected data nodes symbolizing open data flow.

How OpenDataTechs' Product Create Value

Interested? contact us