## Watch the webinar: Ingesting varied and distributed configuration metadata

[More details](https://quix.io/events/masterclass-on-ingesting-varied-and-distributed-configuration-metadata)

### Project templates

#### MATLAB runner reference architecture
Automate MATLAB simulation workflows with centralized data capture, version control, and real-time analysis for complex R&D systems development.

#### Configuration management pipeline reference architecture
A pre-configured pipeline architecture that enables real-time configuration updates for R&D systems without service interruptions.

#### Application level monitoring pipeline reference architecture
Real-time application monitoring pipeline for R&D teams. Monitor complex electro-mechanical systems with scalable metrics collection and alerts.

#### Streamline test rig data ingestion with unified pipeline architecture
Transform fragmented test data into unified streams. Multi-protocol ingestion, automated normalization, real-time visualization for R&D teams.

#### Data ingestion and configuration management
Dedicated R&D teams can now focus on their core goals without having to worry about sharing configurations or updating other teams manually.

#### Consolidate, map and normalize data from multiple sources
Ingest data from multiple sources into Iceberg tables on S3. Use Quix Streams to normalize and standardize the data before it reaches your data lakehouse.

#### Cut the Cost of Handling Industrial IoT Data in the Cloud
BiqQuery Data Engineers use Quix data pipelines to ingest large volumes of data from their operational estate on AWS. It's easier, cheaper and more flexible.

#### Reliably process late-arriving vehicle IoT data via MQTT
Ingest data from vehicle sensors via MQTT and use windowed aggregations with grace periods to ensure that delayed data is processed correctly.

#### Speed up operational insights on industrial machinery
Get faster data-driven insights on industrial machinery and manufacturing equipment so that you can optimize operational efficiency and reduce machine downtime.

#### Modernize legacy SCADA infrastructure with real-time data processing
Build real-time data processing pipelines for modern energy systems that integrate data from smart meters, IoT sensors, and legacy SCADA systems.

#### Predictive maintenance
Simulates data from a fleet of 3D printers and predicts which ones are going to fail before the print is finished using a time series forecasting algorithm.

### Use Cases
- Alerting
- Anomaly detection
- Data integration
- Energy sector
- ETL
- Heavy industry
- IoT
- Time Series Data