Time series data visualization
Over the years, our R&D team has worked with different kinds of databases ranging from MS SQL to PostgresSQL to MongoDB. Recently we have flexed our data storage competency and added a new genre of databases, the time series ones. Time Series Databases are perfect for data visualization platforms as it is built specifically for handling events, measurements or metrics that are time-stamped and are tracked, monitored and aggregated over time for Grafana development.
We had mentioned about Grafana Sensor visualisation tool, a data cloud platform for our industrial monitoring solutions in one of our earlier blogs. We were on the scout for a good time-series database that could handle large time-series data for Grafana. Our search ended at a combination of Grafana + InfluxDB, an opensource time-series solution that provided the perfect ally to Grafana development and was pretty robust to setup and deploys. Influx does have a UI to add a database if we install the .deb package. InfluxDB provides both cloud and downloadable versions and is flexible enough for complex deployments; it certainly ticked most of our Grafana development team’s boxes.
Grafana development team
We also tried to migrate a few of our existing systems running in MongoDB to InfluxDB in industrial monitoring applications. Though there were some challenges few and far between, especially handling the multi-level management. But we guess that was sorted out soon and the transition was complete. One good thing about InfluxDB we noted was that it does have real-time access out of the gate and quickly identify the patterns and find value for your data. We could add more visualization instances in one of our dashboards, and while we earlier used the sleepy mongoose REST interface, InfluxDB programming allowed us the luxury of connecting it directly to our live graph chart.
InfluxDB programming for sensor data
InfluxDB has a powerful engine that can handle data coming through our large IoT deployments. It supports millions of writes per second and with the help of native clustering, all single points of failure are eliminated and users can get faster & accurate real-time values. It can accept data via HTTP, TCP or UDP protocols.
Grafana development and anomaly detection
Whether it is about measuring indoor air quality, factory monitoring or industrial elevator maintenance or wireless structural monitoring for predictive analytics, we have the right competencies in integrating tools to solve your business problem to ensure 24 x 7 operations.
Having trouble managing millions of data churned out by the sensors? Are you struggling to find a good visualization solution for your machine data? We at Ripples IOT might just have a perfect solution for you. For more info, Contact RipplesIOT