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Energy providers understand that customers tend to lose patience when there is not enough power for them to complete day-to-day activities. Task one is keeping the lights on. If you are transitioning to renewable energy, it helps to know when you need to produce energy so you can choose a suitable energy source. Real-time analytics refers to the process of collecting, analyzing, and interpreting data instantly as it is generated. This approach enables you to track and monitor activity, make the decisions based on real-time insights on data stored in a and keep those lights on. Grafana is a popular data visualization tool that enables you to create customizable dashboards and effectively monitor your systems and applications. Grafana real-time analytics This page shows you how to integrate Grafana with a and make insights based on visualization of data optimized for size and speed in the columnstore.

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Prerequisites

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Optimize time-series data in hypertables

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Optimize your data for real-time analytics

When converts a chunk to the columnstore, it automatically creates a different schema for your data. creates and uses custom indexes to incorporate the segmentby and orderby parameters when you write to and read from the columstore. To increase the speed of your analytical queries by a factor of 10 and reduce storage costs by up to 90%, convert data to the columnstore: Just to hit this one home, by converting cooling data to the columnstore, you have increased the speed of your analytical queries by a factor of 10, and reduced storage by up to 90%.

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Write fast analytical queries

Aggregation is a way of combining data to get insights from it. Average, sum, and count are all examples of simple aggregates. However, with large amounts of data aggregation slows things down, quickly. Continuous aggregates are a kind of hypertable that is refreshed automatically in the background as new data is added, or old data is modified. Changes to your dataset are tracked, and the hypertable behind the continuous aggregate is automatically updated in the background. By default, querying continuous aggregates provides you with real-time data. Pre-aggregated data from the materialized view is combined with recent data that hasn’t been aggregated yet. This gives you up-to-date results on every query. You create continuous aggregates on uncompressed data in high-performance storage. They continue to work on data in the columnstore and rarely accessed data in tiered storage. You can even create continuous aggregates on top of your continuous aggregates.

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Visualize energy consumption

A Grafana dashboard represents a view into the performance of a system, and each dashboard consists of one or more panels, which represent information about a specific metric related to that system. To visually monitor the volume of energy consumption over time: You have integrated Grafana with a and made insights based on visualization of your data.