How to manage high data volume in telemetry?

Date:2023-01-07
1. Limit data collection: Collecting data is not an end in itself. Start by asking why a specific metric is important, and determine whether it is necessary to collect it in the first place. Ask yourself - does this data add value? Make sure that only the data points that are most relevant are being collected. 2. Identify common data patterns: Look for patterns and trends in the data that can be used to simplify data analysis. Instead of collecting individual data points for each event, try gathering just the averages and ranges. This will help reduce the overall size of your data set. 3. Automate data aggregation: Automate the process of aggregating data from multiple sources, reducing the amount of manual labor required. Use tools and scripts that can be programmed to collect, move, and store data in a centralized repository. 4. Streamline data storage: Move data storage and processing offsite or to the cloud, allowing you to access the data whenever you need it. This will free up local storage space, while allowing you to take advantage of the latest storage technologies. 5. Implement data retention policies: Develop and implement data retention policies to ensure only recent data is kept and that data that is no longer needed is removed. This helps to keep your data set size manageable and your analysis targeted. 6. Consider alternate data formats: Consider converting large data sets into formats optimized for machine learning, such as CSV or JSON. This data can then be easily analyzed and presented in more meaningful ways.
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