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Understanding time-series data and why it matters
- Time-series data is collected at regular or unpredictable intervals and tracks multiple data points over a specified time frame, providing a dynamic view of changes, patterns, and trends.
- Time-series data enables predictive modeling and forecasting of future behaviors and values based on historical patterns and trends, and helps organizations predict spare part and resource demand based on sensor telemetry tracking wear rates of equipment components.
- Anomaly detection is another important use case for time-series data, enabling organizations to identify anomalies or abnormalities by comparing new data points against established baselines and ranges based on past sequences.
- Time-series data requires a dedicated store that can efficiently handle the high ingestion rates, automatic aggregation, and rapid querying of large datasets. Amazon Timestream offers two engine options to support your time-series needs.
- Time-series data has become vitally important for many machine learning (ML) applications and models, as it captures patterns, sequences, and relationships that provide vital context.
- Time-series data powers more accurate real-world algorithms and provides insights crucial for financial forecasting, demand planning, infrastructure monitoring, ML model efficiency, and predictive maintenance.
- Victor Servin, Senior Product Manager for the Amazon Timestream team at AWS, brings 18 years of experience leading product and engineering teams in the Telco industry and an additional 5 years of expertise in supporting startups with Product Led Growth strategies and scalable architectures.
- Servin's data-driven approach is suited to drive the adoption of analytical products like Timestream, and he helps customers to efficiently achieve their goals.
- If you are already using a time-series engine like InfluxDB, check out the Migration Guide to speed up your move to manage journey, or explore Timestream engines.
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