Summary and Quiz
Understand how to manage and analyze time-series data using Amazon Timestream. Learn to handle common workloads, configure retention and rollups, optimize for late-arriving data, and apply advanced features like scheduled queries and tiered storage to ensure efficient, cost-effective operations in production environments.
We'll cover the following...
Common time series workloads
Three dominant workload categories recur across AWS architectures: observability and DevOps metrics demanding sub-second ingestion, IoT and sensor telemetry where dimension cardinality can explode into millions, and industrial operational telemetry requiring queries spanning months. All share high-velocity appends, time-window queries, and strong recency bias, making purpose-built time-series services the right fit over general-purpose OLTP databases.
Retention, rollups, and downsampling
Retention planning is a day-one architectural decision. In Timestream for LiveAnalytics, the memory store (hot tier) and magnetic store (cold tier) each carry independently configurable retention windows, and data transitions automatically between them. Rollups are precomputed, coarser-grained aggregates that replace high-resolution raw data for historical queries, reducing query latency and bytes-scanned cost. Scheduled queries automate this aggregation within the service. Deleting raw data before rollups complete leaves permanent gaps.
Late-arriving data and cardinality
Late-arr ...