Time-Series Workloads: Amazon Timestream, DynamoDB, and OpenSearch
A company is moving a large-scale time-series application from Cassandra to a managed AWS service. The workload involves high-frequency writes (millions per second) and queries that aggregate data over time windows. Which THREE AWS services are suitable for this time-series workload?
Quick Answer
The correct answer is Amazon Timestream, Amazon DynamoDB, and Amazon OpenSearch Service. Amazon Timestream is purpose-built for time-series workloads, offering automatic write optimization and built-in time-based aggregation functions that handle high-frequency writes at millions per second without manual tuning. DynamoDB supports this pattern through careful time-series partitioning—using the entity ID as the partition key and timestamp as the sort key—which distributes writes evenly and enables efficient range queries over time windows. OpenSearch Service ingests streaming time-series data and provides powerful aggregation queries via its date histogram and pipeline aggregations. On the AWS Certified Database Specialty DBS-C01 exam, this question tests your ability to distinguish purpose-built services from general-purpose databases; a common trap is choosing Amazon Redshift or Aurora, which lack the write throughput and time-series optimizations required here. Memory tip: think “TDO” for Timestream, DynamoDB, OpenSearch—the three services that together handle time-series ingestion, storage, and analytics.
⚠ Common exam trap
It's easy for candidates to choose Amazon Redshift for time-series analytics due to its columnar storage, overlooking its high write latency and lack of support for real-time, high-frequency ingestion at millions of writes per second.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Amazon OpenSearch Service
Amazon OpenSearch Service is suitable because it supports high-frequency writes via bulk indexing and provides powerful aggregation queries (e.g., date histograms, percentiles) over time windows, making it ideal for time-series analytics. It can ingest millions of events per second when properly scaled with optimized shard strategies and using the OpenSearch ingest pipeline.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Amazon OpenSearch Service
Why this is correct
Supports time-series ingestion and aggregation.
- ✗
Amazon Aurora
Why it's wrong here
Relational database with write limitations.
- ✓
Amazon DynamoDB
Why this is correct
Can handle high write throughput with proper time-series pattern.
- ✓
Amazon Timestream
Why this is correct
Designed for time-series data with built-in aggregation.
- ✗
Amazon Redshift
Why it's wrong here
Not optimized for high-frequency writes.
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Same concept, more angles
1 more way this is tested on DBS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company runs a time-series application that records sensor data every second. The data volume is 500 GB per month and grows continuously. They need to query the last 30 days of data frequently and older data rarely. Which database design is MOST appropriate?
easy- ✓ A.Amazon Timestream
- B.Amazon RDS for PostgreSQL with partitioning
- C.Amazon DynamoDB with TTL
- D.Amazon S3 with Athena and partitioning
Why A: Amazon Timestream is purpose-built for time-series data, automatically storing recent data in memory for fast queries and moving older data to a cost-optimized store. This matches the workload of frequent queries on the last 30 days and rare queries on older data, with continuous growth at 500 GB/month.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DBS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DBS-C01 exam.