Amazon DynamoDB + OpenSearch Service | Overview.

Amazon DynamoDB + OpenSearch Service - Overview.

Scope:

• Intro,
• Why Pair DynamoDB and OpenSearch,
• Integration Patterns,
• Integration Flow diagram & explanation,
• Architecture Reference,
• Use Cases,
• Advanced Patterns,
• Final thoughts.

Intro:

• Amazon DynamoDB and OpenSearch Service are commonly used together on AWS, leveraging a managed zero-ETL integration.
•  This zero-ETL integration provides a powerful search and analytics capabilities over data stored in DynamoDB.
• OpenSearch patterns with DynamoDB is a common integration, since DynamoDB excels at low-latency transactional workloads, while OpenSearch is optimized for rich querying, full-text search, and analytics.
• Together, they enable fast operational queries + flexible search/analytics.

1. Why Pair DynamoDB and OpenSearch

• DynamoDB strengths: single-digit millisecond performance, serverless, horizontal scale, predictable cost.
• Gaps: limited query patterns (key-value, range queries, GSIs, sparse indexes).
• OpenSearch strengths: full-text search, fuzzy matching, aggregations, anomaly detection, k-NN vector search.
• Pattern: Use DynamoDB as the system of record and OpenSearch as a search/analytics engine.

2. Integration Patterns

a) Streaming Ingestion (Near Real-Time Sync)

• Use DynamoDB Streams → AWS Lambda → Amazon OpenSearch Service.
• Data flow:
1. DynamoDB item change captured by Streams.
2. Lambda transforms document (JSON flattening, enrichment).
3. Lambda indexes document into OpenSearch.
• ✅ Pros: Near real-time sync, simple event-driven architecture.
• ⚠️ Cons: Lambda scaling considerations, error handling required.

b) Batch ETL (Periodic Sync)

• Use AWS Glue, Data Pipeline, or custom jobs to export DynamoDB data to S3, then bulk load into OpenSearch.
• ✅ Pros: Suitable for large datasets, cost-efficient for infrequent updates.
• ⚠️ Cons: Not real-time, data freshness delay.

c) Dual Write (App Level)

• Application writes to both DynamoDB and OpenSearch in the same transaction flow.
• ✅ Pros: Low latency, no need for stream-based pipelines.
• ⚠️ Cons: Higher complexity (retry logic, consistency handling).

d) Hybrid Pattern

• DynamoDB as OLTP system.
• OpenSearch as secondary index/search engine for specific workloads.
• Example: DynamoDB stores user profiles, OpenSearch provides full-text search across names, skills, locations.

Integration Flow diagram & explanation: 

         

Flow explanation:

1.     DynamoDB holds operational data.

2.     DynamoDB Streams capture item changes.

3.     Lambda or Kinesis processes, enriches, and batches data.

4.     OpenSearch Service indexes documents for advanced search and analytics.

5.     OpenSearch Dashboards provides visualization and insights.

3. Architecture Reference

Ingestion Layer:

• DynamoDB Streams → Lambda (or Kinesis Data Streams / Firehose) → OpenSearch bulk index API.

Storage:

• DynamoDB = Source of truth.
• OpenSearch = Hot, UltraWarm, Cold tiers depending on query frequency.

Query:

• Application queries:
• DynamoDB for exact lookups.
• OpenSearch for full-text search, aggregations, k-NN search(K-Nearest Neighbours...non-parametric, supervised machine learning algorithm used for both classification and regression). 
• Results combined in app layer (if needed).

4. Use Cases

• E-commerce: DynamoDB stores product catalog, OpenSearch powers search by keyword, category, price ranges, recommendations.
• Log/Event Analytics: DynamoDB as ingestion buffer, OpenSearch for queryable logs.
• User Profiles & Social Apps: DynamoDB stores canonical profile, OpenSearch provides fuzzy name search, skill matching.
• IoT & Time-Series: DynamoDB for fast writes, OpenSearch for analytical queries & dashboards.

5. Best Practices

• Use bulk indexing in OpenSearch (not single doc writes).
• Implement dead-letter queues (DLQ) in Lambda/Kinesis for failed events.
• Design retry logic & idempotency to avoid duplicate writes.
• Keep OpenSearch indices lifecycle-managed (rollover → UltraWarm → Cold → delete).
• Avoid over-sharding OpenSearch (aim 10–50 GB/shard).
• Monitor with CloudWatch + OpenSearch Dashboards.

6. Advanced Patterns

• Search + Update:
• Store IDs in OpenSearch, fetch details from DynamoDB → ensures consistency.
• Enrichment:
• Lambda can enrich data before pushing to OpenSearch (geo-hash, sentiment scores, NLP tokens).
• AI + Vector Search:
• DynamoDB stores metadata, OpenSearch k-NN stores embeddings for semantic/AI-powered search.

Final thoughts:

• DynamoDB + OpenSearch is a polyglot persistence pattern: DynamoDB handles fast writes & key-based lookups,
• OpenSearch provides flexible query/search/analytics. 
• Together, they unlock modern search-driven and analytics-heavy applications at scale.