Question: What are the key differences between DynamoDB and Elasticsearch?

Answer

DynamoDB is a managed operational key-value and document database; Elasticsearch is a search and analytics engine. DynamoDB is usually queried by keys, while Elasticsearch indexes document fields for searches, relevance scoring and aggregations.

Data model and requests

A DynamoDB write identifies an item through its table key. This boto3 example assumes a String partition key named pk:

import boto3

table = boto3.resource("dynamodb", region_name="us-east-1").Table("People")
table.put_item(Item={
    "pk": "PERSON#1", "name": "John", "age": 30,
    "job": "Software Engineer"
})

Elasticsearch indexes JSON documents using its own client and API:

import os
from elasticsearch import Elasticsearch

es = Elasticsearch(
    os.environ["ELASTICSEARCH_URL"],
    api_key=os.environ["ELASTIC_API_KEY"]
)
es.index(index="people", id="1", document={
    "name": "John", "age": 30, "job": "Software Engineer"
})

Configure an HTTPS endpoint with an appropriate trusted certificate. The examples use their respective SDKs; neither client can call the other database interchangeably.

Query capabilities and consistency

DynamoDB offers conditional writes, transactions, secondary indexes and configurable base-table read consistency. Native vector search can support semantic similarity use cases, but it does not replace a full-text search engine's analyzers, fuzzy matching and text relevance tools.

Elasticsearch supports full-text search and analytical aggregations over indexed fields. Search visibility is near-real-time and depends on index refresh behavior; do not assume a successful indexing request is immediately searchable. It does not replace DynamoDB's multi-item transaction model.

Deployment and integration

Elasticsearch can be self-managed or run through Elastic's managed offerings. Amazon OpenSearch Service is a separate service and product lineage, not simply another name for current Elasticsearch. DynamoDB's managed zero-ETL search integration targets Amazon OpenSearch Service. A pipeline to Elasticsearch needs an integration that actually supports Elasticsearch.

A common design keeps authoritative application records in DynamoDB and maintains a search copy elsewhere, with retries and reconciliation for lag or failures. See DynamoDB Streams and native semantic search before choosing the search path.

Source: Elastic Python client documentation.