Amazon DynamoDB vs Elasticsearch

Overview

Amazon DynamoDB

Stacks4.0K

Followers3.2K

Votes195

Elasticsearch

Stacks35.5K

Followers27.1K

Votes1.6K

Amazon DynamoDB vs Elasticsearch: What are the differences?

Introduction:

Amazon DynamoDB and Elasticsearch are both popular database technologies used in the industry. Despite being used for similar purposes, they have key differences in terms of their architecture and functionalities.

Data Model: Amazon DynamoDB is a NoSQL database with a flexible schema where each item can have different attributes, while Elasticsearch is schema-free and document-oriented, allowing for complex nested data structures.
Query Language: DynamoDB uses a simple query language with limited complex querying capabilities, whereas Elasticsearch provides a powerful search DSL (Domain Specific Language) allowing for advanced queries like full-text search, aggregations, and filtering.
Scalability: Amazon DynamoDB is fully managed and scales automatically by adjusting read and write capacity units, while Elasticsearch requires manual scaling by adding more nodes to the cluster to handle increased workloads.
Use Cases: DynamoDB is optimized for fast and predictable performance on small to large-scale applications with high availability requirements, while Elasticsearch is designed for full-text search and analytics use cases, making it a go-to solution for log analysis and real-time data monitoring.
Data Replication: In DynamoDB, data replication is handled by the service itself across multiple Availability Zones for high durability, while Elasticsearch provides the ability to set up data replication and sharding configurations for improved performance and fault tolerance.
Indexing Capabilities: DynamoDB does not provide indexing capabilities beyond primary and secondary indexes, whereas Elasticsearch has a sophisticated indexing mechanism that includes inverted indexes, allowing for fast and efficient search operations on large data sets.

In Summary, Amazon DynamoDB and Elasticsearch differ in their data model flexibility, query capabilities, scalability options, use case suitability, data replication mechanisms, and indexing capabilities.

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Advice on Amazon DynamoDB, Elasticsearch

Doru

Solution Architect

Jun 9, 2019

Reviewon

Amazon DynamoDB

I use Amazon DynamoDB because it integrates seamlessly with other AWS SaaS solutions and if cost is the primary concern early on, then this will be a better choice when compared to AWS RDS or any other solution that requires the creation of a HA cluster of IaaS components that will cost money just for being there, the costs not being influenced primarily by usage.

1.36k views1.36k

Comments

Rana Usman

Chief Technology Officer at TechAvanza

Jun 4, 2020

Needs adviceon

Firebase

Elasticsearch

Algolia

Hey everybody! (1) I am developing an android application. I have data of around 3 million record (less than a TB). I want to save that data in the cloud. Which company provides the best cloud database services that would suit my scenario? It should be secured, long term useable, and provide better services. I decided to use Firebase Realtime database. Should I stick with Firebase or are there any other companies that provide a better service?

(2) I have the functionality of searching data in my app. Same data (less than a TB). Which search solution should I use in this case? I found Elasticsearch and Algolia search. It should be secure and fast. If any other company provides better services than these, please feel free to suggest them.

Thank you!

408k views408k

Comments

Detailed Comparison

Amazon DynamoDB	Elasticsearch
With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.	Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
Automated Storage Scaling – There is no limit to the amount of data you can store in a DynamoDB table, and the service automatically allocates more storage, as you store more data using the DynamoDB write APIs;Provisioned Throughput – When creating a table, simply specify how much request capacity you require. DynamoDB allocates dedicated resources to your table to meet your performance requirements, and automatically partitions data over a sufficient number of servers to meet your request capacity;Fully Distributed, Shared Nothing Architecture	Distributed and Highly Available Search Engine;Multi Tenant with Multi Types;Various set of APIs including RESTful;Clients available in many languages including Java, Python, .NET, C#, Groovy, and more;Document oriented;Reliable, Asynchronous Write Behind for long term persistency;(Near) Real Time Search;Built on top of Apache Lucene;Per operation consistency;Inverted indices with finite state transducers for full-text querying;BKD trees for storing numeric and geo data;Column store for analytics;Compatible with Hadoop using the ES-Hadoop connector;Open Source under Apache 2 and Elastic License
Statistics
Stacks 4.0K	Stacks 35.5K
Followers 3.2K	Followers 27.1K
Votes 195	Votes 1.6K
Pros & Cons
Pros 62 Predictable performance and cost 56 Scalable 35 Native JSON Support 21 AWS Free Tier 7 Fast Cons 4 Only sequential access for paginate data 1 Document Limit Size 1 Scaling	Pros 329 Powerful api 315 Great search engine 231 Open source 214 Restful 200 Near real-time search Cons 7 Resource hungry 6 Diffecult to get started 5 Expensive 4 Hard to keep stable at large scale
Integrations
Amazon RDS for PostgreSQL PostgreSQL MySQL SQLite Azure Database for MySQL	Kibana Beats Logstash

What are some alternatives to Amazon DynamoDB, Elasticsearch?

Algolia

Our mission is to make you a search expert. Push data to our API to make it searchable in real time. Build your dream front end with one of our web or mobile UI libraries. Tune relevance and get analytics right from your dashboard.

Azure Cosmos DB

Azure DocumentDB is a fully managed NoSQL database service built for fast and predictable performance, high availability, elastic scaling, global distribution, and ease of development.

Cloud Firestore

Cloud Firestore is a NoSQL document database that lets you easily store, sync, and query data for your mobile and web apps - at global scale.

Typesense

It is an open source, typo tolerant search engine that delivers fast and relevant results out-of-the-box. has been built from scratch to offer a delightful, out-of-the-box search experience. From instant search to autosuggest, to faceted search, it has got you covered.

Cloudant

Cloudant’s distributed database as a service (DBaaS) allows developers of fast-growing web and mobile apps to focus on building and improving their products, instead of worrying about scaling and managing databases on their own.

Amazon CloudSearch

Amazon CloudSearch enables you to search large collections of data such as web pages, document files, forum posts, or product information. With a few clicks in the AWS Management Console, you can create a search domain, upload the data you want to make searchable to Amazon CloudSearch, and the search service automatically provisions the required technology resources and deploys a highly tuned search index.

Google Cloud Bigtable

Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail.

Amazon Elasticsearch Service

Amazon Elasticsearch Service is a fully managed service that makes it easy for you to deploy, secure, and operate Elasticsearch at scale with zero down time.

Manticore Search

It is a full-text search engine written in C++ and a fork of Sphinx Search. It's designed to be simple to use, light and fast, while allowing advanced full-text searching. Connectivity is provided via a MySQL compatible protocol or HTTP, making it easy to integrate.

Azure Search

Azure Search makes it easy to add powerful and sophisticated search capabilities to your website or application. Quickly and easily tune search results and construct rich, fine-tuned ranking models to tie search results to business goals. Reliable throughput and storage provide fast search indexing and querying to support time-sensitive search scenarios.

Related Comparisons

Amazon DynamoDB vs Elasticsearch: What are the differences?

Introduction:

Data Model: Amazon DynamoDB is a NoSQL database with a flexible schema where each item can have different attributes, while Elasticsearch is schema-free and document-oriented, allowing for complex nested data structures.
Query Language: DynamoDB uses a simple query language with limited complex querying capabilities, whereas Elasticsearch provides a powerful search DSL (Domain Specific Language) allowing for advanced queries like full-text search, aggregations, and filtering.
Scalability: Amazon DynamoDB is fully managed and scales automatically by adjusting read and write capacity units, while Elasticsearch requires manual scaling by adding more nodes to the cluster to handle increased workloads.
Use Cases: DynamoDB is optimized for fast and predictable performance on small to large-scale applications with high availability requirements, while Elasticsearch is designed for full-text search and analytics use cases, making it a go-to solution for log analysis and real-time data monitoring.
Data Replication: In DynamoDB, data replication is handled by the service itself across multiple Availability Zones for high durability, while Elasticsearch provides the ability to set up data replication and sharding configurations for improved performance and fault tolerance.
Indexing Capabilities: DynamoDB does not provide indexing capabilities beyond primary and secondary indexes, whereas Elasticsearch has a sophisticated indexing mechanism that includes inverted indexes, allowing for fast and efficient search operations on large data sets.

Amazon DynamoDB vs Elasticsearch

Overview