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  1. Stackups
  2. Application & Data
  3. NoSQL Databases
  4. NOSQL Database As A Service
  5. Amazon DynamoDB vs SQLite

Amazon DynamoDB vs SQLite

OverviewDecisionsComparisonAlternatives

Overview

Amazon DynamoDB
Amazon DynamoDB
Stacks4.0K
Followers3.2K
Votes195
SQLite
SQLite
Stacks19.9K
Followers15.2K
Votes535

Amazon DynamoDB vs SQLite: What are the differences?

Introduction Amazon DynamoDB and SQLite are both popular database management systems but they have significant differences in terms of their architecture, scalability, and query capabilities.

  1. Scalability: One key difference between Amazon DynamoDB and SQLite is their scalability. DynamoDB is a fully managed NoSQL database that automatically scales up or down based on the workload, allowing users to handle fluctuations in traffic without any manual intervention. On the other hand, SQLite is a file-based database that does not support automatic scaling. It is designed for single-machine use and lacks built-in support for scaling to handle high traffic or large datasets.

  2. Data Model: Another difference lies in the data model of DynamoDB and SQLite. DynamoDB is a schema-less database, meaning it doesn't require a predefined schema and can handle unstructured or semi-structured data easily. It allows for flexible data models and efficient horizontal scaling. SQLite, on the other hand, is a relational database that requires a predefined schema to store data. It follows strict ACID (Atomicity, Consistency, Isolation, Durability) compliance and is suitable for structured data.

  3. Query Language: DynamoDB uses JSON-like query language known as Query and Scan operations, which allow users to fetch data based on specific conditions and indexes. It also supports pagination, filtering, and sorting of data. SQLite, being an SQL-based database, supports the full range of SQL queries including complex join operations, aggregations, and nested queries. This makes it more suitable for advanced querying and data analysis tasks.

  4. Durability and Replication: DynamoDB offers automatic data replication across multiple availability zones to ensure high durability and fault tolerance. It provides three different levels of data durability to choose from based on the application's requirements. On the other side, SQLite usually operates as a single, standalone database file without built-in replication or durability features. Users need to implement their own backup and replication strategies if needed.

  5. Hosting and Infrastructure: Amazon DynamoDB is a cloud-based database service provided by AWS, which means it is fully managed and hosted by Amazon. It takes care of all the underlying infrastructure, scaling, and maintenance tasks. SQLite, on the other hand, is self-contained and runs on the user's local machine or within specific applications. It requires minimal setup and is suitable for embedded systems or small-scale deployments.

  6. Cost: Finally, there is a significant difference in the cost structure of DynamoDB and SQLite. DynamoDB is a managed service and users are billed based on the throughput capacity provided and the amount of data stored. It offers a pay-as-you-go pricing model with various options for provisioned throughput and on-demand capacity. SQLite, being a free and open-source database, does not have any direct cost associated with it. However, users may need to consider the cost of hosting infrastructure and additional resources required to manage and maintain the SQLite database.

In summary, Amazon DynamoDB and SQLite differ in their scalability, data model, query capabilities, durability, hosting infrastructure, and cost structures. DynamoDB is a fully managed NoSQL database built for scalability and flexibility, while SQLite is a file-based relational database suitable for single-machine use with a predefined schema. Choosing between the two depends on the specific requirements of the application or project.

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Advice on Amazon DynamoDB, SQLite

Dimelo
Dimelo

Nov 5, 2020

Needs adviceonSQLiteSQLiteMySQLMySQLPostgreSQLPostgreSQL

I need to add a DBMS to my stack, but I don't know which. I'm tempted to learn SQLite since it would be useful to me with its focus on local access without concurrency. However, doing so feels like I would be defeating the purpose of trying to expand my skill set since it seems like most enterprise applications have the opposite requirements.

To be able to apply what I learn to more projects, what should I try to learn? MySQL? PostgreSQL? Something else? Is there a comfortable middle ground between high applicability and ease of use?

670k views670k
Comments
Doru
Doru

Solution Architect

Jun 9, 2019

ReviewonAmazon DynamoDBAmazon 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
Stephen
Stephen

Senior DevOps Engineer at Vital Beats

Nov 9, 2020

Review

A question you might want to think about is "What kind of experience do I want to gain, by using a DBMS?". If your aim is to have experience with SQL and any related libraries and frameworks for your language of choice (python, I think?), then it kind of doesn't matter too much which you pick so much. As others have said, SQLite would offer you the ability to very easily get started, and would give you a reasonably standard (if a little basic) SQL dialect to work with.

If your aim is actually to have a bit of "operational" experience, in terms of things like what command line tools might be available as standard for the DBMS, understanding how the DBMS handles multiple databases, when to use multiple schemas vs multiple databases, some basic privilege management etc. Then I would recommend PostgreSQL. SQLite's simplicity actually avoids most of these experiences, which is not helpful to you if that is what you hope to learn. MySQL has a few "quirks" to how it manages things like multiple databases, which may lead you to making less good decisions if you tried to take your experience over to different DBMS, especially in bigger enterprise roles. PostgreSQL is kind of a happy middle ground here, with the ability to start PostgreSQL servers via docker or docker-compose making the actual day-to-day management pretty easy, while still giving you experience of the kinds of considerations I have listed above.

At Vital Beats we make use of PostgreSQL, largely because it offers us a happy balance between good management and backup of data, and good standard command line tools, which is essential for us where we are deploying our solutions within Kubernetes / docker, and so more graphical tools are not always appropriate for us. PostgreSQL is also pretty universally supported in terms of language libraries and frameworks, without having to make compromises on how we want to store and layout our data.

316k views316k
Comments

Detailed Comparison

Amazon DynamoDB
Amazon DynamoDB
SQLite
SQLite

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.

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

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
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Statistics
Stacks
4.0K
Stacks
19.9K
Followers
3.2K
Followers
15.2K
Votes
195
Votes
535
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
    Scaling
  • 1
    Document Limit Size
Pros
  • 163
    Lightweight
  • 135
    Portable
  • 122
    Simple
  • 81
    Sql
  • 29
    Preinstalled on iOS and Android
Cons
  • 2
    Not for multi-process of multithreaded apps
  • 1
    Needs different binaries for each platform
Integrations
Amazon RDS for PostgreSQL
Amazon RDS for PostgreSQL
PostgreSQL
PostgreSQL
MySQL
MySQL
Azure Database for MySQL
Azure Database for MySQL
No integrations available

What are some alternatives to Amazon DynamoDB, SQLite?

MongoDB

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

Cassandra

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

InfluxDB

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

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