StackShareStackShare
Follow on
StackShare

Discover and share technology stacks from companies around the world.

Follow on

© 2025 StackShare. All rights reserved.

Product

  • Stacks
  • Tools
  • Feed

Company

  • About
  • Contact

Legal

  • Privacy Policy
  • Terms of Service
  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. Hadoop vs SQLite

Hadoop vs SQLite

OverviewDecisionsComparisonAlternatives

Overview

Hadoop
Hadoop
Stacks2.7K
Followers2.3K
Votes56
GitHub Stars15.3K
Forks9.1K
SQLite
SQLite
Stacks19.9K
Followers15.2K
Votes535

Hadoop vs SQLite: What are the differences?

Introduction

This website provides a comparison between Hadoop and SQLite by highlighting their key differences.

  1. Scalability: One of the key differences between Hadoop and SQLite is their scalability. Hadoop is designed to handle massive amounts of data by distributing it across multiple nodes in a cluster, allowing for parallel processing. On the other hand, SQLite is a lightweight database that is more suitable for small to medium-sized applications, as it operates on a single machine.

  2. Data Processing Model: Another major difference is their data processing models. Hadoop follows a batch processing model, which is ideal for processing large volumes of data in parallel. It breaks down tasks into small chunks and distributes them across multiple nodes. SQLite, on the other hand, follows a transactional processing model, which is more suited for traditional database operations requiring ACID (Atomicity, Consistency, Isolation, Durability) compliance.

  3. Fault Tolerance: Hadoop provides built-in fault tolerance through data replication. It stores multiple copies of data on different nodes, ensuring that if a node fails, the data can still be accessed from the replicas. SQLite, on the other hand, does not have built-in fault tolerance mechanisms. If the machine running SQLite fails, it may result in data loss or downtime.

  4. Data Storage: Hadoop is optimized for storing and processing large volumes of unstructured or semi-structured data, such as log files, sensor data, or social media feeds. It utilizes distributed file systems like Hadoop Distributed File System (HDFS) to efficiently store and retrieve data. SQLite, on the other hand, is more suitable for structured data storage, such as relational databases, where data is organized into tables with predefined schemas.

  5. Concurrency: Hadoop is designed to handle concurrent data processing on a large scale. It can process multiple tasks in parallel, allowing for efficient utilization of resources. SQLite, on the other hand, has limited concurrency support. It allows multiple read operations simultaneously but serializes write operations, which may result in slower performance in highly concurrent environments.

  6. Deployment Complexity: Hadoop is a complex ecosystem with various components like HDFS, MapReduce, and YARN. Setting up and managing a Hadoop cluster requires expertise and infrastructure resources. SQLite, on the other hand, is a self-contained database engine that can be easily deployed and managed on a single machine or embedded within applications with minimal configuration.

In summary, Hadoop is a highly scalable and fault-tolerant framework designed for processing big data in a distributed environment, while SQLite is a lightweight database engine suitable for small to medium-sized applications with structured data requirements, offering simpler deployment and management.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs
CLI (Node.js)
or
Manual

Advice on Hadoop, 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
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
Jasmine
Jasmine

Feb 12, 2021

Decided

Backend:

  • Considering that our main app functionality involves data processing, we chose Python as the programming language because it offers many powerful math libraries for data-related tasks. We will use Flask for the server due to its good integration with Python. We will use a relational database because it has good performance and we are mostly dealing with CSV files that have a fixed structure. We originally chose SQLite, but after realizing the limitations of file-based databases, we decided to switch to PostgreSQL, which has better compatibility with our hosting service, Heroku.
175k views175k
Comments

Detailed Comparison

Hadoop
Hadoop
SQLite
SQLite

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

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.

Statistics
GitHub Stars
15.3K
GitHub Stars
-
GitHub Forks
9.1K
GitHub Forks
-
Stacks
2.7K
Stacks
19.9K
Followers
2.3K
Followers
15.2K
Votes
56
Votes
535
Pros & Cons
Pros
  • 39
    Great ecosystem
  • 11
    One stack to rule them all
  • 4
    Great load balancer
  • 1
    Amazon aws
  • 1
    Java syntax
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

What are some alternatives to Hadoop, 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.

Related Comparisons

Bootstrap
Materialize

Bootstrap vs Materialize

Laravel
Django

Django vs Laravel vs Node.js

Bootstrap
Foundation

Bootstrap vs Foundation vs Material UI

Node.js
Spring Boot

Node.js vs Spring-Boot

Liquibase
Flyway

Flyway vs Liquibase