ArangoDB vs Hadoop

Overview

Hadoop

Stacks2.7K

Followers2.3K

Votes56

GitHub Stars15.3K

Forks9.1K

ArangoDB

Stacks273

Followers442

Votes192

ArangoDB vs Hadoop: What are the differences?

Data Model: ArangoDB is a multi-model database that supports key-value pairs, documents, and graphs, allowing for flexible data modeling. On the other hand, Hadoop is primarily designed for handling large-scale distributed data processing using a file system approach.
Query Language: ArangoDB uses its query language called AQL (ArangoDB Query Language), which is SQL-like and allows for complex queries involving joins, aggregations, and graph traversals. Hadoop, on the other hand, relies on MapReduce for data processing, which requires writing and executing custom Java code for querying.
Scalability: ArangoDB can scale both vertically and horizontally, allowing for increased performance as data volume grows by adding more resources to a single server or distributing data across multiple servers. In contrast, Hadoop excels in horizontal scalability by distributing data and processing across a cluster of commodity hardware.
Real-time Processing: ArangoDB supports real-time processing and analytics on live data streams, making it suitable for applications requiring instant insights or responses. Hadoop, in comparison, is better suited for batch processing and offline analytics due to its reliance on MapReduce.
Ease of Use: ArangoDB provides a user-friendly interface and comprehensive documentation, making it easier for developers to get started with data modeling, querying, and administration. Hadoop, on the other hand, has a steeper learning curve due to its complex ecosystem of tools and dependencies, requiring specialized skills to set up and manage clusters effectively.
Use Cases: ArangoDB is well-suited for applications requiring a combination of document, key-value, and graph data models, such as social networking, content management, and recommendation systems. Meanwhile, Hadoop is commonly used for processing large volumes of data in batch mode, such as log analysis, data warehousing, and ETL (Extract, Transform, Load) operations.

In Summary, ArangoDB excels in its flexibility and ease of use for multi-model data processing, real-time analytics, and diverse use cases, while Hadoop is optimized for handling large-scale distributed data processing through its MapReduce framework.

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Advice on Hadoop, ArangoDB

SVP CTO

Apr 22, 2021

Needs adviceon

MarkLogic

Hadoop

Snowflake

For a property and casualty insurance company, we currently use MarkLogic and Hadoop for our raw data lake. Trying to figure out how snowflake fits in the picture. Does anybody have some good suggestions/best practices for when to use and what data to store in Mark logic versus Snowflake versus a hadoop or all three of these platforms redundant with one another?

136k views136k

Comments

gitgkk

Oct 19, 2021

Needs adviceon

TinyMCE

JSON

ArangoDB

Hello All, I'm building an app that will enable users to create documents using ckeditor or TinyMCE editor. The data is then stored in a database and retrieved to display to the user, these docs can contain image data also. The number of pages generated for a single document can go up to 1000. Therefore by design, each page is stored in a separate JSON. I'm wondering which database is the right one to choose between ArangoDB and PostgreSQL. Your thoughts, advice please. Thanks, Kashyap

64.3k views64.3k

Comments

SVP CTO

Apr 22, 2021

Needs advice

for property and casualty insurance company we current Use marklogic and Hadoop for our raw data lake. Trying to figure out how snowflake fits in the picture. Does anybody have some good suggestions/best practices for when to use and what data to store in Mark logic versus snowflake versus a hadoop or all three of these platforms redundant with one another?

23.6k views23.6k

Comments

Detailed Comparison

Hadoop	ArangoDB
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.	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.
-	multi-model nosql db; acid; transactions; javascript; database; nosql; sharding; replication; query language; joins; aql; documents; graphs; key-values; graphdb
Statistics
GitHub Stars 15.3K	GitHub Stars -
GitHub Forks 9.1K	GitHub Forks -
Stacks 2.7K	Stacks 273
Followers 2.3K	Followers 442
Votes 56	Votes 192
Pros & Cons
Pros 39 Great ecosystem 11 One stack to rule them all 4 Great load balancer 1 Java syntax 1 Amazon aws	Pros 37 Grahps and documents in one DB 26 Intuitive and rich query language 25 Good documentation 25 Open source 21 Joins for collections Cons 3 Web ui has still room for improvement 2 No support for blueprints standard, using custom AQL

What are some alternatives to Hadoop, ArangoDB?

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

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

SQLite

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.

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 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

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 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.

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

ArangoDB vs Hadoop: What are the differences?

Data Model: ArangoDB is a multi-model database that supports key-value pairs, documents, and graphs, allowing for flexible data modeling. On the other hand, Hadoop is primarily designed for handling large-scale distributed data processing using a file system approach.
Query Language: ArangoDB uses its query language called AQL (ArangoDB Query Language), which is SQL-like and allows for complex queries involving joins, aggregations, and graph traversals. Hadoop, on the other hand, relies on MapReduce for data processing, which requires writing and executing custom Java code for querying.
Scalability: ArangoDB can scale both vertically and horizontally, allowing for increased performance as data volume grows by adding more resources to a single server or distributing data across multiple servers. In contrast, Hadoop excels in horizontal scalability by distributing data and processing across a cluster of commodity hardware.
Real-time Processing: ArangoDB supports real-time processing and analytics on live data streams, making it suitable for applications requiring instant insights or responses. Hadoop, in comparison, is better suited for batch processing and offline analytics due to its reliance on MapReduce.
Ease of Use: ArangoDB provides a user-friendly interface and comprehensive documentation, making it easier for developers to get started with data modeling, querying, and administration. Hadoop, on the other hand, has a steeper learning curve due to its complex ecosystem of tools and dependencies, requiring specialized skills to set up and manage clusters effectively.
Use Cases: ArangoDB is well-suited for applications requiring a combination of document, key-value, and graph data models, such as social networking, content management, and recommendation systems. Meanwhile, Hadoop is commonly used for processing large volumes of data in batch mode, such as log analysis, data warehousing, and ETL (Extract, Transform, Load) operations.

ArangoDB vs Hadoop

Overview