Hadoop vs Firebird

Overview

Hadoop

Stacks2.7K

Followers2.3K

Votes56

GitHub Stars15.3K

Forks9.1K

Firebird

Stacks83

Followers121

Votes9

GitHub Stars1.4K

Forks263

Firebird vs Hadoop: What are the differences?

What is Firebird? Relational database offering many ANSI SQL standard features that runs on Linux, Windows, and a variety of Unix platform. Firebird is a relational database offering many ANSI SQL standard features that runs on Linux, Windows, MacOS and a variety of Unix platforms. Firebird offers excellent concurrency, high performance, and powerful language support for stored procedures and triggers. It has been used in production systems, under a variety of names, since 1981.

What is Hadoop? Open-source software for reliable, scalable, distributed computing. 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.

Firebird and Hadoop can be categorized as "Databases" tools.

Firebird and Hadoop are both open source tools. Hadoop with 9.26K GitHub stars and 5.78K forks on GitHub appears to be more popular than Firebird with 347 GitHub stars and 95 GitHub forks.

Airbnb, Uber Technologies, and Spotify are some of the popular companies that use Hadoop, whereas Firebird is used by IBSurgeon Ltd, KingHost, and Itasoft Software. Hadoop has a broader approval, being mentioned in 237 company stacks & 127 developers stacks; compared to Firebird, which is listed in 3 company stacks and 5 developer stacks.

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Advice on Hadoop, Firebird

pionell

Sep 16, 2020

Needs adviceon

MariaDB

I have a lot of data that's currently sitting in a MariaDB database, a lot of tables that weigh 200gb with indexes. Most of the large tables have a date column which is always filtered, but there are usually 4-6 additional columns that are filtered and used for statistics. I'm trying to figure out the best tool for storing and analyzing large amounts of data. Preferably self-hosted or a cheap solution. The current problem I'm running into is speed. Even with pretty good indexes, if I'm trying to load a large dataset, it's pretty slow.

159k views159k

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Detailed Comparison

Hadoop	Firebird
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.	Firebird is a relational database offering many ANSI SQL standard features that runs on Linux, Windows, MacOS and a variety of Unix platforms. Firebird offers excellent concurrency, high performance, and powerful language support for stored procedures and triggers. It has been used in production systems, under a variety of names, since 1981.
Statistics
GitHub Stars 15.3K	GitHub Stars 1.4K
GitHub Forks 9.1K	GitHub Forks 263
Stacks 2.7K	Stacks 83
Followers 2.3K	Followers 121
Votes 56	Votes 9
Pros & Cons
Pros 39 Great ecosystem 11 One stack to rule them all 4 Great load balancer 1 Java syntax 1 Amazon aws	Pros 3 Open-Source 3 Free 1 Upgrade from MySQL, MariaDB, PostgreSQL 1 Easy Setup 1 Great Performance Cons 2 Speed

What are some alternatives to Hadoop, Firebird?

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.

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.