Hadoop vs Oracle

Overview

Oracle

Stacks2.6K

Followers1.8K

Votes113

Hadoop

Stacks2.7K

Followers2.3K

Votes56

GitHub Stars15.3K

Forks9.1K

Hadoop vs Oracle: What are the differences?

Introduction:

Hadoop and Oracle are both essential tools in the realm of big data management and analytics. While they serve similar purposes, there are key differences between the two that make each suitable for distinct use cases. Below are the crucial disparities that distinguish Hadoop from Oracle.

Architecture: Hadoop follows a distributed file system architecture, allowing it to store and process massive amounts of data across a cluster of commodity hardware. On the other hand, Oracle is based on a centralized database architecture, which is well-suited for transactional processing and structured data.
Scalability: Hadoop is highly scalable and can effortlessly scale both vertically and horizontally to accommodate growing data volumes and processing requirements. In contrast, Oracle's scalability is typically achieved through costly hardware upgrades and may face limitations in handling petabytes of data efficiently.
Data Types: Hadoop is designed to handle both structured and unstructured data, making it ideal for processing diverse data formats such as text, images, and videos. Oracle, on the other hand, excels in managing structured relational data and is optimized for complex transactional processing.
Cost: Hadoop is generally perceived as a cost-effective solution for big data analytics due to its open-source nature and ability to run on commodity hardware. In contrast, Oracle is a commercial database system that involves licensing fees, maintenance costs, and expenses associated with proprietary hardware.
Data Processing Model: Hadoop employs a batch processing model suitable for processing large datasets with high latency requirements, making it ideal for tasks like log processing and ETL jobs. Oracle, on the other hand, supports real-time transaction processing and complex queries, making it preferable for interactive applications with low latency demands.
Ecosystem: Hadoop offers a rich ecosystem of tools and frameworks such as Apache Spark, Pig, and Hive, enhancing its capabilities for data processing, analysis, and machine learning. Oracle, while also having a robust ecosystem of tools, may require additional licensing for accessing certain features and functionalities.

In Summary, Hadoop and Oracle differ significantly in architecture, scalability, data types, cost, data processing models, and ecosystem, making each better suited for specific data management and analytics requirements.

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Advice on Oracle, Hadoop

Daniel

Data Engineer at Dimensigon

Jul 18, 2020

Decided

We have chosen Tibero over Oracle because we want to offer a PL/SQL-as-a-Service that the users can deploy in any Cloud without concerns from our website at some standard cost. With Oracle Database, developers would have to worry about what they implement and the related costs of each feature but the licensing model from Tibero is just 1 price and we have all features included, so we don't have to worry and developers using our SQLaaS neither. PostgreSQL would be open source. We have chosen Tibero over Oracle because we want to offer a PL/SQL that you can deploy in any Cloud without concerns. PostgreSQL would be the open source option but we need to offer an SQLaaS with encryption and more enterprise features in the background and best value option we have found, it was Tibero Database for PL/SQL-based applications.

495k views495k

Comments

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

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

Oracle	Hadoop
Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database.	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.
Statistics
GitHub Stars -	GitHub Stars 15.3K
GitHub Forks -	GitHub Forks 9.1K
Stacks 2.6K	Stacks 2.7K
Followers 1.8K	Followers 2.3K
Votes 113	Votes 56
Pros & Cons
Pros 44 Reliable 33 Enterprise 15 High Availability 5 Hard to maintain 5 Expensive Cons 14 Expensive	Pros 39 Great ecosystem 11 One stack to rule them all 4 Great load balancer 1 Java syntax 1 Amazon aws

What are some alternatives to Oracle, Hadoop?

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.

Related Comparisons

Hadoop vs Oracle: What are the differences?

Introduction:

Architecture: Hadoop follows a distributed file system architecture, allowing it to store and process massive amounts of data across a cluster of commodity hardware. On the other hand, Oracle is based on a centralized database architecture, which is well-suited for transactional processing and structured data.
Scalability: Hadoop is highly scalable and can effortlessly scale both vertically and horizontally to accommodate growing data volumes and processing requirements. In contrast, Oracle's scalability is typically achieved through costly hardware upgrades and may face limitations in handling petabytes of data efficiently.
Data Types: Hadoop is designed to handle both structured and unstructured data, making it ideal for processing diverse data formats such as text, images, and videos. Oracle, on the other hand, excels in managing structured relational data and is optimized for complex transactional processing.
Cost: Hadoop is generally perceived as a cost-effective solution for big data analytics due to its open-source nature and ability to run on commodity hardware. In contrast, Oracle is a commercial database system that involves licensing fees, maintenance costs, and expenses associated with proprietary hardware.
Data Processing Model: Hadoop employs a batch processing model suitable for processing large datasets with high latency requirements, making it ideal for tasks like log processing and ETL jobs. Oracle, on the other hand, supports real-time transaction processing and complex queries, making it preferable for interactive applications with low latency demands.
Ecosystem: Hadoop offers a rich ecosystem of tools and frameworks such as Apache Spark, Pig, and Hive, enhancing its capabilities for data processing, analysis, and machine learning. Oracle, while also having a robust ecosystem of tools, may require additional licensing for accessing certain features and functionalities.

Hadoop vs Oracle

Overview