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  1. Stackups
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  5. JDA vs Oracle

JDA vs Oracle

OverviewDecisionsComparisonAlternatives

Overview

Oracle
Oracle
Stacks2.6K
Followers1.8K
Votes113
JDA
JDA
Stacks18
Followers16
Votes1

JDA vs Oracle: What are the differences?

Introduction

In this Markdown code, we will discuss the key differences between JDA and Oracle.

  1. User Base: One of the key differences between JDA and Oracle is their user base. JDA is more commonly used by supply chain and retail companies for its specialized features and functionalities. On the other hand, Oracle is a widely used enterprise resource planning (ERP) system that caters to a diverse range of industries, including manufacturing, finance, healthcare, and more.

  2. Functionality: JDA is specifically designed to optimize supply chain processes and provide detailed insights into inventory management, demand planning, and transportation management. It offers advanced features like demand forecasting, replenishment optimization, and real-time visibility. On the other hand, Oracle provides a comprehensive suite of business applications that cover a wide range of functions including finance, human resources, customer relationship management (CRM), procurement, and more.

  3. Scalability: JDA is known for its scalability and ability to handle large volumes of data. It is capable of managing complex supply chain networks with thousands of locations, products, and suppliers. Oracle, being an enterprise-level solution, also offers scalability but may require additional resources and configuration depending on the size and complexity of the organization.

  4. Customizability: JDA provides a high level of configurability and customization options to cater to specific business requirements. It allows users to tailor the software to their unique supply chain processes. Oracle, on the other hand, offers a wide range of pre-built modules and workflows that can be customized to some extent, but may have certain limitations compared to JDA's flexibility.

  5. Integration Capabilities: JDA provides robust integration capabilities with other systems and platforms through APIs and connectors. It allows seamless integration with ERP systems, warehouse management systems, transportation management systems, and more. Oracle also offers extensive integration capabilities but is primarily focused on its own suite of applications. Integrating Oracle with third-party systems may require more effort and customization.

  6. Cost: The cost of implementing and maintaining JDA and Oracle can vary significantly. JDA's pricing is typically based on the number of users and the scope of functionality required. Oracle's pricing is more complex and depends on factors such as the number of users, modules, implementation services, and ongoing support. In general, Oracle is considered more expensive than JDA, especially for smaller organizations.

In summary, JDA and Oracle differ in terms of their user base, functionality, scalability, customizability, integration capabilities, and cost. While JDA is specialized for supply chain optimization, Oracle offers a broader suite of applications catering to various industries.

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Advice on Oracle, JDA

Daniel
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.

496k views496k
Comments
Abigail
Abigail

Dec 6, 2019

Decided

In the field of bioinformatics, we regularly work with hierarchical and unstructured document data. Unstructured text data from PDFs, image data from radiographs, phylogenetic trees and cladograms, network graphs, streaming ECG data... none of it fits into a traditional SQL database particularly well. As such, we prefer to use document oriented databases.

MongoDB is probably the oldest component in our stack besides Javascript, having been in it for over 5 years. At the time, we were looking for a technology that could simply cache our data visualization state (stored in JSON) in a database as-is without any destructive normalization. MongoDB was the perfect tool; and has been exceeding expectations ever since.

Trivia fact: some of the earliest electronic medical records (EMRs) used a document oriented database called MUMPS as early as the 1960s, prior to the invention of SQL. MUMPS is still in use today in systems like Epic and VistA, and stores upwards of 40% of all medical records at hospitals. So, we saw MongoDB as something as a 21st century version of the MUMPS database.

540k views540k
Comments
Abigail
Abigail

Dec 10, 2019

Decided

We wanted a JSON datastore that could save the state of our bioinformatics visualizations without destructive normalization. As a leading NoSQL data storage technology, MongoDB has been a perfect fit for our needs. Plus it's open source, and has an enterprise SLA scale-out path, with support of hosted solutions like Atlas. Mongo has been an absolute champ. So much so that SQL and Oracle have begun shipping JSON column types as a new feature for their databases. And when Fast Healthcare Interoperability Resources (FHIR) announced support for JSON, we basically had our FHIR datalake technology.

558k views558k
Comments

Detailed Comparison

Oracle
Oracle
JDA
JDA

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.

It strives to provide a clean and full wrapping of the Discord REST api and its Websocket-Events for Java. This library is a helpful tool that provides the functionality to create a discord bot in java.

-
Java 8;REST;Websocket;Automation
Statistics
Stacks
2.6K
Stacks
18
Followers
1.8K
Followers
16
Votes
113
Votes
1
Pros & Cons
Pros
  • 44
    Reliable
  • 33
    Enterprise
  • 15
    High Availability
  • 5
    Expensive
  • 5
    Hard to maintain
Cons
  • 14
    Expensive
Pros
  • 1
    Best Java Discord API
Integrations
No integrations available
Java
Java
Discord
Discord

What are some alternatives to Oracle, JDA?

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.

SQLite

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

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.

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