In the 1970s, Oracle Corporation became the first company to commercialize the relational database. At a time when software engineers were writing code on pads of paper, Oracle created software that provided tremendous gains in efficiency. The RDBMS became a standard and Oracle became one of most established and entrenched software vendors in the enterprise. Alternatives like MongoDB arrived in the 2000s to address new ways of storing and managing data.
Modern enterprises are thinking about ways to better leverage their data—whether it's to gain better customer insight, adapt to changing user expectations, or beat competitors to market with new applications and business models. As a result, many of the assumptions that drove the development of earlier relational databases have changed:
- Demands for higher developer productivity and faster time to market, with traditional rigid relational data models and waterfall development of monolithic applications giving way to agile methodologies, microservices, and DevOps, compressing release cycles from months and years to days and weeks.
- The need to manage massive increases in new, rapidly changing data types: structured, semi-structured, and polymorphic data generated by new classes of web, mobile, social, and IoT applications.
- The wholesale shift to distributed systems and cloud computing, enabling developers to exploit on-demand, highly scalable compute and storage infrastructure, with the ability to serve audiences any place they work and play around the globe while meeting a whole new set of regulatory demands for data sovereignty.
As a result, NoSQL (non-relational) databases, such as MongoDB, emerged to address the requirements of new applications and modernize existing workloads. With support for multi-document ACID transactions, MongoDB makes it easier than ever to address use cases that are now, or will be in the future, struggling with Oracle.
This page provides an overview of Oracle and MongoDB, highlighting the key differences and the appropriate use cases for each. You can learn more about the benefits of modernizing legacy systems and development processes by visiting our legacy modernization page.
What is Oracle?
Oracle is a global technology company specializing in database management systems. Its core database offering is Oracle Database 23ai. This is sold through a per-processing licensing model where price increases depending on the required add-ons for specific functionality. In Oracle, you pre-define your database schema based on your requirements. You then set up rules to govern the relationships between fields in your tables. Related information may be stored in separate tables. This information is then associated through the use of foreign keys and joins. Any changes in schema necessitate a migration procedure, which can take the database offline or significantly reduce application performance.
What is MongoDB?
MongoDB is a non-relational database developed by MongoDB, Inc. MongoDB stores structured, semistructured, and unstructured data as documents in a binary representation called BSON (Binary JSON). Related information is stored together for fast query access through the MongoDB Query Language (MQL). Fields can vary from document to document; there is no need to declare the structure of documents to the system—documents are self-describing. If a new field needs to be added to a document, it can be created without affecting all other documents in the collection, without updating a central system catalog, and without taking the system offline. Optionally, schema validation can be used to enforce data governance controls over each collection.
MongoDB's document data model maps naturally to objects in application code. This makes it simple for developers to learn and use. Documents give you the ability to represent hierarchical relationships to store arrays and other more complex structures easily.
Native, idiomatic drivers are provided for more than 12 languages—and the MongoDB community has built dozens more—enabling ad-hoc queries, real-time aggregation, and rich indexing for powerful programmatic ways to access and analyze data of any structure.
Documents can unify related data that would otherwise be modeled across separate parent-child tables in a relational schema. As a result, MongoDB's atomic single-document operations already provide transaction semantics that meet the data integrity needs of the majority of applications. One or more fields may be written in a single operation. This includes updates to multiple subdocuments and elements of an array. The guarantees provided by MongoDB ensure complete isolation as a document is updated; any errors cause the operation to roll back so that clients receive a consistent view of the document.
Unlike Oracle and other relational databases, MongoDB is built on a distributed system architecture rather than a monolithic, single-node design. As a result, MongoDB offers out-of-the-box scale-out and data localization via zone sharding and replica sets to maintain always-on availability.
Multi-Document ACID Transactions
Multi-document transactions make MongoDB the only database to combine the ACID guarantees of traditional relational databases and the speed, flexibility, and power of the document model, with an intelligent distributed systems design to scale-out and place data where you need it. Through snapshot isolation, transactions provide a consistent view of data and enforce all-or-nothing execution to maintain data integrity. Transactions in MongoDB feel just like transactions developers are familiar with in Oracle. They are multi-statement, with similar syntax (e.g. start_transaction and commit_transaction), and therefore easy for anyone with prior transaction experience to add to any application.
Why use MongoDB instead of Oracle?
Organizations of all sizes are adopting MongoDB because it enables them to build applications faster, handle highly diverse data types, and manage applications more efficiently at scale.
Development is simplified as MongoDB documents map naturally to modern, object-oriented programming languages. Using MongoDB removes the complex object-relational mapping (ORM) layer that translates objects in code to cells in relational tables.
When evaluating databases, it is critical to consider the relative costs of each solution—not just the cost of the software, but also the hardware, development, and deployment costs. Organizations can save millions of dollars by switching from Oracle to MongoDB, benefiting from improved data replication and gains in developer productivity, reduced licensing, and lower hardware requirements.
MongoDB can also be scaled within and across multiple distributed data centers. This provides new levels of availability and scalability previously unachievable with relational databases like Oracle. As your deployments grow in terms of data volume and throughput, MongoDB scales easily with no downtime and without changing your application. In contrast, to achieve scale with Oracle often requires significant, custom engineering work or investment in expensive, custom hardware.
If MongoDB is the right solution for you and you're currently using Oracle, check out our migration guide and MongoDB's Relational Migrator—a tool that simplifies migrating and transforming data from a relational database to MongoDB, de-risking your transition to MongoDB while helping you take full advantage of the document model.
Users Selecting MongoDB Over Oracle
As the following examples illustrate, the preference for MongoDB over Oracle is driven by radical improvements in developer productivity, application performance, and scale, while significantly reducing cost and lock-in.
To keep pace with demands from the business, Travelers Insurance modernized its development processes with a microservices architecture supported by agile and DevOps methodologies. But the rigidity of its existing Oracle and SQL Server databases prevented the company from moving at the speed it needed. The solution was MongoDB and its flexible data model, which is particularly well-suited for enterprise-grade applications. The company eliminated the three-day wait to make database changes, creating a software development pipeline that supports continuous delivery of new business functionality.
Financial giant RBS has modernized its investment banking systems with a new data fabric powered by MongoDB. As a result of decommissioning hundreds of Oracle servers, it has accelerated developer productivity to build new applications faster, created a simplified cloud-ready infrastructure for data scale, and avoided millions of dollars of cost.
Telefonica migrated its customer personalization service from Oracle to MongoDB. Using Oracle, it took seven developers, multiple iterations and 14 months to build a system that just didn't perform. Using MongoDB, a team of three developers built its new personalization service in three months, which now powers both legacy and new products across the globe. MongoDB helps Telefonica be more agile, save money, and drive new revenue streams.
China Eastern’s migration from Oracle to MongoDB enabled the company's project and engineering teams to build an application that was not possible with Oracle, transforming customer experience, and driving more business online. The simplicity of the document data model, dynamic schema, idiomatic drivers, and indexing flexibility means the development teams can now launch new applications faster while also unlocking significant cost savings.
When Would Oracle be a Better Fit?
While many organizations have successfully migrated from RDBMS to MongoDB, you cannot drop-in MongoDB as a replacement for legacy applications built around the relational data model and SQL. However, organizations are benefiting from modernizing mission-critical, revenue-generating applications to MongoDB. For example, Cisco moved from a legacy relational database to MongoDB and improved customer experience by reducing latency 8x while eliminating downtime for system upgrades. Cisco's development teams can build and release new applications faster, while the company's e-commerce platform can tap into the business agility enabled by cloud computing.
Terminology and Concepts
Many concepts in Oracle have close analogs in MongoDB. The table below outlines the common concepts between Oracle and MongoDB.
Feature Comparison
Like Oracle, MongoDB offers a rich set of features and functionality far beyond those offered by simple NoSQL data stores. MongoDB has a rich query language, highly functional secondary indexes (including text search and geospatial), a powerful aggregation framework for data analysis, faceted search, graph processing, and more. With MongoDB you can also make use of these features across more diverse data types than a relational database, and you can do it at scale.
Query Language
Both Oracle and MongoDB have a rich query language. Below are a few examples of SQL statements and how they map to MongoDB. A more comprehensive list of statements can be found in the MongoDB documentation.
Ready to Migrate From Oracle to MongoDB?
If you're considering migrating your data from Oracle to MongoDB, the Relational Migrator can help. It is a free MongoDB tool that simplifies and streamlines the migration process, significantly lowering the effort and risk of your migration initiative.
Ready to Migrate? Start with the Free Relational Migrator today!