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Oracle vs SAP HANA: What are the differences?
Introduction
In this article, we will explore the key differences between Oracle and SAP HANA, two popular database management systems.
Performance: Oracle is known for its reliability and robustness in handling large volumes of data. It uses a traditional disk-based storage method, providing stable performance and scalability. On the other hand, SAP HANA is an in-memory database platform that stores data in RAM for faster processing. This allows SAP HANA to deliver incredibly high-speed performance and real-time analytics, making it an ideal choice for businesses that require immediate access to data.
Data Processing: Oracle follows a row-based data processing model, which means it retrieves and processes one row of data at a time. This approach is suited for transactional workloads. In contrast, SAP HANA utilizes a column-based data processing model that retrieves and processes data by columns. This columnar storage allows for faster data access and analysis, particularly beneficial for complex analytical queries and reporting.
Data Compression: When it comes to data compression, SAP HANA excels. It utilizes advanced compression algorithms that significantly reduce storage space requirements, resulting in cost savings and improved performance. Oracle also offers data compression techniques, but it may not achieve the same level of compression as SAP HANA.
Data Replication: Oracle provides various options for data replication, such as Oracle Data Guard and Oracle GoldenGate, offering high availability and disaster recovery capabilities. SAP HANA, on the other hand, incorporates replication tools like SAP Landscape Transformation (SLT) to replicate data from different sources. This enables real-time data replication and integration from various systems, supporting operational reporting and analytics.
Application Ecosystem: Oracle has a vast ecosystem of business applications and software solutions, making it easier for organizations to find compatible tools and resources. SAP HANA is tightly integrated with SAP's application suite, which includes enterprise resource planning, customer relationship management, and supply chain management solutions. This integration allows seamless data processing and analytical capabilities within the SAP environment.
Deployment Options: Oracle offers both on-premises and cloud-based deployment options. It provides flexibility for organizations to choose the deployment model that suits their needs. SAP HANA primarily focuses on providing a cloud-based deployment model, enabling users to leverage the infrastructure and scalability offered by cloud platforms like SAP Cloud Platform and Amazon Web Services.
In summary, Oracle is known for its reliability and disk-based storage, while SAP HANA excels in performance through its in-memory processing and columnar data storage. SAP HANA offers advanced data compression, real-time replication tools, tight integration with SAP applications, and primarily focuses on cloud deployment.
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.
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.
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.
Pros of Oracle
- Reliable44
- Enterprise33
- High Availability15
- Hard to maintain5
- Expensive5
- Maintainable4
- Hard to use4
- High complexity3
Pros of SAP HANA
- In-memory5
- SQL5
- Distributed4
- Performance4
- Realtime2
- Concurrent2
- OLAP2
- OLTP2
- JSON1
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Cons of Oracle
- Expensive14
