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Oracle vs Snowflake: What are the differences?
Differences between Oracle and Snowflake
Query Language: Oracle uses SQL as its query language, while Snowflake uses a variant of SQL called SQL-like language. Although both languages are similar, Snowflake's SQL-like language incorporates some unique syntax and features specific to its platform.
Architecture: Oracle follows a traditional on-premises or single-node architecture, where a single database server handles all data processing tasks. On the other hand, Snowflake utilizes a cloud-based architecture, which includes separate compute and storage layers. This architecture allows Snowflake to deliver elastic scalability and separation of compute and storage, resulting in better performance and cost optimization.
Scalability: Oracle's scalability is limited to the capacity of its hardware and infrastructure, requiring manual adjustments to handle increased workloads. In contrast, Snowflake provides automatic and on-demand scalability by decoupling compute and storage resources. Snowflake can seamlessly scale up or down based on workload requirements without any manual intervention.
Data Sharing: Oracle allows data sharing through traditional methods such as exports, imports, and replication, requiring significant effort to maintain data synchronization across multiple instances. In contrast, Snowflake offers built-in data sharing capabilities, allowing seamless sharing of live data between multiple accounts, departments, or organizations without the need for complex data movement processes.
Concurrency: Oracle traditionally handles concurrency by executing multiple queries concurrently but with some limitations. Snowflake, in contrast, provides robust concurrency management with true parallel execution of queries. Snowflake's architecture ensures that multiple users can run queries concurrently without affecting performance or resource allocation.
Cost Model: Oracle typically employs a fixed cost model, requiring upfront investments in hardware and software licenses. Snowflake, being a cloud-based platform, follows a pay-as-you-go pricing model. This means users are charged only for the resources they consume, allowing for more flexibility and cost optimization.
In Summary, Oracle relies on a traditional on-premises architecture with SQL as its query language, while Snowflake utilizes a cloud-based architecture with a SQL-like language. Snowflake offers better scalability, automatic concurrency management, built-in data sharing capabilities, and a flexible cost model compared to Oracle.
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 Snowflake
- Public and Private Data Sharing7
- Multicloud4
- Good Performance4
- User Friendly4
- Great Documentation3
- Serverless2
- Economical1
- Usage based billing1
- Innovative1
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Cons of Oracle
- Expensive14
Cons of Snowflake
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What is Oracle?
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