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Kudu vs Oracle: What are the differences?
Introduction:
Kudu and Oracle are both widely used database management systems in the industry, each offering distinct features and capabilities for handling data.
Data Storage: Kudu is a columnar storage engine while Oracle uses row-based storage. This fundamental difference impacts data organization, retrieval speeds, and analytical processing capabilities.
Schema Flexibility: Kudu supports schema evolution, allowing users to modify the structure of their tables without any downtime. On the other hand, Oracle requires a more rigid approach to schema changes, often necessitating complex migration processes.
Data Consistency: Oracle emphasizes strong data consistency, ensuring that transactions follow the ACID properties to maintain integrity. In contrast, Kudu adopts a more relaxed consistency model to prioritize high performance and scalability.
Partitioning Strategy: Kudu employs range partitioning by default, dividing data into ranges based on a specified column. Oracle offers various partitioning strategies including range, hash, and list partitioning, providing more flexibility in data distribution.
Indexing Techniques: Oracle supports a wide range of indexing techniques such as B-trees, bitmap indexes, and function-based indexes, catering to diverse query optimization needs. Kudu primarily relies on hash- and range-partitioned indexes for efficient data retrieval.
Usage Scenarios: Kudu is well-suited for real-time analytics and nested data structures due to its optimized storage format and processing capabilities. Oracle, on the other hand, is preferred for traditional OLTP workloads, complex transactions, and enterprise applications demanding high levels of data integrity and security.
In Summary, Kudu and Oracle differ in data storage, schema flexibility, data consistency, partitioning strategy, indexing techniques, and usage scenarios, making them suitable for distinct use cases in the database management domain.
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 Apache Kudu
- Realtime Analytics10
Pros of Oracle
- Reliable44
- Enterprise33
- High Availability15
- Expensive5
- Hard to maintain5
- Maintainable4
- Hard to use4
- High complexity3
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Cons of Apache Kudu
- Restart time1
Cons of Oracle
- Expensive14
