Realm.io

Website

realm.io

$ Details

Categories

#Data Integration #Databases #NoSQL Databases #NoSQL Database As A Service

Apache Cassandra

Website

cassandra.apache.org

$ Details

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Categories

#Databases #NoSQL Databases #Relational Databases #Big Data

Realm.io features and specs

• Easy Integration
  Realm is designed to be easy to set up and integrate into existing projects, with straightforward APIs and comprehensive documentation.
• Performance
  Realm provides high performance with minimal overhead. It's faster than many traditional databases for many use cases, especially with large datasets and complex queries.
• Cross-Platform Support
  Realm supports multiple platforms including iOS, Android, and React Native, allowing for easy cross-platform development.
• Real-Time Data Sync
  Realm offers real-time synchronization of data between devices and a server, ensuring consistency and enabling collaborative features.
• Rich Data Types
  Realm supports complex data types such as lists and objects, making it more flexible for various types of applications.

Possible disadvantages of Realm.io

• Learning Curve
  Despite extensive documentation, there can be a learning curve for developers new to Realm, particularly if they are accustomed to traditional SQL databases.
• Storage Size
  Realm databases can become large quickly, especially if not properly managed, potentially impacting app performance and storage costs.
• Limited Query Language
  While powerful, Realm's query language isn't as mature or feature-rich as SQL, which might limit some advanced querying needs.
• Tooling
  The tooling ecosystem for Realm is not as extensive as those for more established databases like SQLite or MongoDB, which could impact developer productivity.
• Vendor Lock-In
  Using Realm might lead to vendor lock-in, as migrating away from it to another database system can be complex and time-consuming.

Apache Cassandra features and specs

• Scalability
  Apache Cassandra is designed for linear scalability and can handle large volumes of data across many commodity servers without a single point of failure.
• High Availability
  Cassandra ensures high availability by replicating data across multiple nodes. Even if some nodes fail, the system remains operational.
• Performance
  It provides fast writes and reads by using a peer-to-peer architecture, making it highly suitable for applications requiring quick data access.
• Flexible Data Model
  Cassandra supports a flexible schema, allowing users to add new columns to a table at any time, making it adaptable for various use cases.
• Geographical Distribution
  Data can be distributed across multiple data centers, ensuring low-latency access for geographically distributed users.
• No Single Point of Failure
  Its decentralized nature ensures there is no single point of failure, which enhances resilience and fault-tolerance.

Possible disadvantages of Apache Cassandra

• Complexity
  Managing and configuring Cassandra can be complex, requiring specialized knowledge and skills for optimal performance.
• Eventual Consistency
  Cassandra follows an eventual consistency model, meaning that there might be a delay before all nodes have the latest data, which may not be suitable for all use cases.
• Write-heavy Operations
  Although Cassandra handles writes efficiently, write-heavy workloads can lead to compaction issues and increased read latency.
• Limited Query Capabilities
  Cassandra's query capabilities are relatively limited compared to traditional RDBMS, lacking support for complex joins and aggregations.
• Maintenance Overhead
  Regular maintenance tasks such as node repair and compaction are necessary to ensure optimal performance, adding to the administrative overhead.
• Tooling and Ecosystem
  While the ecosystem for Cassandra is growing, it is still not as extensive or mature as those for some other database technologies.

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Course Intro | DS101: Introduction to Apache Cassandra™

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