Apache Cassandra

Website

cassandra.apache.org

Categories

#Databases #NoSQL Databases #Relational Databases #Big Data

Spark Framework

Website

sparkjava.com

Categories

#Web Frameworks #Developer Tools #PHP MVC Framework #Runtime

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.

Spark Framework features and specs

• Ease of Use
  Spark Framework provides a simple and intuitive API, making it easy to set up and run a web application with minimal configuration.
• Lightweight
  Spark is very lightweight, which makes it well-suited for small applications and microservices where resource consumption is a concern.
• Java 8 Lambda Support
  It supports Java 8 lambdas, allowing developers to write clean, readable, and more concise code.
• Rapid Development
  The framework facilitates rapid development and prototyping, enabling developers to quickly build and iterate on ideas.
• Minimal Configuration
  With less boilerplate code required, Spark allows developers to focus on business logic rather than intricate configurations.

Possible disadvantages of Spark Framework

• Limited Ecosystem
  Compared to more established frameworks, Spark has a smaller ecosystem of plugins and extensions, which might limit functionality for larger projects.
• Performance Overhead
  While suitable for small applications, the simplicity of Spark might introduce performance overhead when scaling up to larger, complex applications.
• Concurrency Limitations
  Its concurrency model may not be robust enough for high-concurrency applications, potentially leading to scalability issues.
• Less Community Support
  Spark's smaller user base means that community support and resources such as tutorials and forums are more limited compared to larger frameworks.
• Basic Feature Set
  The framework offers a basic feature set, which may require additional coding or third-party libraries to achieve advanced functionalities.

Course Intro | DS101: Introduction to Apache Cassandra™

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