Apache Cassandra

Website

cassandra.apache.org

$ Details

-

Categories

#Databases #NoSQL Databases #Relational Databases #Big Data

Apache ORC

Website

orc.apache.org

$ Details

Categories

#Big Data #Data Management #Data Dashboard #Data Warehousing

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.

Apache ORC features and specs

• Efficient Compression
  ORC provides highly efficient compression, which reduces the storage footprint of data and enhances performance by decreasing I/O operations.
• Columnar Storage
  The columnar storage format significantly improves read performance by allowing for selective access to necessary columns while ignoring others.
• Predicate Pushdown
  ORC supports predicate pushdown, enabling the query engine to skip over non-relevant data, thus enhancing query performance.
• Type Richness
  ORC supports complex types (like structs and maps), making it suitable for diverse data storage and query needs.
• Schema Evolution
  It facilitates seamless schema evolution, allowing easier adjustments to the dataset over time without breaking existing queries.
• Built-in Indexes
  Indexes such as bloom filters and min/max values are built-in, accelerating query processing by enabling quicker data lookup.

Possible disadvantages of Apache ORC

• Complexity
  The intricacies of its features may introduce additional complexity in implementation and maintenance, potentially increasing the learning curve.
• Write Performance
  While ORC is optimized for read-heavy workloads, its write performance can be less efficient compared to other formats like Parquet.
• Compatibility
  ORC may not be as widely supported as other formats, limiting the choice of tools and environments that can leverage its full capabilities.
• Compression Overhead
  The process of compressing and decompressing data can introduce a computational overhead, affecting performance in some scenarios.

Analysis of Apache Cassandra

Course Intro | DS101: Introduction to Apache Cassandra™

More videos:

No Apache ORC videos yet. You could help us improve this page by suggesting one.

Add video