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Spark Application Coexisting w/ NOSQL Databases - Gokul Prabagaren & Nagesh Kumar Vinnakota
Designing schema for NoSQL databases requires careful planning. Learn how Spark applications can coexist with MongoDB and Cassandra to improve query performance and scalability in this talk.
- Designing schema for NoSQL databases requires a deep understanding of the data and its relationships.
- Distributed NoSQL databases like Cassandra and MongoDB require careful planning and optimization to achieve optimal performance.
- Incorrect partitioning can lead to slow query performance and data inconsistencies.
- Using the right partitioner is crucial for efficient data storage and retrieval in distributed NoSQL databases.
- Pushdown to the server can improve query performance and reduce data transfer over the network.
- Incorrectly modeling data can lead to unwanted results and anti-patterns in NoSQL databases.
- MongoDB is suitable for use cases that require flexible schema and high scalability, while Cassandra is suitable for use cases that require low latency and high availability.
- Spark applications can benefit from the use of NoSQL databases like MongoDB and Cassandra to improve query performance and scalability.
- Co-locating Spark executors with data storage can improve query performance and reduce data transfer over the network.
- The right partitioner can help to reduce the number of partition splits and improve query performance in distributed NoSQL databases.
- Schema design in NoSQL databases is critical to achieving optimal performance and scalability.
- Using the right data model and schema can help to improve query performance and reduce data inconsistencies in NoSQL databases.
- The use of Spark and NoSQL databases can improve the performance and scalability of big data applications.
- The right caching mechanism can help to improve query performance and reduce data inconsistencies in NoSQL databases.