Enhance your understanding of integrating Cassandra with Spark for real-time analytics. This quiz covers key concepts, key-value data handling, distributed processing, and practical scenarios in using Cassandra with Spark for fast, scalable analytics solutions.
Which feature of Cassandra makes it well-suited for real-time analytics with Spark?
Explanation: Horizontal scalability allows Cassandra to handle large volumes of data efficiently by adding more nodes as needed, matching well with distributed processing in Spark. Relational joins are not a strength of Cassandra, as it is not a relational store. Single-node storage would prevent real-time data processing at scale. Manual sharding is complex and not necessary with Cassandra's built-in sharding mechanisms.
When processing time-series data in real time, which data model is most commonly used in Cassandra for efficient Spark queries?
Explanation: Wide rows with compound primary keys allow efficient range queries and quick access to time-series segments in Cassandra, which benefits Spark's batch or streaming jobs. Flat tables lack structure for segmenting time-series data. Highly normalized tables are not ideal in Cassandra, as joins are costly. Random key-values make efficient queries hard due to lack of order.
What is the purpose of using Spark’s connector for Cassandra when performing analytics?
Explanation: The connector enables seamless data movement between Cassandra and Spark, allowing Spark jobs to read from and write to Cassandra tables. It does not merge relational databases; Cassandra is not relational. Running Spark code directly inside database nodes is inaccurate, as data transfer is handled through sessions. The connector does not specifically encrypt all traffic, though secure connections can be configured separately.
In a live sensor monitoring example, how does Spark Streaming interact with Cassandra for real-time dashboards?
Explanation: Spark Streaming can pull recent entries directly from Cassandra as data arrives, making real-time dashboards responsive. If updates happened only once an hour, the analytics would not be real time. Saving all results to local files is inefficient for analytics distributed across nodes. Processing only historical records ignores the real-time aspect required in live monitoring.
Why is it important to correctly design primary keys for Cassandra tables used in Spark analytics?
Explanation: A well-designed primary key ensures that data is partitioned optimally, allowing Spark to efficiently read, group, and analyze data. Complex joins are not native to Cassandra, so this is not the main reason. Storing binary files is not typical for analytics workflows. Secondary indexes have limitations in Cassandra and should not be relied upon for unlimited scaling.
How does Spark’s distributed architecture complement Cassandra in large-scale analytics?
Explanation: Spark's distributed processing reads from multiple Cassandra partitions in parallel, speeding up analytics tasks. Running all analytics on one server would lose the benefit of both tools. Manual data copying is unnecessary, as the connector handles distributed reads. Spark does not replace the underlying data model, but works alongside it.
What mechanism ensures resilience when executing analytics jobs using Spark over a Cassandra dataset?
Explanation: Cassandra’s data replication ensures that, if a node fails during analytics jobs, data is still available from other nodes. Manual backups provide recovery, but not real-time resilience. In-memory-only processing is fast but not inherently fault tolerant if a node is lost. Disabling sharding makes scaling difficult and can increase failure risk.
Why must schema changes in Cassandra be carefully managed when used alongside Spark analytics jobs?
Explanation: When Spark jobs expect a certain schema, changes in Cassandra (such as added or removed columns) may lead to errors or wrong analytics. Creating more tables does not automatically boost performance and can complicate processes. Schema changes do not delete all data by default. Spark can usually read changed tables if handled properly, but not if schemas are inconsistent.
Which advantage does Cassandra’s write-optimized architecture provide for Spark-based event logging?
Explanation: Cassandra’s write-optimized design lets Spark log events very quickly, which is valuable for real-time analytics. It does not generate visualizations automatically; these must be built separately. Duplicate data prevention depends on key design, not just on architecture. Data is not always sorted unless explicitly structured by the application.
How does eventual consistency in Cassandra affect analytics performed with Spark?
Explanation: Eventual consistency means some recent changes may take time to become visible, so Spark analytics may occasionally work with slightly stale data. Updates are not instantly seen by every node. Cassandra is not strictly synchronized, so locks are unnecessary for most analytics. Spark does not require table locks, which could affect performance and scalability.