MCQs on Cassandra Ecosystem and Integration | Cassandra
Chapter 9 delves into the integration of Cassandra with various technologies in the data ecosystem. Learn how to work with drivers for languages like Java, Python, and Node.js, integrate Cassandra with Spark for analytics, use Kafka for real-time data ingestion, and deploy Cassandra with Kubernetes and Docker. Additionally, explore tools like Cassandra Reaper and Medusa for efficient management.
Working with Drivers (Java, Python, Node.js)
- Which driver is commonly used to interact with Cassandra in Java?
a) Cassandra Driver for Java
b) Java SQL Driver
c) JDBC Driver
d) Cassandra Connector for Java - In Python, which library is commonly used to connect to Cassandra?
a) PyCassa
b) cassandra-driver
c) pyspark
d) pyodbc - Which of the following is true about the Cassandra Node.js driver?
a) It allows connecting to Cassandra through SQL-like queries
b) It only supports reading data
c) It is used for connecting with NoSQL databases
d) It is used to manage Cassandra clusters - The Cassandra Java driver connects to Cassandra using which protocol?
a) HTTP
b) Thrift
c) CQL (Cassandra Query Language)
d) WebSocket - What is the function of the
Sessionobject in the Cassandra Java driver?
a) It manages the connection pool
b) It executes SQL queries
c) It defines the schema
d) It stores data
Integration with Spark for Analytics
- Apache Spark integrates with Cassandra using which connector?
a) Cassandra-Spark Connector
b) SparkSQL Connector
c) Hadoop-Spark Connector
d) Cassandra-Connector - Which of the following is a key benefit of integrating Cassandra with Spark?
a) Real-time data storage
b) Enhanced data processing and analytics
c) Data security encryption
d) Faster data replication - When using the Spark-Cassandra connector, what data format does Spark support for reading Cassandra data?
a) Parquet
b) JSON
c) CSV
d) All of the above - How does Spark use Cassandra in a big data pipeline?
a) For high-speed data ingestion
b) For storing large data sets
c) For complex analytical queries and aggregations
d) For managing distributed compute resources - What is the default mode of reading data from Cassandra into Spark?
a) Batch mode
b) Real-time streaming mode
c) Data warehouse mode
d) File-based mode
Using Kafka for Real-Time Ingestion
- Kafka is used with Cassandra to enable:
a) Batch data processing
b) Real-time data ingestion and streaming
c) Data warehousing
d) Data replication across clusters - The Cassandra Kafka Connector is used to:
a) Transfer data from Cassandra to Kafka
b) Perform data analytics on Kafka topics
c) Ingest real-time data into Cassandra from Kafka
d) Connect Kafka to Spark for analytics - Which of the following is NOT a benefit of using Kafka with Cassandra?
a) Real-time data ingestion
b) High throughput
c) Data replication across multiple nodes
d) Data compression - The integration of Kafka with Cassandra typically involves:
a) Storing Kafka logs in Cassandra
b) Using Kafka for stream processing and Cassandra for persistence
c) Transforming data before inserting into Kafka
d) Using Cassandra as a Kafka consumer - What is the primary role of Kafka in real-time data ingestion into Cassandra?
a) Data transformation
b) Data storage
c) Streamlining real-time data flow into Cassandra
d) Data backup
Cassandra with Kubernetes and Docker
- Which containerization platform can be used to deploy Cassandra in isolated environments?
a) Docker
b) Kubernetes
c) VirtualBox
d) Both a and b - When using Kubernetes with Cassandra, which component is responsible for managing Cassandra nodes?
a) StatefulSets
b) Pods
c) Deployments
d) ConfigMaps - What is the primary advantage of deploying Cassandra with Docker?
a) Automated backups
b) Portability and scalability
c) Data compression
d) Real-time analytics - In Cassandra, how does Kubernetes improve deployment and management?
a) By automating backup processes
b) By providing automatic scaling and management of nodes
c) By enabling high throughput
d) By simplifying data storage configurations - Which of the following is used to run Cassandra containers in a Docker environment?
a) docker-compose
b) kubectl
c) Docker Swarm
d) helm
Tools: Cassandra Reaper, Medusa, and More
- What is the primary function of Cassandra Reaper?
a) To manage Cassandra backups
b) To provide a monitoring dashboard
c) To handle repair and maintenance operations
d) To manage user access and roles - Medusa is a tool used for:
a) Real-time data streaming
b) Backup and restore operations in Cassandra
c) Query optimization
d) Cluster scaling - Which of the following best describes Cassandra Reaper?
a) A backup tool for Cassandra
b) A performance tuning tool
c) A tool for scheduling and automating repair operations
d) A tool for data visualization - Medusa supports which of the following features for Cassandra backups?
a) Incremental backups
b) Full snapshot backups
c) Cloud storage integration
d) All of the above - How does Cassandra Reaper contribute to cluster performance?
a) By providing backup solutions
b) By optimizing query execution
c) By repairing and optimizing Cassandra’s nodes automatically
d) By scaling the cluster for additional nodes
Advanced Integration Concepts
- Cassandra supports integration with which of the following analytics tools?
a) Apache Hive
b) Apache Spark
c) Apache Flink
d) All of the above - For high availability, Cassandra can be integrated with which of the following?
a) Redis
b) Zookeeper
c) Kubernetes
d) Both b and c - What is the benefit of integrating Cassandra with Docker?
a) Reduced latency
b) Simplified container management
c) Increased data redundancy
d) Better query performance - What does Kafka provide in a real-time data pipeline with Cassandra?
a) Storage management
b) Stream processing
c) Data replication
d) Data persistence - Which of the following is a best practice for managing Cassandra in a cloud environment?
a) Deploying on a single instance
b) Using auto-scaling with Kubernetes
c) Disabling backups
d) Ignoring monitoring and alerting
Answers Table
| QNo | Answer |
|---|---|
| 1 | a) Cassandra Driver for Java |
| 2 | b) cassandra-driver |
| 3 | c) It is used for connecting with NoSQL databases |
| 4 | b) Thrift |
| 5 | a) It manages the connection pool |
| 6 | a) Cassandra-Spark Connector |
| 7 | b) Enhanced data processing and analytics |
| 8 | d) All of the above |
| 9 | c) For complex analytical queries and aggregations |
| 10 | a) Batch mode |
| 11 | b) Real-time data ingestion and streaming |
| 12 | c) Ingest real-time data into Cassandra from Kafka |
| 13 | c) Data replication across clusters |
| 14 | b) Using Kafka for stream processing and Cassandra for persistence |
| 15 | c) Streamlining real-time data flow into Cassandra |
| 16 | d) Both a and b |
| 17 | a) StatefulSets |
| 18 | b) Portability and scalability |
| 19 | b) By providing automatic scaling and management of nodes |
| 20 | a) docker-compose |
| 21 | c) To handle repair and maintenance operations |
| 22 | b) Backup and restore operations in Cassandra |
| 23 | c) A tool for scheduling and automating repair operations |
| 24 | d) All of the above |
| 25 | c) By repairing and optimizing Cassandra’s nodes automatically |
| 26 | d) All of the above |
| 27 | d) Both b and c |
| 28 | b) Simplified container management |
| 29 | b) Stream processing |
| 30 | b) Using auto-scaling with Kubernetes |