Describe the approach you would use to build a Hadoop data pipeline for real-time analytics from social media data streams.
- Apache Flink for ingestion, Apache Hadoop MapReduce for processing, and Apache Hive for storage
- Apache Flume for ingestion, Apache Spark Streaming for processing, and Apache Cassandra for storage
- Apache Kafka for ingestion, Apache Spark for processing, and Apache HBase for storage
- Apache Sqoop for ingestion, Apache Storm for processing, and Apache HDFS for storage
The approach for building a Hadoop data pipeline for real-time analytics from social media data streams involves using Apache Sqoop for ingestion, Apache Storm for processing real-time data, and Apache HDFS for storage. This combination ensures efficient data transfer, real-time processing, and scalable storage.
In Sqoop, the ____ connector is used for efficient data import/export between Hadoop and specific RDBMS.
- Direct Connect
- Generic JDBC
- Native
- Specialized
Sqoop uses the Generic JDBC connector for efficient data import/export between Hadoop and specific Relational Database Management Systems (RDBMS). It provides a generic interface to interact with various databases, making it versatile and widely applicable.
Sqoop's ____ feature allows incremental import of data from a database.
- Batch Processing
- Data Replication
- Incremental Load
- Parallel Execution
Sqoop's Incremental Load feature enables the incremental import of data from a database. This means that only the new or updated records since the last import will be transferred, reducing the amount of data processed and improving efficiency.
Which tool is commonly used for deploying a Hadoop cluster?
- Apache Ambari
- Apache Kafka
- Apache Spark
- Apache ZooKeeper
Apache Ambari is commonly used for deploying and managing Hadoop clusters. It provides a web-based interface for cluster provisioning, monitoring, and management, making it easier for administrators to set up and maintain Hadoop environments.
In a scenario with frequent schema modifications, why would Avro be preferred over other serialization frameworks?
- Binary Encoding
- Compression Efficiency
- Data Serialization
- Schema Evolution
Avro is preferred in scenarios with frequent schema modifications due to its support for schema evolution. Avro allows for the flexible addition and removal of fields, making it easier to handle changes in the data structure without breaking compatibility. This feature is crucial in dynamic environments where the schema evolves over time.
Using ____ in Hadoop development can significantly reduce the amount of data transferred between Map and Reduce phases.
- Compression
- Indexing
- Serialization
- Shuffling
Using compression in Hadoop development can significantly reduce the amount of data transferred between Map and Reduce phases. Compression techniques help minimize the data size, leading to faster data transfer and more efficient processing in Hadoop.
Which component of Apache Spark allows it to efficiently process streaming data?
- Spark GraphX
- Spark MLlib
- Spark SQL
- Spark Streaming
Spark Streaming is the component of Apache Spark that enables the efficient processing of streaming data. It provides a high-level API for stream processing, allowing real-time analysis of data streams in the Spark framework.
In MapReduce, the ____ phase is responsible for preparing the data for processing by the Mapper.
- Input
- Output
- Partition
- Shuffle
In MapReduce, the Input phase is responsible for preparing the data for processing by the Mapper. During this phase, input data is read and split into key-value pairs, which are then processed by the Mapper function.
Apache Oozie uses ____ to interact with the Hadoop job tracker and execute jobs.
- Hadoop Pipes
- MapReduce
- Oozie Actions
- Workflow Engine
Apache Oozie uses the Workflow Engine to interact with the Hadoop job tracker and execute jobs. The Workflow Engine coordinates the execution of actions and manages the workflow lifecycle, interacting with the underlying Hadoop ecosystem.
In a scenario where a Hadoop cluster experiences frequent node failures, what should the administrator focus on?
- Data Replication
- Hardware Health
- Job Scheduling
- Network Latency
The administrator should focus on data replication. By ensuring that data is replicated across nodes, the impact of node failures can be mitigated. This approach enhances fault tolerance, as the loss of data on a single node can be compensated by its replicated copies on other nodes in the cluster.