In Hadoop, ____ plays a critical role in scheduling and coordinating workflow execution in data pipelines.
- HDFS
- Hive
- MapReduce
- YARN
In Hadoop, YARN (Yet Another Resource Negotiator) plays a critical role in scheduling and coordinating workflow execution in data pipelines. YARN manages resources efficiently, enabling multiple applications to share and utilize resources on a Hadoop cluster.
In Apache Oozie, ____ actions allow conditional control flow in workflows.
- Decision
- Fork
- Hive
- Pig
In Apache Oozie, Decision actions allow conditional control flow in workflows. They enable the workflow to take different paths based on the outcome of a condition, providing flexibility in designing complex workflows.
Which component acts as the master in a Hadoop cluster?
- DataNode
- NameNode
- ResourceManager
- TaskTracker
In a Hadoop cluster, the NameNode acts as the master. It manages the metadata and keeps track of the location of data blocks in the Hadoop Distributed File System (HDFS). The NameNode is a critical component for ensuring data integrity and availability.
Considering a use case with high query performance requirements, how would you leverage Avro and Parquet together in a Hadoop environment?
- Convert data between Avro and Parquet for each query
- Use Avro for storage and Parquet for querying
- Use Parquet for storage and Avro for querying
- Use either Avro or Parquet, as they offer similar query performance
You would leverage Avro for storage and Parquet for querying in a Hadoop environment with high query performance requirements. Avro's fast serialization is suitable for write-heavy workloads, while Parquet's columnar storage format enhances query performance by reading only the required columns.
____ is a recommended practice in Hadoop for efficient memory management.
- Garbage Collection
- Heap Optimization
- Memory Allocation
- Memory Segmentation
Garbage Collection is a recommended practice in Hadoop for efficient memory management. It involves automatic memory management by identifying and freeing up memory occupied by objects that are no longer in use. Proper garbage collection enhances the performance of Hadoop applications.
In a scenario where a data scientist prefers Python for Hadoop analytics, which library would be the most suitable for complex data processing tasks?
- Hadoop Streaming
- NumPy
- Pandas
- PySpark
For complex data processing tasks in a Hadoop environment using Python, PySpark is the most suitable library. PySpark provides a Python API for Apache Spark, allowing data scientists to leverage the power of Spark for distributed and parallel processing of large datasets.
____ is an essential factor in determining the choice between batch and real-time processing in Hadoop applications.
- Data Variety
- Data Velocity
- Data Veracity
- Data Volume
Data Velocity is an essential factor in determining the choice between batch and real-time processing in Hadoop applications. It refers to the speed at which data is generated and the need for processing it in real-time or in batches based on the application requirements.
How does Apache Pig handle schema design in data processing?
- Dynamic Schema
- Explicit Schema
- Implicit Schema
- Static Schema
Apache Pig uses a dynamic schema approach in data processing. This means that Pig doesn't enforce a rigid schema on the data; instead, it adapts to the structure of the data at runtime. This flexibility allows Pig to handle semi-structured or unstructured data effectively.
In the context of Big Data, which 'V' refers to the trustworthiness and reliability of data?
- Variety
- Velocity
- Veracity
- Volume
The 'V' that refers to the trustworthiness and reliability of data in the context of Big Data is Veracity. It emphasizes the quality and accuracy of the data, ensuring that the information is reliable and trustworthy for making informed decisions.
In a scenario involving seasonal spikes in data processing demand, how should a Hadoop cluster's capacity be planned to maintain performance?
- Auto-Scaling
- Over-Provisioning
- Static Scaling
- Under-Provisioning
In a scenario with seasonal spikes, auto-scaling is crucial in capacity planning. Auto-scaling allows the cluster to dynamically adjust resources based on demand, ensuring optimal performance during peak periods without unnecessary over-provisioning during off-peak times.