____ is the process in HBase that involves combining smaller files into larger ones for efficiency.
- Aggregation
- Compaction
- Consolidation
- Merge
Compaction is the process in HBase that involves combining smaller files into larger ones for efficiency. It helps in reducing the number of files and improving read and write performance in HBase.
How does Apache Storm, in the context of real-time processing, integrate with the Hadoop ecosystem?
- It has no integration with Hadoop
- It only works with Hadoop MapReduce
- It replaces Hadoop for real-time processing
- It runs on Hadoop YARN
Apache Storm integrates with the Hadoop ecosystem by running on Hadoop YARN. YARN (Yet Another Resource Negotiator) allows Storm to utilize Hadoop's resource management capabilities, making it easier to deploy and manage real-time processing applications alongside batch processing in a Hadoop cluster.
In Hadoop, what tool is commonly used for importing data from relational databases into HDFS?
- Flume
- Hive
- Pig
- Sqoop
Sqoop is commonly used in Hadoop for importing data from relational databases into HDFS. It provides a command-line interface and supports the transfer of data between Hadoop and relational databases like MySQL, Oracle, and others.
What is the role of UDF (User Defined Functions) in Apache Pig?
- Data Analysis
- Data Loading
- Data Storage
- Data Transformation
UDFs (User Defined Functions) in Apache Pig play a crucial role in data transformation. They allow users to define their custom functions to process and transform data within Pig scripts, providing flexibility and extensibility in data processing operations.
When planning for disaster recovery, how should a Hadoop administrator prioritize data in different HDFS directories?
- Prioritize based on access frequency
- Prioritize based on creation date
- Prioritize based on file size
- Prioritize based on replication factor
A Hadoop administrator should prioritize data in different HDFS directories based on the replication factor. Critical data should have a higher replication factor to ensure availability and fault tolerance in the event of node failures.
____ is a highly efficient file format in Hadoop designed for fast data serialization and deserialization.
- Avro
- ORC
- Parquet
- SequenceFile
Parquet is a highly efficient file format in Hadoop designed for fast data serialization and deserialization. It is columnar-oriented, supports schema evolution, and is optimized for both compression and performance.
The ____ module in Python is often used for Hadoop integration to perform HDFS operations.
- hdfs
- os
- pandas
- pydoop
The pydoop module in Python is commonly used for Hadoop integration. It provides functionalities to perform operations on the Hadoop Distributed File System (HDFS) and interact with Hadoop clusters using Python.
How can counters be used in Hadoop for debugging MapReduce jobs?
- Analyze Input Data
- Monitor Task Progress
- Record Job History
- Track Performance Metrics
Counters in Hadoop are used to monitor task progress. They provide valuable information about the execution of MapReduce jobs, helping developers identify bottlenecks, track the number of records processed, and troubleshoot performance issues during debugging.
What is the key advantage of using Crunch for data processing in a Hadoop environment?
- Complex Configuration
- High-Level Abstractions
- Limited Scalability
- Low-Level APIs
The key advantage of using Crunch for data processing in a Hadoop environment is its provision of high-level abstractions. Crunch simplifies the development process by providing a higher-level API, making it easier for developers to express complex data processing tasks concisely.
Which feature of Avro makes it particularly suitable for schema evolution in Hadoop?
- Schema Evolution
- Schema Inversion
- Schema Rigidity
- Schema Validation
Avro is suitable for schema evolution due to its support for schema evolution. It allows for the addition of new fields and the evolution of existing ones without requiring modifications to the existing data. This flexibility is crucial in evolving data structures in a Hadoop environment.