The process of ____ is crucial for transferring bulk data between Hadoop and external data sources.
- Deserialization
- ETL (Extract, Transform, Load)
- Serialization
- Shuffling
The process of ETL (Extract, Transform, Load) is crucial for transferring bulk data between Hadoop and external data sources. ETL involves extracting data from external sources, transforming it into a suitable format, and loading it into the Hadoop cluster for analysis.
Apache Pig scripts are primarily written in which language?
- Java
- Pig Latin
- Python
- SQL
Apache Pig scripts are primarily written in Pig Latin, a high-level scripting language designed for expressing data analysis programs in a concise and readable way. Pig Latin scripts are then translated into MapReduce jobs for execution on a Hadoop cluster.
In a scenario where Hadoop NameNode crashes, what recovery procedure is typically followed?
- Manually Reallocate Data Blocks
- Reboot the Entire Cluster
- Restart NameNode Service
- Restore from Secondary NameNode
In the event of a NameNode crash, the typical recovery procedure involves restoring from the Secondary NameNode. The Secondary NameNode contains a checkpoint of the metadata, allowing for a faster recovery compared to restarting the entire cluster.
Considering a high-availability requirement, what feature of YARN should be emphasized to maintain continuous operation?
- Application Master Backup
- NodeManager Redundancy
- Resource Localization
- ResourceManager Failover
The high-availability feature in YARN is achieved through ResourceManager Failover. This ensures continuous operation by having a standby ResourceManager ready to take over in case the primary ResourceManager fails, minimizing downtime and maintaining cluster availability.
The use of ____ in Apache Spark significantly enhances the speed of data transformations in a distributed environment.
- Caching
- DataFrames
- RDDs
- SparkSQL
The use of DataFrames in Apache Spark significantly enhances the speed of data transformations in a distributed environment. DataFrames provide a higher-level abstraction and optimization opportunities for Spark's Catalyst query engine, making it more efficient for processing large-scale data.
For a Hadoop-based ETL process, how would you select the appropriate file format and compression codec for optimized data transfer?
- Avro with LZO
- ORC with Gzip
- SequenceFile with Bzip2
- TextFile with Snappy
In a Hadoop-based ETL process, choosing ORC (Optimized Row Columnar) file format with Gzip compression is ideal for optimized data transfer. ORC provides efficient storage and Gzip offers a good balance between compression ratio and speed.
In a Hadoop cluster, the ____ tool is used for cluster resource management and job scheduling.
- HBase
- HDFS
- MapReduce
- YARN
In a Hadoop cluster, the YARN (Yet Another Resource Negotiator) tool is used for cluster resource management and job scheduling. YARN separates resource management and job scheduling functionalities in Hadoop, allowing for more efficient cluster utilization.
In a scenario involving time-series data storage, what HBase feature would be most beneficial?
- Bloom Filters
- Column Families
- Time-to-Live (TTL)
- Versioning
For time-series data storage, configuring HBase with Time-to-Live (TTL) can be advantageous. TTL allows you to automatically expire data after a specified period, which is useful for managing and cleaning up older time-series data, optimizing storage, and improving query performance.
To handle large-scale data processing, Hadoop clusters are often scaled ____.
- Horizontally
- Linearly
- Logarithmically
- Vertically
To handle large-scale data processing, Hadoop clusters are often scaled 'Horizontally.' This means adding more commodity hardware or nodes to the existing cluster, allowing it to distribute the workload and handle increased data processing demands.
When setting up a new Hadoop cluster in an enterprise, what is a key consideration for integrating Kerberos?
- Network Latency
- Secure Shell (SSH)
- Single Sign-On (SSO)
- Two-Factor Authentication (2FA)
A key consideration for integrating Kerberos in a Hadoop cluster is achieving Single Sign-On (SSO). Kerberos provides a centralized authentication system, allowing users to log in once and access various services without the need to re-enter credentials. This enhances security and simplifies user access management.