What is the primary storage model used by Apache HBase?
- Column-family Store
- Document Store
- Key-value Store
- Relational Store
Apache HBase utilizes a column-family store as its primary storage model. Data is organized into column families, which consist of columns containing related data. This design allows for efficient storage and retrieval of large amounts of sparse data.
What advanced technique is used in Hadoop clusters to optimize data locality during processing?
- Data Compression
- Data Encryption
- Data Locality Optimization
- Data Shuffling
Hadoop clusters use the advanced technique of Data Locality Optimization to enhance performance during data processing. This technique ensures that computation is performed on the node where the data resides, minimizing data transfer across the network and improving overall efficiency.
For a large-scale Hadoop cluster, how would you optimize HDFS for both storage efficiency and data processing speed?
- Enable Compression
- Implement Data Tiering
- Increase Block Size
- Use Short-Circuit Reads
Optimizing HDFS for both storage efficiency and data processing speed involves implementing data tiering. This strategy involves segregating data based on access patterns and placing frequently accessed data on faster storage tiers, enhancing performance without compromising storage efficiency.
In monitoring Hadoop clusters, ____ plays a critical role in ensuring data replication and consistency.
- Block Scanner
- Checkpoint Node
- HDFS Balancer
- Secondary NameNode
The HDFS Balancer is a crucial component in monitoring Hadoop clusters. It ensures data replication and consistency by redistributing data blocks across the nodes to maintain a balanced storage load. This helps prevent data skew and ensures optimal performance in the cluster.
When dealing with a large dataset containing diverse data types, how should a MapReduce job be structured for optimal performance?
- Custom InputFormat
- Data Serialization
- Multiple MapReduce Jobs
- SequenceFile Input
Structuring a MapReduce job for optimal performance with diverse data types involves using appropriate Data Serialization techniques. This ensures efficient data transfer between Map and Reduce tasks, especially when dealing with varied data formats and structures.
Which component of HDFS is responsible for data replication and storage?
- DataNode
- JobTracker
- NameNode
- ResourceManager
The component of HDFS responsible for data replication and storage is DataNode. DataNodes are responsible for storing and managing the actual data blocks and replicating them to ensure fault tolerance.
Custom implementations in MapReduce often involve overriding the ____ method for tailored data processing.
- combine()
- map()
- partition()
- reduce()
Custom implementations in MapReduce often involve overriding the map() method for tailored data processing. The map() method defines how input data is transformed into intermediate key-value pairs, a crucial step in the MapReduce process.
In Hadoop, ____ is a technique used to optimize data transformation by processing only relevant data.
- Data Filtering
- Data Pruning
- Data Sampling
- Data Skewing
Data Pruning is a technique in Hadoop used to optimize data transformation by processing only relevant data. It involves eliminating unnecessary data early in the processing pipeline, reducing the amount of data that needs to be processed and improving overall job performance.
The ____ architecture in Hadoop is designed to avoid a single point of failure in the filesystem.
- Fault Tolerant
- High Availability
- Redundant
- Scalable
The High Availability architecture in Hadoop is designed to avoid a single point of failure in the filesystem. It ensures that critical components like the NameNode have redundancy and failover mechanisms in place to maintain continuous operation even if a node fails.
In advanced Hadoop data pipelines, ____ is used for efficient data serialization and storage.
- Avro
- JSON
- XML
- YAML
In advanced Hadoop data pipelines, Avro is used for efficient data serialization and storage. Avro is a binary serialization format that provides a compact and fast way to serialize data, making it suitable for Hadoop applications where efficiency is crucial.