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Interview Questions and Answers

Pig is a high-level data flow language and execution framework for parallel data processing on Hadoop. It provides a simplified programming model compared to MapReduce.

Spark is a fast and general-purpose distributed processing engine that can run on Hadoop. It provides in-memory data processing capabilities, making it significantly faster than MapReduce for iterative and interactive workloads.

Alternatives to Hadoop include: Apache Spark (for faster processing), Amazon EMR (managed Hadoop service), Google Cloud Dataproc (managed Hadoop service), and various cloud-based data warehousing solutions like Amazon Redshift and Google BigQuery.

The JobTracker was the central point for submitting and managing MapReduce jobs in Hadoop 1.x. It assigned tasks to TaskTrackers and monitored their progress. This role is largely replaced by the ResourceManager in YARN (Hadoop 2.x).

TaskTrackers were the worker nodes in Hadoop 1.x that executed the MapReduce tasks assigned by the JobTracker. They reported their status back to the JobTracker.

The ResourceManager is the central authority in YARN for managing cluster resources. It allocates resources to applications based on their requirements.

NodeManagers are the worker nodes in YARN that manage the resources (CPU, memory, disk, network) on individual machines. They launch and monitor containers on the nodes.

A Container represents a set of physical resources (CPU, memory, disk, network) allocated by the ResourceManager to run an applications tasks. Its a lightweight execution environment.

Hadoop is configured through XML configuration files located in the `$HADOOP_CONF_DIR` directory. Key configuration files include `core-site.xml`, `hdfs-site.xml`, `mapred-site.xml`, and `yarn-site.xml`.

Hive is a data warehousing system built on top of Hadoop. It provides an SQL-like interface (HiveQL) for querying and analyzing large datasets stored in HDFS.

Advantages of Hadoop include: Scalability (handles large datasets), Fault Tolerance (data replication), Cost-Effectiveness (commodity hardware), Flexibility (supports various data types), and Parallel Processing (faster data analysis).

Common use cases include: Log analysis, clickstream analysis, fraud detection, recommendation systems, sentiment analysis, and data warehousing.

Hadoop achieves fault tolerance through data replication in HDFS. By default, each data block is replicated three times across different nodes in the cluster. If a node fails, the data is still available from the other replicas.

The NameNode is the master node in HDFS. It stores the metadata about the file system, such as file names, directories, and block locations. It doesnt store the actual data blocks.

DataNodes are the worker nodes in HDFS. They store the actual data blocks and serve read/write requests from clients. They also report their status to the NameNode.

Hadoop is an open-source, distributed processing framework for managing large datasets across clusters of computers. It enables applications to work with thousands of nodes and petabytes of data.

The core components of Hadoop are Hadoop Distributed File System (HDFS) for storage and Yet Another Resource Negotiator (YARN) for resource management and job scheduling.

HDFS (Hadoop Distributed File System) is a distributed file system designed to store very large files across a cluster of commodity hardware. It provides high-throughput access to application data and is fault-tolerant.

YARN (Yet Another Resource Negotiator) is the resource management layer of Hadoop. It manages cluster resources and schedules applications, allowing multiple data processing engines like MapReduce, Spark, and Tez to run on the same Hadoop cluster.

MapReduce is a programming model and software framework for processing large datasets in parallel on a distributed computing environment. It breaks down processing into two phases: Map (data transformation) and Reduce (aggregation).

Managing traffic on streets.
- Content Management and Archiving Emails.
- Processing Brain Neuronal Signals using a Hadoop Computing Cluster.
- Fraud detection and Prevention.
- Advertisements Targeting Platforms.
- Managing content on soci

Standalone Mode. Pseudo-distributed Mode. Fully-Distributed Mode.

Hadoop is a platform used to store and handle big data using distributed storage and parallel processing. To manage big data, it is the most widely used programme. There are three Hadoop elements.
- Hadoop HDFS - Hadoop Distributed File System (HDFS) i

Block is the physical representation of data. It contains a minimum amount of data that can be read or write. InputSplit is the logical representation of data present in the block. InputSplit doesn't contain actual data, but a reference to the data.

RDBMS stores structured data while the Hadoop stores structured, semi-structured, and unstructured data.

The RDBMS is a database management system based on the relational model. The Hadoop is a software for storing data and running applications on

Multiple clients cannot write into an HDFS file at the same time. When one client is permitted by Name node to write data on the data node block, the block gets locked till the write operations are completed.

Volume, velocity, variety, veracity and value are the five keys to making big data a huge business.

- Add hadoop-mapreduce-client-jobclient maven dependency.
- Set local file system.
- Set Number of mappers and reducers.

Checkpoint Hadoop daemons: Daemons mean Process. Hadoop Daemons are a set of processes that run on Hadoop. Secondary NameNode keeps track of checkpoint in a Hadoop Distributed File System, it is also known as the checkpoint Node.

HDFS fault toleran

The main configuration parameters in “MapReduce” framework are:
- Input location of Jobs.
- Output location of Jobs.
- The input format of data.
- The output format of data.
- The class which contains the map function.
- The class whic

Configuration Files are the files that are located in the extracted tar.gz file in the etc/hadoop/ directory.
- HADOOP-ENV.sh
- CORE-SITE.XML
- HDFS-SITE.XML
- MAPRED-SITE.XML
- Masters
- Slave.

If there is only one DataNode in the cluster for a single node cluster operating in standalone or pseudo-distributed mode, so if the DataNode is down, you can operate, and you can't access data, since all files in HDFS are replicated as single files.

NA

Model–view–controller(MVC) is a software design pattern used for developing user interfaces that separate the related program logic into three interconnected elements. Each of these components is built to handle specific development aspects of an applicat

Explain specific instances with respect to the job JD.

Explain specific instances with respect to the job JD.

Use a phased life-cycle plan, Continuous validation, Maintain product control, Use the latest programming practices, Maintain clear accountability for results

The most common software sizing methodology has been counting the lines of code written in the application source. Another approach is to do Functional Size Measurement, to express the functionality size as a number by performing Function point analysis.

The major parts to project estimation are effort estimation, cost estimation, resource estimate. In estimation, there are many methods used as best practices in project management such as-Analogous estimation, Parametric estimation, Delphi process, 3 Poi

Quality control can be defined as a \"part of quality management concentrating on maintaining quality requirements.\" While quality assurance relates to how a process is carried out or how a product is produced, quality control is more the quality managem