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Given a shard, the Client Driver next consults the Shard State Table (SST). For each shard, the SST contains information about each replication node comprising the group (step 5). Based upon information in the SST, such as the identity of the master and the load on the various nodes in a shard, the Client Driver selects the node to which to send the request and forwards the request to the appropriate node. In this case, since we are issuing a write operation, the request must go to the master node. Note: If the machine hosting the master should fail in any way, then the master automatically fails over to one of the other nodes in the shard. That is, one of the replica nodes is automatically promoted to master. References: http://www.oracle.com/technetwork/products/nosqldb/learnmore/nosql-wp-1436762.pdf

Cloudera Impala is Cloudera's open source massively parallel processing (MPP) SQL query engine for data stored in a computer cluster running Apache Hadoop. Impala brings scalable parallel database technology to Hadoop, enabling users to issue low-latency SQL queries to data stored in HDFS and Apache HBase without requiring data movement or transformation. References: https://en.wikipedia.org/wiki/Cloudera_Impala

Replication Nodes are organized into shards. A shard contains a single Replication Node which is responsible for performing database writes, and which copies those writes to the other Replication Nodes in the shard. This is called the master node. All other Replication Nodes in the shard are used to service read-only operations. Note: Oracle NoSQL Database provides multi-terabyte distributed key/value pair storage that offers scalable throughput and performance. That is, it services network requests to store and retrieve data which is organized into key-value pairs.  References: https://docs.oracle.com/cd/E26161_02/html/GettingStartedGuide/introduction.html

The CREATE TABLE command creates a table with the given name. Load files into existing tables with the LOAD DATA command. References: https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DML#LanguageManualDML-InsertingdataintoHiveTablesfromquerieshttps://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL

The number of nodes belonging to a shard is called its Replication Factor. The larger a shard's Replication Factor, the faster its read throughput (because there are more machines to service the read requests) but the slower its write performance (because there are more machines to which writes must be copied). Note: Replication Nodes are organized into shards. A shard contains a single Replication Node which is responsible for performing database writes, and which copies those writes to the other Replication Nodes in the shard. This is called the master node. All other Replication Nodes in the shard are used to service read-only operations. References: https://docs.oracle.com/cd/E26161_02/html/GettingStartedGuide/introduction.html#replicationfactor

Controlling the amount of parallelism that Sqoop will use to transfer data is the main way to control the load on your database. Using more mappers will lead to a higher number of concurrent data transfer tasks, which can result in faster job completion. However, it will also increase the load on the database as Sqoop will execute more concurrent queries. References: https://community.hortonworks.com/articles/70258/sqoop-performance-tuning.html

This configuration defines a single agent named a1. a1 has a source that listens for data on port 44444, a channel that buffers event data in memory, and a sink that logs event data to the console. Note: A sink stores the data into centralized stores like HBase and HDFS. It consumes the data (events) from the channels and delivers it to the destination. The destination of the sink might be another agent or the central stores. A source is the component of an Agent which receives data from the data generators and transfers it to one or more channels in the form of Flume events. Incorrect Answers:D: port 4444 is part of the source, not the sink.References: https://flume.apache.org/FlumeUserGuide.html

Hadoop was designed for batch processing. That means, take a large dataset in input all at once, process it, and write a large output. The very concept of MapReduce is geared towards batch and not real-time. With growing data, Hadoop enables you to horizontally scale your cluster by adding commodity nodes and thus keep up with query. In hadoop Map-reduce does the same job it will take large amount of data and process it in batch. It will not give immediate output. It will take time as per Configuration of system,namenode,task-cker,job-tracker etc. References: https://www.quora.com/What-is-batch-processing-in-hadoop

Kerberos integrates with LDAP servers – allowing the principals and encryption keys to be stored in the common repository. The complication with Kerberos authentication is that your organization needs to have a Kerberos KDC (Key Distribution Center) server setup already, which will then link to your corporate LDAP or Active Directory service to check user credentials when they request a Kerberos ticket. References: https://www.rittmanmead.com/blog/2015/04/setting-up-security-and-access-control-on-a-big-data-appliance/

Hadoop was designed for batch processing. That means, take a large dataset in input all at once, process it, and write a large output. The very concept of MapReduce is geared towards batch and not real-time. With growing data, Hadoop enables you to horizontally scale your cluster by adding commodity nodes and thus keep up with query. In hadoop Map-reduce does the same job it will take large amount of data and process it in batch. It will not give immediate output. It will take time as per Configuration of system,namenode,task-tracker,job-tracker etc. Incorrect Answers:A: Yahoo! has by far the most number of nodes in its massive Hadoop clusters at over 42,000 nodes as of July 2011.  C: Hadoop supports distributed computing of large data sets on large clustersE: Hadoop processes data in parallel.References: https://www.quora.com/What-is-batch-processing-in-hadoop