Download Oracle Big Data 2016 Implementation Essentials.1z0-449.BrainDumps.2018-11-29.42q.tqb

The hdfs_stream script is the preprocessor for the Oracle Database external table created by Oracle SQL Connector for HDFS. References: https://docs.oracle.com/cd/E37231_01/doc.20/e36961/start.htm#BDCUG107

HDFS Transparent Encryption protects Hadoop data that’s at rest on disk. When the encryption is enabled for a cluster, data write and read operations on encrypted zones (HDFS directories) on the disk are automatically encrypted and decrypted. This process is “transparent” because it’s invisible to the application working with the data. The cluster where you want to use HDFS Transparent Encryption must have Kerberos enabled. Incorrect Answers:D: The cluster where you want to use HDFS Transparent Encryption must have Kerberos enabled.References: https://docs.oracle.com/en/cloud/paas/big-data-cloud/csbdi/using-hdfs-transparent-encryption.html#GUID-16649C5A-2C88-4E75-809A-BBF8DE250EA3

com.oracle.bigdata.overflow: Truncates string data. Values longer than 100 characters for the DESCRIPTION column are truncated.com.oracle.bigdata.overflow: Truncates string data. Values longer than 100 characters for the DESCRIPTION column are truncated.com.oracle.bigdata.erroropt: Replaces bad data. Errors in the data for  CUST_NUM or ORDER_NUM set the value to INVALID_NUM.References: https://docs.oracle.com/cd/E55905_01/doc.40/e55814/bigsql.htm#BIGUG76679

The LOAD function loads data from the file system. Syntax: LOAD 'data' [USING function] [AS schema];Terms: 'data'The name of the file or directory, in single quote References: https://pig.apache.org/docs/r0.11.1/basic.html#load

Using the Hive Extensions To enable the Oracle XQuery for Hadoop extensions, use the --auxpath and -i arguments when starting Hive:$ hive --auxpath $OXH_HOME/hive/lib -i $OXH_HOME/hive/init.sql The first time you use the extensions, verify that they are accessible. The following procedure creates a table named  SRC, loads one row into it, and calls the xml_query function. To verify that the extensions are accessible: 1. Log in to an Oracle Big Data Appliance server where you plan to work. 2. Create a text file named src.txt that contains one line:$ echo "XXX" > src.txt 3. Start the Hive command-line interface (CLI):$ hive --auxpath $OXH_HOME/hive/lib -i $OXH_HOME/hive/init.sql The init.sql file contains the CREATE TEMPORARY FUNCTION statements that declare the XML functions. 4. Create a simple table:hive> CREATE TABLE src(dummy STRING); The SRC table is needed only to fulfill a SELECT syntax requirement. It is like the DUAL table in Oracle Database, which is referenced in SELECT statements to test SQL functions. 5. Load data from src.txt into the table:hive> LOAD DATA LOCAL INPATH 'src.txt' OVERWRITE INTO TABLE src; 6. Query the table using Hive SELECT statements:hive> SELECT * FROM src; OK xxx 7. Call an Oracle XQuery for Hadoop function for Hive. This example calls the xml_query function to parse an XML string:hive> SELECT xml_query("x/y", "<x><y>123</y><z>456</z></x>") FROM src;      .      .      . ["123"] If the extensions are accessible, then the query returns ["123"], as shown in the example References: https://docs.oracle.com/cd/E53356_01/doc.30/e53067/oxh_hive.htm#BDCUG693

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

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