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NEW QUESTION: 1
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution. Determine whether the solution meets stated goals.
Your company plans to use Microsoft Azure Resource Manager templates for all future deployments of SQL Server on Azure virtual machines.
You need to create the templates.
Solution: You use Visual Studio to create a JSON template that defines the deployment and configuration settings for the SQL Server environment.
Does the solution meet the goal?
A. Yes
B. No
Answer: A
Explanation:
Azure Resource Manager template consists of JSON, not XAML, and expressions that you can use to construct values for your deployment.
A good JSON editor can simplify the task of creating templates.
Note: In its simplest structure, an Azure Resource Manager template contains the following elements:
{
"$schema": "http://schema.management.azure.com/schemas/2015-01-01/deploymentTemplate.json#",
"contentVersion": "",
"parameters": { },
"variables": { },
"resources": [ ],
"outputs": { }
}
References:https://docs.microsoft.com/en-us/azure/azure-resource-manager/resource-group- authoring-templates
NEW QUESTION: 2
Which three features or functionalities does Cisco Unified Communications Manager provide for the Cisco Unified Contact Center Enterprise solution? (Choose three.)
A. Cisco Extension Mobility for agents
B. agent, supervisor, and team configuration
C. hunt groups and pickup groups for Cisco Unified Contact Center Enterprise
D. call routing from PSTN gateway to Cisco Unified IP IVR
E. call routing from PSTN gateway to agents
F. CTI data on Cisco Agent Desktop screen pop
Answer: A,D,E
NEW QUESTION: 3
You have user profile records in your OLPT database, that you want to join with web logs you have already ingested into the Hadoop file system. How will you obtain these user records?
A. Ingest with Hadoop Streaming
B. Pig LOAD command
C. Ingest with Flume agents
D. Sqoop import
E. Hive LOAD DATA command
F. HDFS command
Answer: B
Explanation:
Apache Hadoop and Pig provide excellent tools for extracting and analyzing data
from very large Web logs.
We use Pig scripts for sifting through the data and to extract useful information from the Web logs.
We load the log file into Pig using the LOAD command.
raw_logs = LOAD 'apacheLog.log' USING TextLoader AS (line:chararray);
Note 1:
Data Flow and Components
*Content will be created by multiple Web servers and logged in local hard discs. This content will then be pushed to HDFS using FLUME framework. FLUME has agents running on Web servers; these are machines that collect data intermediately using collectors and finally push that data to HDFS.
*Pig Scripts are scheduled to run using a job scheduler (could be cron or any sophisticated batch job solution). These scripts actually analyze the logs on various dimensions and extract the results. Results from Pig are by default inserted into HDFS, but we can use storage implementation for other repositories also such as HBase, MongoDB, etc. We have also tried the solution with HBase (please see the implementation section). Pig Scripts can either push this data to HDFS and then MR jobs will be required to read and push this data into HBase, or Pig scripts can push this data into HBase directly. In this article, we use scripts to push data onto HDFS, as we are showcasing the Pig framework applicability for log analysis at large scale.
*The database HBase will have the data processed by Pig scripts ready for reporting and further slicing and dicing.
*The data-access Web service is a REST-based service that eases the access and integrations with data clients. The client can be in any language to access REST-based API. These clients could be BI- or UI-based clients.
Note 2:
The Log Analysis Software Stack
*Hadoop is an open source framework that allows users to process very large data in parallel. It's based on the framework that supports Google search engine. The Hadoop core is mainly divided into two modules:
1.HDFS is the Hadoop Distributed File System. It allows you to store large amounts of data using
multiple commodity servers connected in a cluster.
2.Map-Reduce (MR) is a framework for parallel processing of large data sets. The default implementation is bonded with HDFS.
*The database can be a NoSQL database such as HBase. The advantage of a NoSQL database is that it provides scalability for the reporting module as well, as we can keep historical processed data for reporting purposes. HBase is an open source columnar DB or NoSQL DB, which uses HDFS. It can also use MR jobs to process data. It gives real-time, random read/write access to very large data sets -- HBase can save very large tables having million of rows. It's a distributed database and can also keep multiple versions of a single row.
*The Pig framework is an open source platform for analyzing large data sets and is implemented as a layered language over the Hadoop Map-Reduce framework. It is built to ease the work of developers who write code in the Map-Reduce format, since code in Map-Reduce format needs to be written in Java. In contrast, Pig enables users to write code in a scripting language.
*Flume is a distributed, reliable and available service for collecting, aggregating and moving a large amount of log data (src flume-wiki). It was built to push large logs into Hadoop-HDFS for further processing. It's a data flow solution, where there is an originator and destination for each node and is divided into Agent and Collector tiers for collecting logs and pushing them to destination storage.
Reference: Hadoop and Pig for Large-Scale Web Log Analysis
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