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NEW QUESTION: 1
You are a project manager who uses Project Online.
You build and publish a resource plan for your project. You check the resource demand in the Resource Availability screen.
You do not see the correct demand as defined in the resource plan.
You need to see the correct demand.
What should you do?
A. Ensure that the date range of the resource plan matches the date range of the project plan.
B. Ensure that Work Units is set to hours for the resource plan.
C. Ensure that the resource utilization is calculated from the resource plan.
D. Ensure that the resource's default Booking Type is set to Committed.
Answer: C
NEW QUESTION: 2
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. Hive LOAD DATA command
B. Sqoop import
C. HDFS command
D. Ingest with Flume agents
E. Ingest with Hadoop Streaming
F. Pig LOAD command
Answer: F
Explanation:
Explanation/Reference:
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
NEW QUESTION: 3
다음 정책을 IAM 그룹에 연결할 수 있습니다. 해당 그룹의 IAM 사용자는 콘솔을 사용하여 사용자 이름과 일치하는 AWS S3의 "홈 디렉토리"에 액세스 할 수 있습니다.
{
"버전": "2012-10-17",
"명명 서": [
{
"조치": [ "s3 : *"],
"효과": "허용",
"자원": [ "arn : aws : s3 ::: bucket-name"],
"조건": { "StringLike": { "s3 : prefix": [ "home / $ {aws : username} / *"]}}
},
{
"Action": [ "s3 : *"],
"효과": "허용",
"자원": [ "arn : aws : s3 ::: bucket-name / home / $ {aws : username} / *"]
}
]
}
A. 참
B. 거짓
Answer: B
NEW QUESTION: 4
Answer:
Explanation:
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