Microsoft Design and Implement Big Data Analytics Solutions (70-475日本語版) - 70-475日本語무료 덤프문제 풀어보기

Explanation

Explanation

Perform these operations in the following order:
* Create a data factory.
* Create linked services.
* Create datasets.
* Create a pipeline.
Step 1: New-AzureRMDataFactory
Create a data factory
The New-AzureRmDataFactory cmdlet creates a data factory with the specified resource group name and location.
Step 2: New-AzureRMDataFactoryLinkedService
Create linked services in a data factory to link your data stores and compute services to the data factory.
The New-AzureRmDataFactoryLinkedService cmdlet links a data store or a cloud service to Azure Data Factory.
Step 3: New-AzureRMDataFactoryDataset
You define a dataset that represents the data to copy from a source to a sink. It refers to the Azure Storage linked service you created in the previous step.
The New-AzureRmDataFactoryDataset cmdlet creates a dataset in Azure Data Factory.
Step 4: New-AzureRMDataFactoryPipeline
You create a pipeline.
The New-AzureRmDataFactoryPipeline cmdlet creates a pipeline in Azure Data Factory.
References:
https://docs.microsoft.com/en-us/azure/data-factory/quickstart-create-data-factory-powershell
https://docs.microsoft.com/en-us/powershell/module/azurerm.datafactories/new-azurermdatafactory

Explanation

Box 1: Azure SQL Data Warehourse
Scenario: Relecloud plans to implement a data warehouse named DB2.
Box 2: Clustered Columnstore index
Columnstore index is a new type of index introduced in SQL Server 2012. It is a column-based non-clustered index geared toward increasing query performance for workloads that involve large amounts of data, typically found in data warehouse fact tables.
A clustered columnstore index is the physical storage for the entire table.
Scenario:
Relecloud identifies the following requirements for DB2:
DB2 must be able to store more than 40 TB of data.
References: https://docs.microsoft.com/en-us/sql/relational-databases/indexes/columnstore-indexes-overview

설명: (Fast2test 회원만 볼 수 있음)

Explanation

Lambda architecture is a data-processing architecture designed to handle massive quantities of data by taking advantage of both batch- and stream-processing methods. This approach to architecture attempts to balance latency, throughput, and fault-tolerance by using batch processing to provide comprehensive and accurate views of batch data, while simultaneously using real-time stream processing to provide views of online data.
The two view outputs may be joined before presentation
Box 1: Speed
The speed layer processes data streams in real time and without the requirements of fix-ups or completeness.
This layer sacrifices throughput as it aims to minimize latency by providing real-time views into the most recent data.
Box 2: Batch
The batch layer precomputes results using a distributed processing system that can handle very large quantities of data. The batch layer aims at perfect accuracy by being able to process all available data when generating views.
Box 3: Serving
Output from the batch and speed layers are stored in the serving layer, which responds to ad-hoc queries by returning precomputed views or building views from the processed data.