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NEW QUESTION: 1
Flowlogistic Case Study
Company Overview
Flowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping.
Company Background
The company started as a regional trucking company, and then expanded into other logistics market. Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources.
Solution Concept
Flowlogistic wants to implement two concepts using the cloud:
* Use their proprietary technology in a real-time inventory-tracking system that indicates the location of their loads
* Perform analytics on all their orders and shipment logs, which contain both structured and unstructured data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed.
Existing Technical Environment
Flowlogistic architecture resides in a single data center:
* Databases
* 8 physical servers in 2 clusters
* SQL Server - user data, inventory, static data
* 3 physical servers
* Cassandra - metadata, tracking messages
10 Kafka servers - tracking message aggregation and batch insert
* Application servers - customer front end, middleware for order/customs
* 60 virtual machines across 20 physical servers
* Tomcat - Java services
* Nginx - static content
* Batch servers
Storage appliances
* iSCSI for virtual machine (VM) hosts
* Fibre Channel storage area network (FC SAN) - SQL server storage
* Network-attached storage (NAS) image storage, logs, backups
* 10 Apache Hadoop /Spark servers
* Core Data Lake
* Data analysis workloads
* 20 miscellaneous servers
* Jenkins, monitoring, bastion hosts,
Business Requirements
* Build a reliable and reproducible environment with scaled panty of production.
* Aggregate data in a centralized Data Lake for analysis
* Use historical data to perform predictive analytics on future shipments
* Accurately track every shipment worldwide using proprietary technology
* Improve business agility and speed of innovation through rapid provisioning of new resources
* Analyze and optimize architecture for performance in the cloud
* Migrate fully to the cloud if all other requirements are met
Technical Requirements
* Handle both streaming and batch data
* Migrate existing Hadoop workloads
* Ensure architecture is scalable and elastic to meet the changing demands of the company.
* Use managed services whenever possible
* Encrypt data flight and at rest
* Connect a VPN between the production data center and cloud environment SEO Statement We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around.
We need to organize our information so we can more easily understand where our customers are and what they are shipping.
CTO Statement
IT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO' s tracking technology.
CFO Statement
Part of our competitive advantage is that we penalize ourselves for late shipments and deliveries. Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability. Additionally, I don't want to commit capital to building out a server environment.
Flowlogistic wants to use Google BigQuery as their primary analysis system, but they still have Apache Hadoop and Spark workloads that they cannot move to BigQuery. Flowlogistic does not know how to store the data that is common to both workloads. What should they do?
A. Store the common data in BigQuery as partitioned tables.
B. Store the common data encoded as Avro in Google Cloud Storage.
C. Store he common data in the HDFS storage for a Google Cloud Dataproc cluster.
D. Store the common data in BigQuery and expose authorized views.
Answer: D
NEW QUESTION: 2
sap.m.SplitAppMode列挙によって提供されるモードはどれですか？
A. StretchCompressMode
B. ShowHideMode
C. PopoverMode
D. ShrinkCozyMode
E. HideShowMode
Answer: A,B,C
NEW QUESTION: 3
배치 프로세서로 사용되는 EC2 인스턴스간에 메시지 큐를 설정하려면 Simple Queue Service (SQS)를 사용하는 배치 처리 솔루션의 아키텍처 다이어그램을 참조하십시오. Cloud Watch는 작업 요청 수 (큐 메시지)와 Auto Scaling 그룹을 모니터링합니다. Cloud Watch 경보에 설정된 매개 변수를 기반으로 배치 서버를 자동으로 추가하거나 삭제합니다.
이 아키텍처를 사용하여 다음 중 어떤 기능을 비용 효율적이고 효율적으로 구현할 수 있습니까?
A. 작업 요청 수가 많은 EC2 인스턴스 수를 자동으로 조정하여 비용 효율성을 향상시킵니다.
B. SQS를 통해 메시지를 교환하여 배치 내 EC2 인스턴스 간 메시지 전달을 구현합니다.
C. 메시지를 수신하는 사용중인 EC2 인스턴스가 데이지 체인 설정에서 다음 인스턴스로 메시지를 전달할 수 있도록함으로써 병렬 처리를 통해 작업을 실행하기 위한 전반적인 석회를 줄입니다.
D. 메시지가 SQS에 남아 있고 마모 될 수 있으므로 EC2 인스턴스 오류에 대한 내결함성을 구현하고 메시지를 S3에 백업하여 SQS 오류에 대한 내결함성을 구현합니다.
E. 우선 순위 메타 데이터 필드를 SQS 메시지에 지정하여 우선 순위가 낮은 작업보다 우선 순위가 높은 작업을 처리하십시오.
Answer: A
Explanation:
Explanation
There are cases where a large number of batch jobs may need processing, and where the jobs may need to be re-prioritized.
For example, one such case is one where there are differences between different levels of services for unpaid users versus subscriber users (such as the time until publication) in services enabling, for example, presentation files to be uploaded for publication from a web browser. When the user uploads a presentation file, the conversion processes, for example, for publication are performed as batch processes on the system side, and the file is published after the conversion. Is it then necessary to be able to assign the level of priority to the batch processes for each type of subscriber?
Explanation of the Cloud Solution/Pattern
A queue is used in controlling batch jobs. The queue need only be provided with priority numbers. Job requests are controlled by the queue, and the job requests in the queue are processed by a batch server. In Cloud computing, a highly reliable queue is provided as a service, which you can use to structure a highly reliable batch system with ease. You may prepare multiple queues depending on priority levels, with job requests put into the queues depending on their priority levels, to apply prioritization to batch processes. The performance (number) of batch servers corresponding to a queue must be in accordance with the priority level thereof.
Implementation
In AWS, the queue service is the Simple Queue Service (SQS). Multiple SQS queues may be prepared to prepare queues for individual priority levels (with a priority queue and a secondary queue). Moreover, you may also use the message Delayed Send function to delay process execution.
Use SQS to prepare multiple queues for the individual priority levels.
Place those processes to be executed immediately (job requests) in the high priority queue.
Prepare numbers of batch servers, for processing the job requests of the queues, depending on the priority levels.
Queues have a message "Delayed Send" function. You can use this to delay the time for starting a process.
Configuration
Benefits
You can increase or decrease the number of servers for processing jobs to change automatically the processing speeds of the priority queues and secondary queues.
You can handle performance and service requirements through merely increasing or decreasing the number of EC2 instances used in job processing.
Even if an EC2 were to fail, the messages (jobs) would remain in the queue service, enabling processing to be continued immediately upon recovery of the EC2 instance, producing a system that is robust to failure.
Cautions
Depending on the balance between the number of EC2 instances for performing the processes and the number of messages that are queued, there may be cases where processing in the secondary queue may be completed first, so you need to monitor the processing speeds in the primary queue and the secondary queue.