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Universal Containers (UC) is migrating data from legacy system to Salesforce. During data analysis it was discovered that data types of fields being migrated do not match with Salesforce data types. Which solution should a data architect use to ensure successful data migrations?
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Northern Trail Outfitters <NTO> is streaming IoT data from connected devices to a cloud database. Every 24 hours. 100,000 records are generated. NIO employees will need to see these lol records within Salesforce and generate weekly reports on it. Developers may also need to write programmatic logic to aggregate the records and incorporate them into workflows. Which data pattern will allow a data architect to satisfy these requirements, while also keeping limits in mind?
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Question 185 - Certified Data Architect discussion

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Universal Containers (CU) is in the process of implementing an enterprise data warehouse (EDW). UC needs to extract 100 million records from Salesforce for migration to the EDW.

What data extraction strategy should a data architect use for maximum performance?

A.
Install a third-party AppExchange tool.
Answers
A.
Install a third-party AppExchange tool.
B.
Call the REST API in successive queries.
Answers
B.
Call the REST API in successive queries.
C.
Utilize PK Chunking with the Bulk API.
Answers
C.
Utilize PK Chunking with the Bulk API.
D.
Use the Bulk API in parallel mode.
Answers
D.
Use the Bulk API in parallel mode.
Suggested answer: C

Explanation:

According to the Salesforce documentation2, extracting large amounts of data from Salesforce can be challenging and time-consuming, as it can encounter performance issues, API limits, timeouts, etc. To extract 100 million records from Salesforce for migration to an enterprise data warehouse (EDW), a data extraction strategy that can provide maximum performance is:

Utilize PK Chunking with the Bulk API (option C). This means using a feature that allows splitting a large query into smaller batches based on the record IDs (primary keys) of the queried object.This can improve performance and avoid timeouts by processing each batch asynchronously and in parallel using the Bulk API3.

Installing a third-party AppExchange tool (option A) is not a good solution, as it can incur additional costs and dependencies. It may also not be able to handle such a large volume of data efficiently. Calling the REST API in successive queries (option B) is also not a good solution, as it can encounter API limits and performance issues when querying such a large volume of data. Using the Bulk API in parallel mode (option D) is also not a good solution, as it can still cause timeouts and errors when querying such a large volume of data without chunking.

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asked 23/09/2024
Mekmek Kh
37 questions
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