A company is storing millions of sales transaction records in Amazon Redshift. A data analyst must perform an analysis on sales data. The analysis depends on a subset of customer record data that resides in a Salesforce application. The company wants to transfer the data from Salesforce with the least possible infrastructure setup, coding, and operational effort.
Which solution meets these requirements?
A. Use AWS Glue and the SpringML library to connect Apache Spark with Salesforce and extract the data as a table to Amazon S3 in Apache Parquet format. Query the data by using Amazon Redshift Spectrum.
B. Use Amazon AppFlow to create a flow. Establish a connection and a flow trigger to transfer customer record data from Salesforce to an Amazon Redshift table.
C. Use Amazon API Gateway to configure a Salesforce customer data flow subscription to AWS Lambda events and create tables in Amazon S3 in Apache Parquet format. Query the data by using Amazon Redshift Spectrum.
D. Use Salesforce Data Loader to export the Salesforce customer data as a .csv file and load it into Amazon S3. Query the data by using Amazon Redshift Spectrum.
Which solution meets these requirements?
A. Use AWS Glue and the SpringML library to connect Apache Spark with Salesforce and extract the data as a table to Amazon S3 in Apache Parquet format. Query the data by using Amazon Redshift Spectrum.
B. Use Amazon AppFlow to create a flow. Establish a connection and a flow trigger to transfer customer record data from Salesforce to an Amazon Redshift table.
C. Use Amazon API Gateway to configure a Salesforce customer data flow subscription to AWS Lambda events and create tables in Amazon S3 in Apache Parquet format. Query the data by using Amazon Redshift Spectrum.
D. Use Salesforce Data Loader to export the Salesforce customer data as a .csv file and load it into Amazon S3. Query the data by using Amazon Redshift Spectrum.