DAA-C01 Exam Dumps | You are building a real-time data pipeline to ingest IoT sensor data into Snowflake. The data arrives

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Question 125/151

You are building a real-time data pipeline to ingest IoT sensor data into Snowflake. The data arrives in Avro format via Kafka topics. You need to ensure data integrity, minimize latency, and accurately determine the data volume ingested for billing and monitoring purposes. Which of the following options provide the BEST combination of techniques to achieve these goals? (Select TWO)

A. Use Snowflake's Kafka connector to directly load data from Kafka into a raw data table. After loading, run scheduled tasks to perform data quality checks and transformations.

B. Implement a stream processing framework (e.g., Apache Flink, Spark Streaming) to consume data from Kafka, perform data quality checks and transformations, and then load the processed data into Snowflake using the Snowflake JDBC driver.

C. Use Snowpipe with auto-ingest to continuously load data from a cloud storage location (e.g., AWS S3, Azure Blob Storage) where Kafka Connect is writing the Avro data. Configure Snowpipe error notifications to capture data quality issues. Do not perform Transformation.

D. Implement a custom Kafka consumer application that validates and transforms the Avro data before loading it into a staging table in Snowflake using the Snowflake Python connector. Use a Snowflake Task to move data from the staging to the final table.

E. Utilize Snowflake Streams and Tasks to create a change data capture (CDC) pipeline within Snowflake. Initially, load all data into a raw table. Then, use a stream to track changes, validate the data, and apply transformations incrementally.

Correct Answer: B,E

Options B and E provide the best combination. Option B uses a stream processing framework for real-time validation and transformation before loading into Snowflake, minimizing latency and ensuring data quality. Option E leverages Snowflake Streams and Tasks for CDC, enabling incremental data validation and transformation within Snowflake, ensuring data integrity and volume tracking.

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