Databricks Certified Data Engineer - Associate
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You are working on a data engineering project that requires processing a large volume of Avro files stored in a directory named 'avro_data'. These files are named in the pattern 'data_YYYYMMDD.avro', where YYYYMMDD represents the date. Your task is to create a Spark DataFrame query that efficiently extracts data from these files and creates a temporary view named 'avro_view'. The solution must consider the following constraints: 1) The query should be optimized for performance given the large volume of data. 2) It must correctly specify the Avro file format to ensure accurate data parsing. 3) The temporary view should be accessible for subsequent SQL queries. Choose the best option from the following:
You are working on a data engineering project that requires processing a large volume of Avro files stored in a directory named 'avro_data'. These files are named in the pattern 'data_YYYYMMDD.avro', where YYYYMMDD represents the date. Your task is to create a Spark DataFrame query that efficiently extracts data from these files and creates a temporary view named 'avro_view'. The solution must consider the following constraints: 1) The query should be optimized for performance given the large volume of data. 2) It must correctly specify the Avro file format to ensure accurate data parsing. 3) The temporary view should be accessible for subsequent SQL queries. Choose the best option from the following:
Explanation:
The correct answer is A, as it directly specifies the Avro file format with the 'avro.' prefix, ensuring the data is correctly parsed. The OPTIONS clause includes the necessary Avro schema converter, and the query structure is optimized for performance by leveraging Spark's ability to read files in parallel. Options B, C, and D either incorrectly specify the file format or use an unnecessary intermediary, which could impact performance and accuracy.