Speeding up ML Pipeline Development and Runtime with Serverless and SQL
Question
You want to rebuild your ML pipeline for structured data on Google Cloud.
You are using PySpark to conduct data transformations at scale, but your pipelines are taking over 12 hours to run.
To speed up development and pipeline run time, you want to use a serverless tool and SQL syntax.
You have already moved your raw data into Cloud Storage.
How should you build the pipeline on Google Cloud while meeting the speed and processing requirements?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.B.
The best approach for rebuilding an ML pipeline for structured data on Google Cloud, using a serverless tool and SQL syntax, while meeting the speed and processing requirements, would be option D: Ingest the data into BigQuery using BigQuery Load, convert PySpark commands into BigQuery SQL queries to transform the data, and then write the transformations to a new table.
Here is a detailed explanation for why option D is the best choice:
Ingest Data into BigQuery: The first step is to ingest the raw data into BigQuery using BigQuery Load. This is a fast and efficient way to load large amounts of data into BigQuery, and it can handle both structured and unstructured data. BigQuery also allows for quick and easy querying of large datasets.
Convert PySpark commands into BigQuery SQL queries: Next, PySpark commands need to be converted into BigQuery SQL queries. This is necessary because SQL is a simpler language and easier to write, read, and maintain. BigQuery SQL queries can also run much faster than PySpark commands because BigQuery is designed to handle large datasets and can parallelize queries to improve performance.
Transform data using BigQuery SQL queries: Once the PySpark commands are converted into BigQuery SQL queries, they can be used to transform the data. BigQuery offers a powerful set of SQL functions and operators for data transformation, aggregation, and filtering. This allows for efficient and scalable data transformation at scale, without the need for complex PySpark code.
Write transformations to a new table: Finally, the transformed data can be written to a new table in BigQuery. This table can then be used as the input for downstream ML processes or analysis. BigQuery offers a variety of features for working with tables, including partitioning, clustering, and sharding, which can improve performance and reduce costs.
In conclusion, option D is the best choice because it offers a simple, fast, and efficient way to build an ML pipeline for structured data on Google Cloud using serverless tools and SQL syntax. By ingesting data into BigQuery and using SQL queries for transformation, pipeline run times can be significantly reduced, and development time can be accelerated.
