Summary
This chapter examined various data transformation methodologies, tools, and use cases, including filters, aggregations, and join. Each operation’s utility and function were stated, which enabled us to cover practical applications.
Next, we explored data transformation use cases in sales analysis, social media analysis, customer segmentation, and website analytics. These case studies demonstrate the concepts’ efficacy.
SQL and Spark, two key data transformation tools, dominated this chapter. SQL, a popular query language, is used to change data, whereas Spark is a powerful data processing engine. We compared SQL and Spark’s DataFrame API to show these tools’ adaptability.
Finally, we discussed the main data transformation techniques, which include event, batch, and stream processing. We emphasized their unique features and usefulness before covering windowing. After, you learned about data transformations through practical examples and were...
