Storing the transformed files in S3 provides the additional benefit of being able to query this data using Amazon Athena or Amazon Redshift Spectrum. You can use either of these format types for long-term storage in Amazon S3. ![]() You can also write it to delimited text files, such as in comma-separated value (CSV) format, or columnar file formats such as Optimized Row Columnar (ORC) format. You can then write the data to a database or to a data warehouse. If the developers want to ETL this data into their data warehouse, they might have to resort to nested loops or recursive functions in their code. Further down, the player’s arsenal information includes additional nested JSON data. ![]() The player named “user1” has characteristics such as race, class, and location in nested JSON data. Sample 1 shows example user data from the game. Suppose that the developers of a video game want to use a data warehouse like Amazon Redshift to run reports on player behavior based on data that is stored in JSON. Let’s look at how Relationalize can help you with a sample use case. ![]() The transformed data maintains a list of the original keys from the nested JSON separated by periods. ![]() Relationalize transforms the nested JSON into key-value pairs at the outermost level of the JSON document. May 2022: This post was reviewed and updated to include resources for orchestrating data and machine learning pipelines.ĪWS Glue has a transform called Relationalize that simplifies the extract, transform, load (ETL) process by converting nested JSON into columns that you can easily import into relational databases.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |