Edit the schema and be sure to fix any values, like adding the correct data types.
Select the input as a previously crawled JSON data table and select a new output empty directory. We transform our data set, by using a Glue ETL. $5 per TB of data scanned is the pricing for Athena. Athena pricing varies based upon the scanning of the data. For large data sets it reduces scanned data and reduces query time hence reducing the cost required for scanning the data in Athena. Parquet has been selected as it is columnar. After running the crawler, a table gets created which stores all the metadata about the JSON file for querying purpose.Īt this point we could actually query the data, but best practice is to run an ETL (Extract, Transform & Load), to transform the data so it is more efficient to query later on. This will parse all the files from S3 bucket. Next create a crawler using the classifier, and select input as the JSON data from stored S3 bucket path. Since in our use case we are using JSON data set, we can use JSON custom classifier ( regex) where we can mention a JSON path expression which can be used to define a JSON structure and table schema. Athena uses these tables for querying.įor proper grouping of Glue metadata tables, create customized classifiers based on different data types such as ‘JSON’. The crawler will traverse the specified S3 files and group the things into tables. First it provides data crawlers that use inbuilt and/or custom classifiers to try and parse the JSON data. This is where Glue comes into the picture. Now we have the JSON data, we need to structure it enough that Athena can query it. This data is in JSON format which needs to be stored in S3 bucket as mentioned in the above diagram. In this use case, we can use the claims data of medical insurance company or vehicle contracts. We can use Amazon S3 for data storage, data transformation (ETL) using Glue and then data visualization (Analytics) via Athena & QuickSight.īelow diagram represents the workflow of usage of these AWS services.įirst we need to generate our data set. In order to fulfill this end to end requirement usage of AWS services is the best option. The problem here is to handle such a large dataset and generate complex reporting by doing data transformation. Process raw claims data (medical insurance or vehicle contracts related data) which is a large dataset and generate reporting visuals with the help of processed data.
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