# AWS Data analysis flow

## Data Analysis flow

1. Collect / Ingest Data
   * Needs Event Pipeline
   *   Event Pipeline Approach #1

       API Gateway Call a Lambda for every API call and then upload the payload into an S3 bucket.

       * Pros: Simple
       * Cons: It gets costly when throughput increases.
   *   Event Pipeline Approach #2

       Directly invoke the Kinesis Firehose from an API Gateway.

       * Pros: Avoid cost of Lambda and S3 request.
       * Cons:&#x20;
       * [Get Started](https://molla4455.gitbook.io/dev-log/openapi/build-event-pipeline-kinesis-with-swagger)
2. Store
   * S3
   * RDS
3. Process / Analyze
   * EMR (Redshift, Spark)
4. Visualize / Report

## To get more insights

* [야생의 땅 듀랑고의 데이터 엔지니어링 이야기](https://www.slideshare.net/ssuser380e9c/ndc18-95524337)
* [Kinesis와 Lambda를 이용한 비용 효율적인 센서 데이터 처리](https://www.slideshare.net/awskr/kinesis-lambda-77400693)
* [Data Analytics Flow & Components](https://aipkds.tistory.com/?page=9)
* [AWS를 통한 빅데이터 기반 비지니스 인텔리전스 구축](https://www.slideshare.net/awskorea/1-bigdata-bi-configuration)
* [Build Serverless Real-Time Analytics Pipelines for Mobile Gaming](https://d1.awsstatic.com/architecture-diagrams/ArchitectureDiagrams/serverless-analytics-for-mobile-gaming.pdf?did=wp_card\&trk=wp_card)
* [How to build an Event Pipeline within 1 hour and minimum lines of code in AWS](https://medium.com/a-tale-of-2-from-data-to-information/how-to-build-an-event-pipeline-within-1-hour-and-minimum-lines-of-code-in-aws-eb1bd0bb6cd2)
