OpenSearch is a data analytics and search engine that offers powerful tools for indexing, querying, and visualizing complex data. It is used in cybersecurity, health care, and financial services. To analyze data in OpenSearch, you must first ingest it. AWS provides several ways to streamline this process, including using the AWS Ingestion pipeline, Lambda integration, Kinesis Data Firehose, REST API, etc.

In this Cloud Lab, you will learn how to automate data ingestion by building an AWS OSIOpenSearch Ingestion pipeline. The pipeline starts by uploading data objects to an S3 bucket, which acts as the data source, while OpenSearch serves as the destination (or sink) for analysis. Whenever a file is uploaded to the S3 bucket, an SQS event is triggered to notify the pipeline in real time. The AWS OSI pipeline then reads and parses the S3 objects, sending the processed data to OpenSearch for analysis.

The following is the high-level architecture diagram of the infrastructure you’ll create in this Cloud Lab:

A high-level architectural diagram for automating data ingestion into OpenSearch

After completing this Cloud Lab, you’ll be well-equipped to create and manage IAM policies and roles to design and build an AWS data ingestion pipeline for OpenSearch. Moreover, you will have a thorough understanding of how to trigger S3 events, handle them using SQS, and leverage these events to automate data ingestion workflows within AWS.