Problem: By utilizing AWS Glue, we aim to tackle multiple challenges. One of these involves gathering data from clients' APIs, transforming it, and then loading it into Amazon S3 and Redshift. Another critical aspect is ensuring that data loaded into S3 and Redshift is properly prepared for subsequent processes, such as data visualization. Solution: We utilize AWS Glue Jobs to connect to clients' APIs. After gathering and transforming this data, we ingest it into S3 and Redshift. Another use case involves ETL Workflows, which serve as mechanisms for extracting data from S3 and Redshift, transforming it via SQL queries into usable data for inserting/updating our dimensions and facts in the main schemas. These updated facts and dimensions serve as the source for data visualizations. Additionally, we utilize AWS Glue Data Catalog feature, which serves as a centralized metadata repository for all client data.

Problem: Creating resources from the console or using the CLI is easier, but as the infrastructure grows, tracking deployed resources becomes increasingly challenging. While CloudFormation addresses this issue, manually updating every stack is not practical. Solution: In addition to CloudFormation, which serves as an Infrastructure as Code (IaC) tool, crucial services for this solution are from the AWS Developer Tools group of services. AWS CodePipeline manages the entire infrastructure deployment process, retrieving data from the source Git repository and deploying it to the appropriate CloudFormation stack. Additionally, if the client prefers to use AWS's Git repository, AWS CodeCommit can be employed. If there is a need to build code or deploy it, for example, to an EC2 instance, AWS CodeBuild and AWS CodeDeploy are utilized.

Database migration to Amazon S3 involves transferring data from traditional databases or storage systems to Amazon Simple Storage Service (S3), a scalable and secure object storage service. This process includes exporting data, transforming it into a compatible format, and uploading it to S3, enabling cost-effective storage, enhanced accessibility, and integration with various AWS analytics and machine learning services.

Problem: Our client is facing issues with the poor analytical capabilities of their production databases. Although data consolidation from these databases is performed using Amazon S3, analytics on the data within S3 proves to be challenging . Consequently, additional processing is required to enhance the efficiency of data analysis. Solution: Within our Redshift cluster, we maintain one or more staging schemas, essentially serving as data replications from the Data Lake. To facilitate the transfer of data from S3 into the Redshift staging schemas, we leverage event notifications in conjunction with AWS Lambda and Amazon SQS. The subsequent step involves ETL processing of this data, for which AWS Glue is employed. This process utilizes data from the staging schemas, optimizing it for further operations, and then stores it into the main schema. Views and materialized views are subsequently generated from this data, making them readily accessible for use by Data Visualization tools.

Problem: Businesses often face challenges when it comes to seamlessly integrating Apache Kafk into their systems, particularly in automatically triggering applications to read messages as soon as they appear in a Kafka topic. Solution: Our solution automates this process, ensuring that whenever new messages are detected within a Kafka topic, our application is triggered to read them without manual intervention. This is achieved by leveraging AWS serverless services like AWS Lambda (for reading messages from Kafka topic) and Amazon S3 (for storing messages).