Step into the shoes of a data engineer working for a mobile game development studio. The company’s data architecture includes an Amazon Athena data lake and an AWS Redshift data warehouse. The board has requested data insights based on user behavior data. You’ll create data pipelines that provide improved OLAP analytics based on user engagement data, build in-app user recommendations based on purchase preferences, and implement a data-driven decision-making process.

In the first liveProject, you’ll create a batch-processing data pipeline using AWS RDS, AWS S3, and Amazon Athena to learn one of the most cost-effective data platform design patterns. Next, you’ll build a simple yet reliable data streaming pipeline that prevents resource shortages and transforms data in real-time (while it’s still relevant), ensuring more accurate data. Lastly, you’ll use Amazon Personalize to create an ML data pipeline that provides product recommendations tailored to users’ data. By the end of the series, you’ll have learned data platform design concepts, business intelligence (BI) concepts, and the extract, transform, load (ETL) process using infrastructure as code, plus you’ll have valuable firsthand experience using popular AWS data transformation and processing tools to build data pipelines.

Pricing

Most of the services used in this liveProject series are available under the AWS Free Tier. However, the Free Tier doesn't cover RDS DB instances launched with Amazon Aurora, Amazon RDS for Microsoft SQL Server, or Oracle database engines. AWS RDS may incur charges if left running. Be sure to delete all associated RDS instances and backup images. Total charges should be under $2 for the series. Please check the AWS Pricing Calculator for more details and cost estimates.