Summary

In this chapter, you learned about some of the fundamental concepts surrounding big data, including data lakes, data warehouses, and data integration. You also then learned about the services on GCP with which you can build big data and data analytics solutions. You've understood what problems are solved by a message-oriented architecture and why and when you would build data pipelines. We then discussed some common architectural patterns, and how to choose the right services for your solution. Finally, you got some hands-on practice with a use case involving an IoT data analytics pipeline, where you built a solution using Cloud IoT Core, Pub/Sub, Dataflow, and BigQuery.

You should now possess the foundational knowledge and skills to design big data solutions for modern enterprises that can unlock business value from large volumes of data.

In the next chapter, you will learn about another crucial pillar of modern enterprise solutions architecture: automation and...