Summary
In this chapter, we covered the evolution of modern data architectures and key design patterns, such as the Lambda architecture that enables building scalable and flexible data platforms. We learned how the Lambda approach combines both batch and real-time data processing to provide historical analytics while also powering low-latency applications.
We discussed the transition from traditional data warehouses to next-generation data lakes and lakehouses. You now understand how these modern data platforms based on cloud object storage provide schema flexibility, cost efficiency at scale, and unification of batch and streaming data.
We also did a deep dive into the components and technologies that make up the modern data stack. This included data ingestion tools such as Kafka and Spark, distributed processing engines such as Spark Structured Streaming for streams and Spark SQL for batch data, orchestrators such as Apache Airflow, storage on cloud object stores, and serving...
