Summary
In this chapter, we provided a comprehensive overview of the various methods and tools available for getting data into the cloud. The chapter started by discussing the differences between batch ingestion and streaming ingestion and when to use each method. It explained the benefits and limitations of each approach and provided examples of use cases for each method.
One of the key tools introduced in this chapter is ADLS. This is a powerful storage solution for big data and allows for efficient and flexible storage of large datasets in the cloud. The chapter explained how ADLS can store data in a variety of formats, including structured and unstructured data. We also discussed access tiers, redundancy, and data lake tiers.
We delved into architectures for both batch ingestion from cloud sources and on-premises sources. Next, we explained streaming architectures, such as lambda and kappa architectures, which are becoming increasingly popular for real-time data ingestion...
