Ingesting Data into the Cloud
Ingesting data into the cloud is a key step in any data pipeline and can greatly impact the efficiency and scalability of your data processing and analysis. This is why it is critical for any cloud data architect to have a deep understanding of data ingestion techniques and architectures on Azure. In this chapter, we will dive into the world of data ingestion on Azure, focusing on the key concepts of batch ingestion and data streaming, as well as various ingestion architectures.
We will begin by discussing the differences between batch ingestion and data streaming, and when to use each method. We will also explore the benefits and limitations of each approach and provide examples of use cases for each method.
Next, we will explore data ingestion architectures on Azure. We will introduce Azure Data Factory and Azure Synapse pipelines for designing and implementing data pipelines on Azure. Azure Data Lake Storage (ADLS) is introduced for permanently...
