Chapter 8. Implementing Batch and Streaming Pipelines
In previous chapters, we provided an overview of AWS data analytics services and explained how to design a data ingestion pipeline, apply transformations, manage data stores, implement security and governance, and achieve operational efficiency for your analytics workloads.
In this chapter, we will provide a hands-on implementation guide of popular use cases for batch and streaming pipelines. Before getting started, please make sure you have created an AWS account and configured IAM permissions as described in Chapter 2.
Data Processing Pipeline
A data processing pipeline is a sequence of steps to refine and transform the data and make it available in a format that can be consumed by end users for analytics. The use cases for which data needs to be transformed may include the following:
Cleansing data and improving data quality
Transforming data by aggregating with internal datasets and applying specific business rules
Formatting it for time series analysis or preparing data for machine learning model development
Creating a specific data model for faster data analysis or BI reporting
Making data available in a specific format to share with downstream systems
Figure 8-1 represents a high-level architecture for a data pipeline that includes data sources, data ingestion, data processing, and data consumption layers.
Figure ...
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