CHAPTER 7Data Pipelines
The data engineering life cycle consists of various stages, but how does data flow between these stages? It flows through data pipelines. Data pipelines power data systems and are a foundational component of data engineering. A data pipeline simply moves data between systems, applies transformations to make it analysis-ready, or does both concurrently.
It’s a structured system that controls the flow of data from one or more sources to one or more destinations. Along the way, it can perform operations like cleaning, filtering, joining, or aggregating data. Data pipelines can run in real time or in batches, depending on the needs of the business, and it usually follows a series of stages: collect, ingest, process, store, and serve.
IN THIS CHAPTER, YOU WILL LEARN THE FOLLOWING:
- Popular ingestion methods in data engineering
- How batch and streaming pipelines work
- Publish and subscribe patterns in message queues
- Windowing in stream processing
- The Lambda architecture
- Data orchestration and its key components
- Scheduling and automation in data pipelines
- Best practices for designing directed acyclic graphs (DAGs)
- How to build an ETL pipeline and automate with Apache Airflow
At the end of this chapter, you will have a good understanding of various types of data pipeline architectures, their use cases, and the techniques needed to design, build, and manage them effectively.
Batch Pipelines
Imagine a fintech (financial-technology) company that offers loan services. ...
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