From the course: Google Cloud Data Engineering Foundations

Different kinds of data pipelines and their use cases - Google Cloud Tutorial

From the course: Google Cloud Data Engineering Foundations

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Different kinds of data pipelines and their use cases

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- [Instructor] Data pipelines are the heart of data engineering workflows. They are responsible for moving data from one system to another, transforming it along the way, and making it available for analysis. There are different types of data pipelines, batch pipelines, stream pipelines, and orchestrator pipelines. In batch processing, we have large volumes of data uploaded in batches, chunked by time. They are ideal for scenarios where data can be collected over a period of time and processed in bulk. Batch processing are commonly also called as ETL pipelines, extract transform load pipelines. They're good for data warehousing or historical data analysis. On the other extreme end, we have stream data pipelines. Stream pipelines are designed to process data continuously, asset flows through the system. There are ideal for scenarios where the data needs to be processed in real time and analyzed on the fly. Stream pipelines are commonly used for tasks like IoT data processing, log analysis, even driven architecture, or my favorite, stock market data analysis. To differentiate between realtime and streaming pipeline, consider the following use case. A stock market data analyst is a real-time pipeline, whereas the video that you're watching right now is a streaming pipeline. The difference, the data that's arriving at real-time, it's highly crucial for the business that it has to be processed. If you miss a particular window of data, then you might be in huge trouble. Last but not the least, my favorite, orchestrated pipelines. Orchestrated pipelines are designed to coordinate execution of multiple tasks in a workflow. They're ideal for complex data processing workflows that needs to be managed and monitored. Orchestration pipelines are commonly used for tasks like data validation, data transformation, and workflow scheduling, and it involves only minimal business logic. Each type of data pipeline has its own use cases and benefits. Understanding the difference between them is essential for designing effective data solutions. Using one over the other can lead to inefficiencies and bottlenecks in your data pipelines. So choose your pipelines wisely.

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