Explain about Pipeline Parallelism & Partition Parallelism?

 

Pipeline Parallelism

Pipeline parallelism involves breaking down a process into several distinct stages, similar to an assembly line in a factory. Each stage of the pipeline handles a specific part of the task, and stages can work simultaneously. As soon as one stage finishes processing a piece of data, it hands it off to the next stage, while starting to process new data.

Example: In an ETL (Extract, Transform, Load) process:

1. Extract Stage: Data is extracted from source systems.

2. Transform Stage: Extracted data is transformed into a suitable format.

3. Load Stage: Transformed data is loaded into the data warehouse.

By using pipeline parallelism, these stages can overlap in execution, speeding up the overall process. While the extract stage processes new data, the transform stage can work on already extracted data, and the load stage can handle data that's been transformed.

Partition Parallelism

Partition parallelism involves dividing large datasets or tables into smaller, more manageable segments, or partitions. Each partition can be processed independently and in parallel, which speeds up operations such as querying and updating.

Example: Imagine you have a massive sales table with millions of rows.

1. You could partition this table by month, creating smaller segments like January, February, etc.

2. When you run a query for sales data for a specific month, it only needs to scan that month's partition, not the entire table.

3. This drastically reduces the amount of data processed and improves performance.

Partition parallelism is particularly useful in distributed systems where partitions can be allocated to different nodes, allowing for scalable and efficient data processing.

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