Batch Processing in Data Pipelines
Batch processing is a core concept in data engineering, enabling you to handle large volumes of data efficiently by grouping data into batches and processing them together at scheduled intervals. Unlike real-time or streaming processing, batch processing focuses on throughput and reliability rather than immediate results. This approach is especially well-suited for ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) workflows, where you often need to move, clean, and transform data from multiple sources before loading it into a data warehouse or analytics platform. Batch processing allows you to optimize resource usage, simplify error handling, and ensure data consistency across large datasets. It is commonly used for daily data loads, periodic reporting, and backfilling historical data, making it a foundational strategy in building robust data pipelines.
123456789101112131415import pandas as pd # Read the CSV file from the provided URL df = pd.read_csv("https://content-media-cdn.codefinity.com/courses/68740680-eb0c-4ee2-91a8-54871b7c1823/titanic.csv") # Example transformation: add a column indicating processing batch # Here, set a fixed batch name for demonstration batch_name = "2024-06-01-nightly" df["batch"] = batch_name # Example processing: filter passengers older than 30 filtered_df = df[df["Age"] > 30] # Output the result print(filtered_df.head())
When designing batch processing workflows, you need to consider how and when batches are executed. Batch windowing refers to the time intervals at which data is collected and processed—such as hourly, daily, or weekly. Choosing the right batch window depends on your business requirements and the freshness of data needed for downstream tasks. Scheduling is typically managed by job schedulers or orchestration tools, which automate the execution of batch jobs at specified times or in response to triggers. Latency, or the delay between data arrival and its availability after processing, is another important consideration. While batch processing can introduce higher latency compared to real-time systems, it provides predictability, scalability, and easier error recovery, making it ideal for many analytical and reporting scenarios in ETL and ELT pipelines.
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Batch processing is a core concept in data engineering, enabling you to handle large volumes of data efficiently by grouping data into batches and processing them together at scheduled intervals. Unlike real-time or streaming processing, batch processing focuses on throughput and reliability rather than immediate results. This approach is especially well-suited for ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) workflows, where you often need to move, clean, and transform data from multiple sources before loading it into a data warehouse or analytics platform. Batch processing allows you to optimize resource usage, simplify error handling, and ensure data consistency across large datasets. It is commonly used for daily data loads, periodic reporting, and backfilling historical data, making it a foundational strategy in building robust data pipelines.
123456789101112131415import pandas as pd # Read the CSV file from the provided URL df = pd.read_csv("https://content-media-cdn.codefinity.com/courses/68740680-eb0c-4ee2-91a8-54871b7c1823/titanic.csv") # Example transformation: add a column indicating processing batch # Here, set a fixed batch name for demonstration batch_name = "2024-06-01-nightly" df["batch"] = batch_name # Example processing: filter passengers older than 30 filtered_df = df[df["Age"] > 30] # Output the result print(filtered_df.head())
When designing batch processing workflows, you need to consider how and when batches are executed. Batch windowing refers to the time intervals at which data is collected and processed—such as hourly, daily, or weekly. Choosing the right batch window depends on your business requirements and the freshness of data needed for downstream tasks. Scheduling is typically managed by job schedulers or orchestration tools, which automate the execution of batch jobs at specified times or in response to triggers. Latency, or the delay between data arrival and its availability after processing, is another important consideration. While batch processing can introduce higher latency compared to real-time systems, it provides predictability, scalability, and easier error recovery, making it ideal for many analytical and reporting scenarios in ETL and ELT pipelines.
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