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What’s the best approach to handling large datasets while scraping?
Breaking the data into manageable chunks is essential. I use pagination to pull smaller sets at a time, which helps avoid memory overload.
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7 Replies
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For massive datasets, I use a cloud database like MongoDB or AWS DynamoDB to store data as it’s scraped. This keeps it organized and accessible.
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Data compression, such as saving in Parquet or JSONL format, helps reduce file size and speeds up data processing.
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If I need to process the data immediately, I set up streaming to a data pipeline with tools like Kafka. This way, I handle data in real time.
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Scheduling periodic scrapes rather than one large scrape can help maintain manageable data flows, especially for sites with frequent updates.
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A distributed scraping setup, where multiple scrapers work on different parts of the site simultaneously, helps speed up data collection without overloading any one scraper.
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Real-time monitoring of data quality helps me catch errors or missing entries early, especially when scraping high-volume sites.
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I also use local storage as a temporary buffer, then upload the data in batches to cloud storage. This method reduces network load during scraping.
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