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Towards Data Science
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Pandas Can’t Handle This: How ArcticDB Powers Massive Datasets
- A data analysis project involving weather data and stock prices of energy companies highlighted the challenges of handling massive datasets with Pandas and the advantages of using ArcticDB.
- Downloading global weather data with over 3.8 billion datapoints proved to be a challenging task compared to stock price data.
- ArcticDB, a database developed at Man Group, was used for handling large datasets efficiently in this project.
- ArcticDB offers fast queries, versioning support, and better memory management, making it a preferred choice for handling massive datasets.
- ArcticDB's integration with storage systems like AWS S3, Mongo DB, and LMDB allows for easy scaling into production.
- ArcticDB provides seamless data retrieval and versioning support, enabling efficient analysis of large datasets.
- Comparative analysis showed ArcticDB to be significantly faster and more efficient than Pandas for handling large datasets.
- Pandas is suitable for smaller projects, while ArcticDB excels in scenarios requiring performance, scalability, and quick data retrieval.
- ArcticDB complements Pandas by bridging the gap between interactive exploration and production-scale analytics, making it a valuable tool for handling substantial datasets.
- Overall, ArcticDB proves to be a crucial ally when dealing with large, time-series data, enabling smooth workflows and efficient data analysis.
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