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Now that you know why it is important to manage unstructured data correctly and what problems it can cause, let's examine a typical project workflow for managing unstructured data. Popular data lake solutions include Amazon S3 , Azure Data Lake , and Hadoop.
Tools such as Python’s Pandas library, Apache Spark, or specialised datacleaning software streamline these processes, ensuring data integrity before further transformation. Step 3: Data Transformation Data transformation focuses on converting cleaneddata into a format suitable for analysis and storage.
Here are some project ideas suitable for students interested in big data analytics with Python: 1. Kaggle datasets) and use Python’s Pandas library to perform datacleaning, data wrangling, and exploratory data analysis (EDA). Implement real-time analytics to monitor trends or anomalies in the data.
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