When dealing with large datasets, I prioritize the following using AWS: Data Preprocessing: I use Amazon SageMaker managed Jupyter notebooks for EDA and visualizations, using tools such as Pandas, and Matplotlib or Seaborn to find missing values, reduce noise, and improve data quality. Computation: I use Amazon EMR with Apache Spark for parallel processing across clusters, processing data in batches to reduce memory and compute constraints. Dimensionality Reduction: I use feature selection or PCA on Sagemaker to simplify datasets, get significant patterns, and reduce complexities on datasets. Storage: Parquet provides compact, efficient storage and quick access, and it is connected with Amazon s3 for scalability and data management.

Data Preprocessing: Clean and preprocess the data to remove duplicates, handle missing values, and normalize it for consistency. Sampling: Use techniques like stratified sampling or downsampling to work with manageable subsets of the data. Distributed Computing: Leverage frameworks like Apache Spark or Dask to process data in parallel across multiple machines. Efficient Storage: Store data in efficient formats like Parquet or HDF5 for fast access and reduced storage requirements. Feature Engineering: Focus on selecting important features to reduce dimensionality and improve model performance.

Managing large datasets requires strategic efficiency. My top approaches: 1. Efficient Data Loading: Stream data in mini-batches using tools like PyTorch DataLoader or TensorFlow's tf.data, paired with optimized formats like Parquet or TFRecord, to reduce memory overhead. 2. Distributed Processing: Leverage frameworks like Apache Spark or Ray for scalable preprocessing, and use stratified sampling to create representative subsets for faster iterations. 3. Feature Optimization: Apply dimensionality reduction (e.g., PCA) or autoencoders to reduce computational load, while domain-specific feature selection improves relevance and performance