Handling Large Datasets
Big data requires specialized tools and approaches.
Spark
PySpark provides Python interface to Spark. SparkSession for initialization. RDD: Resilient Distributed Datasets.
DataFrame API similar to Pandas. Spark SQL for queries. Distributed processing across clusters.
Dask
Dask provides parallel computing in Python. dask.dataframe mimics Pandas API.
Scales from laptop to cluster. Good for larger-than-memory datasets.
Cloud Platforms
AWS (EMR, SageMaker), GCP (Dataproc, Vertex AI), Azure (Synapse, ML) provide managed big data.
Key Takeaways
- Spark handles big data processing
- Dask scales Python workflows
- Cloud platforms provide managed solutions