Description: Kafka is a distributed streaming platform that enables the handling of real-time data feeds. It provides a publish-subscribe model for processing streams of records.
Use Case: Use Kafka for ingesting and managing streaming data.
Description: Flink and Spark Streaming are distributed stream processing frameworks that allow you to process and analyze data in real-time.
Use Case: Use Flink or Spark Streaming for performing computations on streaming data and extracting meaningful insights.
Description: Pandas is a powerful data manipulation library that provides data structures for efficiently handling structured data.
Use Case: Use pandas for pre-processing, cleaning, and transforming data.
Description: NumPy is a fundamental package for scientific computing with Python. It provides support for large, multi-dimensional arrays and matrices, along with mathematical functions to operate on these arrays.
Use Case: Use NumPy for numerical operations and array manipulations.
Description: Scikit-learn is a machine learning library that provides simple and efficient tools for data analysis and modeling, including various algorithms for classification, regression, clustering, and more.
Use Case: Use scikit-learn for implementing machine learning models to make decisions based on streaming data.
Description: TensorFlow and PyTorch are popular deep learning frameworks. They provide tools for building and training neural networks, which can be useful for complex pattern recognition tasks.
Use Case: Use TensorFlow or PyTorch for implementing deep learning models for more complex decision-making scenarios.
Description: Fluentd and Logstash are tools for collecting, processing, and forwarding log data. They can be useful for handling log streams in a streaming data processing pipeline.
Use Case: Use Fluentd or Logstash for log collection and processing.
Description: Celery is a distributed task queue system that can be used to distribute tasks across multiple worker nodes.
Use Case: Use Celery for asynchronous task execution, especially for triggering actions based on certain patterns.
Description: Dask is a parallel computing library that integrates with existing Python libraries like NumPy, pandas, and scikit-learn. It enables parallel processing and scaling of computations.
Use Case: Use Dask for parallelizing computations and handling larger-than-memory datasets.
Description: FastAPI and Flask are web frameworks that can be used to build APIs for serving predictions or handling action triggers.
Use Case: Use FastAPI or Flask to expose your models or decision-making logic through RESTful APIs.
Remember to install these libraries using tools like pip or conda before incorporating them into your project. The choice of specific libraries may depend on the specific requirements and architecture of your streaming data processing system.