What frameworks are available for big data is answered as follows:

Big data processing and analytics is a complex and large field that involves a multitude of technologies and tools. Here is a list of some commonly used frameworks in big data processing and analytics:

Hadoop:

Hadoop is a distributed computing framework that consists of two core components: HDFS, a distributed file system, and MapReduce.HDFS provides storage for massive amounts of data. MapReduce provides computation for massive data.Hadoop has the advantages of high reliability, high efficiency, scalability and openness, so it is widely used in the field of big data.

Spark:

Spark is a memory-based distributed computing framework that provides faster computation and easier APIs than Hadoop.The core component of Spark is the Resilient Distributed Dataset (RDD), which allows for the distributed storage and processing of data in a cluster. Spark also provides several libraries including MLlib, a machine learning library, GraphX, a graph computing library, and Streaming, a stream processing library.

Flink:

Flink is a high-performance, high-throughput distributed stream processing framework that provides stream-based processing and batch processing.The core component of Flink is the DataFlowGraph, which assigns each node in the data flow graph to a different compute nodes for parallel processing.Flink also provides several libraries including the machine learning library MLlib, the graph computing library GraphX, and others.

Storm:

Storm is a distributed real-time computing system that can process real-time data streams.The core component of Storm is the topology, which can assign each node in the topology to a different compute node for parallel processing.Storm also provides a scalable API. provides an extensible API that can be easily integrated with other frameworks.

Kafka:

Kafka is a distributed stream processing platform that can be used for processing and storing real-time data streams.The core component of Kafka is the Publish-Subscribe model (Pub-Sub), which publishes streams of data to different consumer nodes and guarantees message order and reliability.Kafka also provides an extensible API for easy integration with other frameworks.

Besides these frameworks, there are many other frameworks and tools that can be used for big data processing and analytics, such as Hive, HBase, Pig, Impala, and so on. These frameworks and tools have their own characteristics and advantages, you can choose the right tool for data processing and analysis according to the actual needs.