maximum data size Hive can handle?

Apache Hive is built on top of Hadoop, which means it's designed to handle very large data sets that are distributed across clusters of computers. The maximum data size Hive can handle is not limited by the software itself, but by the underlying Hadoop infrastructure.

Here are the factors that determine the maximum data size Hive can handle:

1. Number of Nodes in the Hadoop Cluster: The more nodes you have in your Hadoop cluster, the more data you can store and process. Each node in a Hadoop cluster contributes its own storage and processing power.

2. Storage Capacity of Each Node: The total amount of data that can be stored in a Hadoop cluster (and therefore accessed by Hive) is determined by the storage capacity of each node in the cluster. If each node has a large amount of disk space, the cluster can store more data.

3. HDFS Replication Factor: Hadoop Distributed File System (HDFS) typically creates multiple copies (replicas) of each data block for fault tolerance, which effectively reduces the total available storage. The default replication factor is 3, but it can be configured based on your needs.

4. Data Processing Capacity: The maximum data size Hive can handle also depends on the processing power of your Hadoop cluster. Processing large amounts of data requires significant computational resources.

In practice, Hadoop clusters can scale to handle petabytes of data and beyond. Hive, as a data warehousing solution built on top of Hadoop, can handle data analysis tasks on these large data sets. However, the performance of Hive queries can be impacted by the size of the data, the complexity of the queries, and the resources available in the Hadoop cluster.