2 phase commit JTA Microservices & BigData Vs TraditionalDB

2 phase commit is slow and so in microservices let's have each it's own database.
Cannot use JTA because However, to take advantage of this mechanism, the resources have to be deployed to a single JTA platform. This isn’t always feasible for a microservice architecture
Jta means usertranssction begin and commit Avi explicit transactions. Javax transaction. Javax.ejb package.
.By far, one of the most feasible models of handling consistency across microservices is eventual consistency. This model doesn’t enforce distributed ACID transactions across microservices. Instead, it proposes to use some mechanisms of ensuring that the system would be eventually consistent at some point in the future. Split in multiple webservices.
BigData is distributed and TraditionalDB is Centralized Single Model as Multiple Nodes replication possible in BigData
BigData can cater structured(database tables),unstructured(jpg,videos) and semi structured data(json,xml)
Hadoop Distributed File System (HDFS) FileSystem for Storage , MapReduce Framework for parrallel processing
Hadoop not suitable for CRUD(create,read,update,delete) for that use BigData
Hadoop is for WORM( write once read many times)
Since BigData is distributed so can guranteee data availability but no gaurantee consistency since data should be replicated across various nodes
For high availability we are compromising consistency incase of partitions