SQL and traditional relational tables are focused on data warehousing, with consistently structured data (ie every tuple is the same)Much of the strength of Pig and Hadoop is the ability to process the vast amounts of semi/unstructured dataWith HCat we have made it easier for Pig and MR users to interact with data in the data warehouseNeed to make it go the other way as wellGood news is most of the pieces are in place, just need to tie a few things togetherObservation: much of the semi/unstructured data records its own structure (PB, Thrift, Avro, JSON, etc.)
SQL and traditional relational tables are focused on data warehousing, with consistently structured data (ie every tuple is the same)Much of the strength of Pig and Hadoop is the ability to process the vast amounts of semi/unstructured dataWith HCat we have made it easier for Pig and MR users to interact with data in the data warehouseNeed to make it go the other way as wellGood news is most of the pieces are in place, just need to tie a few things togetherObservation: much of the semi/unstructured data records its own structure (PB, Thrift, Avro, JSON, etc.)
SQL and traditional relational tables are focused on data warehousing, with consistently structured data (ie every tuple is the same)Much of the strength of Pig and Hadoop is the ability to process the vast amounts of semi/unstructured dataWith HCat we have made it easier for Pig and MR users to interact with data in the data warehouseNeed to make it go the other way as wellGood news is most of the pieces are in place, just need to tie a few things togetherObservation: much of the semi/unstructured data records its own structure (PB, Thrift, Avro, JSON, etc.)
Not concurrent: runs one query at a timeNot secure: runs as Hive user