Since ERP5's 4773fac27bb33f1beec4ed44e5ba05df009a2654 BigFile component is no longer on file-system thus adjust imports accordingly (and add its bt5).

See merge request nexedi/wendelin!47

See merge request nexedi/wendelin!46

See merge request nexedi/wendelin!48

See merge request nexedi/wendelin!49

erp5_wendelin_data: add new category Big Data for business applications and Stream Ingestion category to "use"

- state after ingestion is "validated"

Be explicit and set desired portal_type as otherwise by default Data Stream Bucket will be created and tests will fail for missing API.

See merge request nexedi/wendelin!45

erp5_wendelin_data_lake_ingestion: split ingestion validation is done right after last chunk (eof) is ingested instead of waiting for the alarm
- better handling of data stream hash calculation and publication

erp5_wendelin_data_lake_ingestion:  split files are no longer appended/processed and no data streams are removed anymore
- all ingestions and data streams corresponding to split parts are kept
- client will receive the list of all data streams and it will be in charge of merging the parts during the download
- validate chunk data streams only when full file was ingested
- only process split ingestions when full file was ingested
- calculate full split file size
- calculate hash and add state control during data stream validation
- stop invalidated ingestions

- do not return invalid data streams (filter by reference and state)
- groups split file datastreams by reference
- returns full file size and large hash

erp5_wendelin_data_lake_ui: fix UI queries to get documents and update renamed server scripts in urls

See merge request nexedi/wendelin!44

categories. This is usually needed for Wendelin applications where for some
reasons data is stored in different Data Streams which must form a logical and sorted "link".
Add test.

See merge request nexedi/wendelin!42

See merge request !41

Allow owner of a Data Stream to edit it despite published. In normal ERP5 this is not normal but with Data Lake functionality validation is done at ebulk side (which also takes care of increasing automatically Data Set's version). This is quite simplified model of ebulk only validation of a Data Set but sufficient for most cases.