Value Streams Vs Data Tables
As we know, Value Streams are for continuous data streams and Data tables are moreover designed for static data.
What would be an ideal use-case when one should use value streams and Data tables?
As we know, Value Streams are for continuous data streams and Data tables are moreover designed for static data.
What would be an ideal use-case when one should use value streams and Data tables?
Not to confuse the picture.
but there are Streams, ValueStreams and DataTables
From the Digital Guide:
A ThingWorx stream is a list of activities from things or data associated with things. Data associated with things includes time series data and event-driven data.
ValueStreams are also like streams, but they will store the data 'flat', because it does it in an automated fashion, while you have to script how to store the information into a regular stream.
Streams also work in an asynchronous way and are more suited for large number of entries.
A ThingWorx data table is similar to a Relational Database (RDBMS) Table. A ThingWorx data shape defines the columns or fields of the data table. To use a data shape to define a data table, at least one of the fields in the data shape must be set as a primary key. This enables the update and delete functionality, as well as efficient querying of data.
DataTables are synchronous and support primary keys and indexing and are more suited for reference/lookup.
So generally anytime you are storing information over time, historical information, think Stream/ValueStream.
If you need a more static reference table, think DataTable.
Enter your E-mail address. We'll send you an e-mail with instructions to reset your password.