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We plan to always store data in the database with the same format.
One or more timestamp columns, usually date time
One or more columns that designate unique time arrays. This can be an integer ID or set of dimension columns.
One value column. The concept of a pivoted table is not supported internally, but can be passed as input data.
Database format
timestamp
id
value
2020-01-01 01:00:00
1
1.0
2020-01-01 02:00:00
1
2.0
2020-01-01 03:00:00
1
3.0
2020-01-01 01:00:00
2
11.0
2020-01-01 02:00:00
2
12.0
2020-01-01 03:00:00
2
13.0
Input Formats
Create a view from data stored in files matching the format above. No copying required.
Create a table from data stored in files matching the format above.
Array of floats, optional timestamps and IDs. Start time and frequency are required without timestamps. Add rows to existing table or create a new one. Auto-generate IDs and timestamps as necessary.
Create a table from files with data stored with a pivoted dimension. Unpivot the data for final storage. Timestamps are optional. If they exist in Parquet files must have timestamp type. If they existing in CSV files, str-format is required unless it matches the default ISO format.
timestamp
device1
device2
2020-01-01 01:00:00
1.0
11.0
2020-01-01 02:00:00
1.0
12.0
2020-01-01 03:00:00
3.0
13.0
That would be converted into
timestamp
device_name
value
2020-01-01 01:00:00
device1
1.0
2020-01-01 02:00:00
device1
2.0
2020-01-01 03:00:00
device1
3.0
2020-01-01 01:00:00
device2
11.0
2020-01-01 02:00:00
device2
12.0
2020-01-01 03:00:00
device2
13.0
Required file formats
Parquet
Arrow
CSV
TBD
Units
The text was updated successfully, but these errors were encountered:
Database vs User Schemas
We plan to always store data in the database with the same format.
Database format
Input Formats
That would be converted into
Required file formats
TBD
The text was updated successfully, but these errors were encountered: