METERDATA_TRK
Tracking table for the publication of wholesale settlement data associated with BILLING run
Deprecation Warning
This table is in the HISTORICAL TABLES package, so is likely deprecated.
Warning
This is a different table to METERDATATRK
Column Schema
CASE_ID
Case Identifier
SQL Type: NUMBER(15,0)
Polars Type: pl.Int64
This column is mandatory, and a primary key.
AGGREGATE_READS_LOAD_DATETIME
Timestamp of the aggregated reads being loaded for this case
SQL Type: DATE
Polars Type: pl.Datetime()
This column is not mandatory.
Example: 2026/01/20 00:05:00
ENDDATE
The end date of data associated with the Case_ID
Warning
Despite what the name suggests, this is a datetime, not a date.
SQL Type: DATE
Polars Type: pl.Datetime()
This column is not mandatory.
Example: 2026/01/20 00:05:00
INDIVIDUAL_READS_LOAD_DATETIME
Timestamp of the non aggregated reads being loaded for this case
SQL Type: DATE
Polars Type: pl.Datetime()
This column is not mandatory.
Example: 2026/01/20 00:05:00
LASTCHANGED
Last changed date for the record
For more information, see the dedicated documentation page.
SQL Type: DATE
Polars Type: pl.Datetime()
This column is not mandatory.
Example: 2026/01/20 00:05:00
STARTDATE
The start date of data associated with the CASE_ID
Warning
Despite what the name suggests, this is a datetime, not a date.
SQL Type: DATE
Polars Type: pl.Datetime()
This column is not mandatory.
Example: 2026/01/20 00:05:00
New Data Timing
Posting of a billing run
Code Examples
Polars
Casting is enums, categoricals etc is not essential, but may speed up parsing.
Swapping int for pl.UInt16, pl.Int32 would also speed up parsing. However some of the datatypes are inferred, so they may be wrong.
This code deduplicates for you. Deduplication is normally only required across multiple months.
This code assumes that all columns are present, which may not be true for older files.
import polars as pl
import polars.selectors as cs
# e.g. "2029/03/15 00:05:00"
dt_s_fmt = "%Y/%m/%d %H:%M:%S"
csv_paths = ["SOMEFILE.CSV", "OTHERFILE.CSV"]
lf = (
pl.concat(
[
pl.scan_csv(
csv_path, # must be unzipped beforehand
skip_rows=1, # skip the header
infer_schema=False, # much faster
)
.filter(cs.by_index(0) == "D") # drop the footer
.select(~cs.by_index(range(4))) # drop metadata columns
for csv_path in csv_paths
],
how="diagonal_relaxed",
)
.with_columns(
pl.col(
"AGGREGATE_READS_LOAD_DATETIME",
"ENDDATE",
"INDIVIDUAL_READS_LOAD_DATETIME",
"LASTCHANGED",
"STARTDATE",
).str.strptime(pl.Datetime, dt_s_fmt),
)
.cast({"CASE_ID": int}) # or pl.Int64
# Deduplicate
# We want one row per ['CASE_ID']
# Tie-breaker: greatest value of ['CASE_ID']
.sort(["CASE_ID"], descending=False)
.unique(subset=["CASE_ID"], keep="last")
)
Nemosis
This table is not yet available in Nemosis.
Availability
AEMO's documentation states that this table's visibility is "Public". However, it is not available in the public monthly MMSDM archive. AEMO probably call this public because it is available to all market participants over private FTP.
There is a chance that is is still public to non-participants. It may only be available for some months; or (rarely) it may be available in a non-standard location.
Deduplication and PDR Loader Configuration
The primary keys are:
The transaction type is "insert", which means that you will usually not see duplicate data. If you do, you can deduplicate based on the primary keys, without sorting first. Rows from different files with the same primary keys should have the same values.
| Report Name | Report Subtype | Version |
|---|---|---|
| METERDATA | TRK | 1 |
This means that the CSV files start with metadata columns:
Indexes
Links
This page as JSON (schema documented here, but may change)
Download a JSON Schema specification for this table.
Official AEMO schema documentation
Other Information
Package: HISTORICAL TABLES