How to edit historical data
The need for editing historical data
Editing historical data can be necessary for several reasons:
-
Error correction: Mistakes in data entry or processing can lead to inaccuracies. Correcting these errors ensures the data reflects true events or values.
-
Compliance and regulations: Certain industries have strict regulations that may require updates to historical data to remain compliant, especially if the data impacts financial reporting or legal obligations.
-
Improved analysis: As new insights or methodologies emerge, historical data may need to be adjusted to ensure analyses are relevant and accurate, allowing for better decision-making.
-
Data quality enhancement: Over time, organizations may refine their data quality standards. Updating historical records can help align them with current standards.
-
Business strategy changes: Shifts in business strategy might necessitate alterations in historical data for better reporting or strategic analysis.
By carefully managing these edits, you can ensure that the historical data remains a valuable asset for analysis and decision-making.
Warning
Editing historical data in a database should be approached with caution, as it can affect data integrity and accuracy.
How to edit historical data
How you edit the data depends on how the data was ingested by Zenskar:
- If you used usage event APIs to send data to Zenksar, refer to the section Edit historical data using usage-event APIs.
- If you used data-source connectors, refer to the following sections conditionally:
Edit historical data using usage-event APIs
Edit historical data when billable metrics are derived using the COUNT
SQL aggregate function
COUNT
SQL aggregate functionIn this example, you rent out your office space on a per day basis. The following data schema is used for sending usage events to Zenskar:
{
"data": {
"Id": "string",
"was_office_space_used": "string"
},
"timestamp": "timestamp",
"customer_id": "string"
}
Let us assume that the usage event APIs are used to send the following data to Zenskar:
data.Id | data.was_office_space_used | timestamp | customer_id |
---|---|---|---|
c01 | Yes | 2023-04-28T13:26:05.017000 | acc93335-aabb-43e9-aabb-138ac880b715 |
c02 | Yes | 2023-04-29T13:26:05.017000 | acc93335-aabb-43e9-aabb-138ac880b715 |
c03 | Yes | 2023-04-30T13:26:05.017000 | acc93335-aabb-43e9-aabb-138ac880b715 |
The billable metric, in this example, is the number of days the office space was used. The following SQL query uses COUNT
aggregate function on the data.was_office_space_used
column to calculate the billable metric.
SELECT
COALESCE(COUNT(CASE WHEN data.was_office_space_used = 'Yes' THEN 1 ELSE null END)) AS "quantity"FROM
your_table
WHERE
DATE("timestamp") >= DATE({{start_date}}) AND
DATE("timestamp") <= DATE({{end_date}}) AND
"customer_id" = CAST({{customer.external_id}} AS String)
Breakdown of the SQL query
SELECT
clause:
COALESCE(COUNT(CASE WHEN data.was_office_space_used = 'Yes' THEN 1 ELSE null END)) AS "quantity"
COUNT
: This function counts non-null values.CASE
statement: checks if the columndata.was_office_space_used
has the valueYes
. If it does, it returns 1; otherwise, it returns null.COALESCE
: this function returns the first non-null value in the list. If there are no instances wheredata.was_office_space_used
isYes
,COUNT
would return0
, andCOALESCE
ensures that it is treated as such.
FROM
clause:
FROM your_table
Replace your_table
with the actual name of the table containing your data.
WHERE
Clause:
WHERE
DATE("timestamp") >= DATE({{start_date}}) AND
DATE("timestamp") \<= DATE({{end_date}}) AND
"customer_id" = CAST({{customer.external_id}} AS String)
DATE("timestamp")
: This converts the timestamp field to a date format, ensuring the comparison works with date values.DATE({{start_date}}) and DATE({{end_date}})
: These placeholders represent the start and end dates for filtering.customer_id = CAST({{customer.external_id}} AS String)
: This checks if thecustomer_id
in the table matches the providedexternal_id
, which is also cast to a string to ensure type compatibility.
Method 1: modify the data schema by introducing a new data field
Note
The examples given in this document are for reference only. The ideas given herein are guidelines and not rules. You must adapt the ideas given in this document as per your use case.
You realize that an error was made in data entry. To correct the error, you decide to introduce a new field in the data schema. In this example, we will use a boolean
type.
{
"data": {
"Id": "string",
"was_office_space_used": "float",
"deduct_from_count": "bool"
},
"timestamp": "timestamp",
"customer_id": "string"
}
The following is the database table schema that Zenskar will create.
data.Id | data.was_office_space_used | data.deduct_from_count | timestamp | customer_id |
---|---|---|---|---|
c01 | Yes | 2023-04-28T13:26:05.017000 | acc93335-aabb-43e9-aabb-138ac880b715 | |
c02 | Yes | 2023-04-29T13:26:05.017000 | acc93335-aabb-43e9-aabb-138ac880b715 | |
c03 | Yes | 2023-04-30T13:26:05.017000 | acc93335-aabb-43e9-aabb-138ac880b715 | |
c04 | No | True | 2023-05-01T13:26:05.017000 | acc93335-aabb-43e9-aabb-138ac880b715 |
The following SQL query calculates the adjusted count of data.was_office_space_used
column by taking into account the count of the newly introduced data.deduct_from_count
column:
SELECT
COALESCE(COUNT(CASE WHEN data.was_office_space_used = 'Yes' THEN 1 ELSE null END)) - COUNT(NULLIF(data.deduct_from_count,'')) AS "quantity"
FROM
your_table
WHERE
DATE("timestamp") >= DATE({{start_date}}) AND
DATE("timestamp") <= DATE({{end_date}}) AND
"customer_id" = CAST({{customer.external_id}} AS String)
Breakdown of the SQL query
SELECT
clause:
COALESCE(...)
: This function returns the first non-null value in its list of arguments. Here, it is used to ensure that if the result of the subtraction is null, which can happen if there are no rows, it will return0
instead.COUNT(data.was_office_space_used)
: This counts the number of non-null entries in thedata.was_office_space_used
column.COUNT(NULLIF(data.deduct_from_count, ''))
: TheNULLIF
function returnsNULL
ifdata.deduct_from_count
is an empty string (''), effectively counting only non-empty entries. So, this counts the number of non-empty entries indata.deduct_from_count
. The entire expression calculates the difference between the count ofdata.was_office_space_used
and the count of non-emptydata.deduct_from_count
.
FROM
clause specifies the table from which the data is being queried.
WHERE
clause:
DATE("timestamp") >= DATE({{start_date}}) AND DATE("timestamp") <= DATE({{end_date}})
: this filters the results to include only those records where the timestamp is within the specified date range, defined by the variables{{start_date}}
and{{end_date}}
."customer_id" = CAST({{customer.external_id}} AS String)
: This filters the results to include only rows where thecustomer_id
matches theexternal_id
, after convertingexternal_id
to a string.
Method 2: introduce a new usage event for deduction
Let us assume that you used the following data schema for sending usage events:
{
"data": {
"Id": "string",
"Premises_used": "string"
},
"timestamp": "timestamp",
"customer_id": "string"
}
Let us assume that the usage event APIs are used to send the following data to Zenskar:
data.Id | data.Premises_used | timestamp | customer_id |
---|---|---|---|
c01 | Yes | 2023-04-28T13:26:05.017000 | acc93335-aabb-43e9-aabb-138ac880b715 |
c02 | Yes | 2023-04-29T13:26:05.017000 | acc93335-aabb-43e9-aabb-138ac880b715 |
c03 | Yes | 2023-04-30T13:26:05.017000 | acc93335-aabb-43e9-aabb-138ac880b715 |
You may define another schema that can be used for deduction:
{
"data": {
"Id": "string",
"Error": "string"
},
"timestamp": "timestamp",
"customer_id": "string"
}
Let us assume that the usage event APIs are used to send the following data to Zenskar for deduction:
data.Id | data.Error | timestamp | customer_id |
---|---|---|---|
c01 | Yes | 2023-05-15T13:26:05.017000 | acc93335-aabb-43e9-aabb-138ac880b715 |
c02 | Yes | 2023-05-29T13:26:05.017000 | acc93335-aabb-43e9-aabb-138ac880b715 |
SELECT
(SELECT COUNT(data.was_office_space_used) FROM your_table) -
(SELECT COUNT(data.Error) FROM your_error_table) AS "quantity";
Edit historical data when billable metrics are derived using the SUM
aggregate function
SUM
aggregate functionLet us assume that you used the following data schema for sending usage events:
{
"data": {
"Id": "string",
"Usage": "float"
},
"timestamp": "timestamp",
"customer_id": "string"
}
Let us assume that the usage event APIs are used to send the following data to Zenskar:
data.Id | data.Usage | timestamp | customer_id |
---|---|---|---|
c01 | 301.4 | 2023-04-28T13:26:05.017000 | acc93335-aabb-43e9-aabb-138ac880b715 |
c02 | 500 | 2023-04-29T13:26:05.017000 | acc93335-aabb-43e9-aabb-138ac880b715 |
c03 | 104.8 | 2023-04-30T13:26:05.017000 | acc93335-aabb-43e9-aabb-138ac880b715 |
You realize that for data.Id
equaling c02
should be 475 and not 500. In this case, you can make another usage event API call to add another unique data.Id
with a data.Usage
of -25
, as shown below.
data.Id | data.Usage | timestamp | customer_id |
---|---|---|---|
c01 | 301.4 | 2023-04-28T13:26:05.017000 | acc93335-aabb-43e9-aabb-138ac880b715 |
c02 | 500 | 2023-04-29T13:26:05.017000 | acc93335-aabb-43e9-aabb-138ac880b715 |
c03 | 104.8 | 2023-04-30T13:26:05.017000 | acc93335-aabb-43e9-aabb-138ac880b715 |
c04 | -25 | 2023-05-01T13:26:05.017000 | acc93335-aabb-43e9-aabb-138ac880b715 |
When aggregating using the SUM
function, the negative entry in the data.Usage
column will account for the data error.
SELECT
SUM(data.Usage) AS "quantity"
FROM
your_table
WHERE
DATE("timestamp") >= DATE({{start_date}}) AND
DATE("timestamp") <= DATE({{end_date}}) AND
"customer_id" = CAST({{customer.external_id}} AS String)
Edit historical data when billable metrics are derived using the MAX
, AVG
, MIN
, and UNIQUE COUNT
aggregate functions
MAX
, AVG
, MIN
, and UNIQUE COUNT
aggregate functionsTechniques similar to the ones mentioned for COUNT
and SUM
aggregate functions can be used for MAX,
AVG,
MIN, and
UNIQUE COUNT` aggregate functions.
Editing historical data using ROW_NUMBER() OVER (PARTITION BY)
SQL function
ROW_NUMBER() OVER (PARTITION BY)
SQL functionThe ROW_NUMBER()
function in SQL is a window function that assigns a unique sequential integer to rows within a partition of a result set. It is often used to uniquely identify rows within groups of data. The PARTITION BY
clause is used to define how the rows are divided into groups.
Breakdown of ROW_NUMBER() OVER (PARTITION BY)
ROW_NUMBER() OVER (PARTITION BY)
ROW_NUMBER()
: generates a unique number for each row in the result set, starting from 1 for the first row in each partition.OVER
: specifies the window over which the function operates. It can include:PARTITION BY
: defines the groups (partitions) within the data. Each partition is treated independently when generating row numbers.
Let us assume that the usage event APIs are used to send the following data to Zenskar:
data.Id | data.Usage | timestamp | customer_id |
---|---|---|---|
c01 | 301.4 | 2023-04-28T13:26:05.017000 | acc93335-aabb-43e9-aabb-138ac880b715 |
c02 | 500 | 2023-04-29T13:26:05.017000 | acc93335-aabb-43e9-aabb-138ac880b715 |
c03 | 104.8 | 2023-04-30T13:26:05.017000 | acc93335-aabb-43e9-aabb-138ac880b715 |
You realize that for data.Id
equaling c02
, data.Usage
should be 475 and not 500. In this case, you can make another usage event API call to add a row with data.Id
equaling c02
, data.Usage
of 475
, and latest timestamp
, as shown below.
data.Id | data.Usage | timestamp | customer_id |
---|---|---|---|
c01 | 301.4 | 2023-04-28T13:26:05.017000 | acc93335-aabb-43e9-aabb-138ac880b715 |
c02 | 500 | 2023-04-29T13:26:05.017000 | acc93335-aabb-43e9-aabb-138ac880b715 |
c02 | 475 | 2023-05-01T13:26:05.017000 | acc93335-aabb-43e9-aabb-138ac880b715 |
c03 | 104.8 | 2023-04-30T13:26:05.017000 | acc93335-aabb-43e9-aabb-138ac880b715 |
The following query deduplicates the readings based on the most recent timestamp.
WITH DeduplicatedReadings AS (
SELECT
data.Id,
data.Usage,
timestamp,
customer_id,
ROW_NUMBER() OVER (PARTITION BY data.Id ORDER BY timestamp DESC) AS rn
FROM
device_readings
)
SELECT
data.Id,
data.Usage,
timestamp,
customer_id,
FROM
DeduplicatedReadings
WHERE
rn = 1;
The above SQL query will give the following result:
data.Id | data.Usage | timestamp | customer_id |
---|---|---|---|
c01 | 301.4 | 2023-04-28T13:26:05.017000 | acc93335-aabb-43e9-aabb-138ac880b715 |
c02 | 475 | 2023-05-01T13:26:05.017000 | acc93335-aabb-43e9-aabb-138ac880b715 |
c03 | 104.8 | 2023-04-30T13:26:05.017000 | acc93335-aabb-43e9-aabb-138ac880b715 |
Edit historical data using data-source connectors
Edit historical data when data source supports remote querying
Edit data in your database and let Zenskar take care of the rest of the workflow.
Edit historical data when data source does not support remote querying
Edit data in your database and let the periodic sync take care of the syncing the updated data with Zenskar.
Updated 3 days ago