Sigma and Data Pipeline allow you to analyze and export the same underlying data that’s accessible through the Stripe API, but through different interfaces. While the Stripe API provides programmatic access to your data, Sigma offers a SQL-based interface for custom queries and analysis, and Data Pipeline enables bulk data exports.
Additionally, Sigma and Data Pipeline provide access to certain data that isn’t available through the Stripe API, such as reports.
Data freshness
Sigma and Data Pipeline make most of your transaction data available to query within one day.
Sigma makes most of your Stripe transaction data available to query within three hours. All API activity is available to query approximately three hours after it occurs. For example, data from 12:00am UTC is available by 3:00am UTC on the same day.
Query data load times
The interface in the Dashboard displays the date and time of the last payments data. You can use data_load_time as a value in your queries to represent when data is most recently processed on your account. For example, if payment tables were last updated on , the data_load_time is interpreted as 00:00:00 +0000. At times, Sigma may reflect activity that is more recent than data_load_time. For example, a charge authorized just before midnight, but captured soon after, may show as captured.
Making data available requires additional time. You can use data_load_time as a value in your queries that represents when data is most recently processed on your account. Use this value to dynamically set a date range in your scheduled queries.
For example, consider the following scheduled query that returns a list of balance transactions created one month before data_load_time.
select
id,
amount,fee,
currency
from balance_transactions -- this table is the canonical record of changes to your Stripe balancewhere
created < data_load_time and
created >= data_load_time - interval '1' month
orderby created desclimit10
The following timeline illustrates how this works based on data availability:
Date
Timeline for results
data_load_time is interpreted as
The scheduled query includes transaction data through EOD
Query results are available on by 2pm UTC
Now, consider the following scheduled query that returns a list of charge_ids and interchange billing amounts associated with each fee balance debit created one month before data_load_time.
select
ic.charge_id,
ic.billing_currency,
ic.billing_amount,
ic.balance_transaction_id,
ic.balance_transaction_created_at
from icplus_fees as ic
join balance_transactions as bt
on ic.balance_transaction_id = bt.id
where bt.created >= data_load_time - interval '1' month
and bt.created < data_load_time
If this query is scheduled to recur daily, the following timeline illustrates when you can expect the results:
Date
Timeline for results
data_load_time is interpreted as 00:00:00 +0000
The scheduled query includes transaction data through EOD
Query results are available on by 2am UTC
Dataset freshness
View the following table for information on data freshness for specific datasets:
You can view the complete schema, which closely follows our API conventions, in a split-view format that shows details on table relationships. It displays all the available data that you can use in your queries, organized by category. Each category contains a set of tables that represents the available data. Many tables correspond to specific API objects, with each column representing a reported attribute. For example, the charges table represents information about Charge objects, which appears in the Payments section of the Dashboard.
You can select a table to expand it and reveal its available columns, along with a description of the type of data it contains (for example, Boolean , Varchar, and Foreign key). Hover the cursor over any column to reveal a description. Use the search field at the top of the schema to find specific tables and columns. When writing queries, refer to our API reference for additional context and values.
