What BigQuery is
BigQuery is Google Cloud's managed, serverless data warehouse, announced in 2010 and generally available since 2011. Teams load or stream data into it and query it with SQL without managing servers, and can build machine learning models with SQL in the same place.
Data and finance teams use it for reporting and analysis, and each dataset is stored in a location you choose: a single region or a multi-region such as Europe. That is why it is one of the analytics destinations our Ledger and Admin backoffice modules connect to.
What it does in your platform
Ledger
Receives every ledger entry, balance and reconciliation result, so finance and data teams answer their questions in SQL instead of asking for exports. The ledger stays the book of record; BigQuery is where the analysis happens.
Admin backoffice
Receives the backoffice's records of customers, transactions and cases, so the dashboards your teams already use draw on the same data support and compliance act on.
How the connection works
One connector writes the platform's data to BigQuery through its Storage Write API, using a Google Cloud service account that may write to your datasets and nothing more.
An entry is booked
A customer's payment is recorded in the ledger as balanced debits and credits, in real time.
It is written once
The connector appends the entry to your table through the Storage Write API. With stream offsets, BigQuery writes each row exactly once, even when a write is retried.
It stays in its location
The dataset lives where you chose, a region or a multi-region such as Europe, and BigQuery keeps its copies within that location.
Context follows
Balances and reconciliation results from the ledger, and customers, transactions and cases from the backoffice, arrive in their own tables.
Teams query it
Analysts use SQL, notebooks or a BI tool such as Metabase on the same numbers the platform runs on.
Next to other providers
BigQuery does not replace the ledger or your accounting system. The ledger stays the book of record, Xero, QuickBooks, NetSuite or SAP receive the daily journals, and BigQuery holds the full detail for analysis.
If your data team works in Snowflake, or a PostgreSQL reporting database is enough to start, those are connectors of their own. Changing the destination later does not touch your apps or the ledger.
When BigQuery fits best
A strong fit when
- Your company already runs on Google Cloud, or your data team works in BigQuery.
- You want analysts to query every entry and balance with SQL, without exports.
- You need the data stored in a chosen region, or within Europe.
Also worth a look
- Snowflake, if your data team runs on it or on another cloud.
- A PostgreSQL reporting database, when a smaller setup is enough.
How we get you live
The contract
We help you get your Google Cloud account and contract in place for BigQuery, or connect the project your data team already uses.
The datasets
We create the datasets in the location you choose and agree with your data team which tables arrive, from ledger entries to backoffice cases.
The keys
The service account's credentials stay in your platform's secrets, and its role lets it write to your datasets and nothing else.
A full test run
A test environment writes into separate test datasets first, and your analysts check the tables and their totals before production data flows.
