Google BigQuery Business Intelligence & Reporting

Knowi enables data discovery, query, aggregation, visualization and reporting automation from Google BigQuery along with other unstructured and structured datasources.


  1. Connect, extract and transform data from your Google BigQuery, using one of the following options:

    • Using our Cloud9Agent. This can securely pull data inside your network. See agent configuration for more details.
    • Through our UI to connect directly.
  2. Visualize and Automate your Reporting instantly.


If you are not a current Knowi user, check out our Google BigQuery Instant Reporting page to get started.


The following GIF image shows how to connect to Google BigQuery.

BigQuery Connect

  1. Login to Knowi and select Settings -> Datasources from the left down menu.

  2. Click on BigQuery. An authentication popup will be shown. Sign in to your Google BigQuery account to complete the OAuth process, where a secure token is exchanged between Google BigQuery and Knowi.

  3. After the authorization, select a Google BigQuery Project ID set up the datasource against.

  4. Save the Connection. Click on the "Configure Queries" link on the success bar.

Queries & Reports

  1. Set up Query to execute.

    BigQuery Query

    Report Name: Specify a name for the report.

    Queries can be auto-generated using our Data Discovery & Query Generator feature. You can also enter BigQuery queries directly. The query results can be optionally post-processed using Cloud9QL, a SQL-like syntax.

    Data Discovery:

    i. Select a table.

    ii. Select Metrics to track. Click on a metric to select aggregations (or date functions) on a given metric.

    iii. Select any optional dimensions to track. Dimensions are fields you want to group by.

    iv. Select any optional filters to set.

    Note that the queries are auto-generated when the Data Discovery feature is used.


    BigQuery Query: Modify or enter SQL queries directly.

    Cloud9QL: Optional SQL-Like post processor for the data returned by the SQL query. See Cloud9QL Docs for more details.

    Click 'Preview' to see the results.

    1. Scheduling: Configure how often this should be run. Select 'None' for a one time operation. The results are stored within Knowi.

    2. Overwrite Strategy (for scheduled query runs):

      Overwrite Strategies determines how the data is stored in Knowi:

      i. If empty, data will be added on to the existing data for this dataset within Knowi. Or,

      ii. "All": Any existing data for this dataset will be replaced by this results.

      iii. One or More Field Names (Example: "A,B,C"): A new record is created where the values of the combination of the field names do not exist, and, updates current records for the field grouping where it exists. For example, if this is set to say "Date, Type", existing data with the same Date and Type values will be updated with the latest data, and new records created when they do not exist.

    3. Click on 'Save' to complete setting up the report. This also sets up this data extraction on a schedule, if configured.

    4. Click on 'Dashboards' to access dashboards. You can drag and drop the newly created report from the widget list into to the dashboard.

Cloud9Agent Configuration

As an alternative to the UI based connectivity above, you can use Cloud9Agent inside your network to pull from Google BigQuery securely. See Cloud9Agent to download your agent along with instructions to run it.


  • Pull data using SQL.
  • Execute queries on a schedule, or, one time.

The agent contains a datasource_example_bigquery.json and query_example_bigquery.json under the examples folder of the agent installation to get you started.

  • Edit those to point to your database and modify the queries to pull your data.
  • Move it into the config directory (datasource_XXX.json files first if the Agent is running).

Datasource Configuration:

Parameter Comments
name Unique Datasource Name.
datasource Set value to bigquery
authRefreshToken OAuth Offline Token generated by Google BigQuery
projectId Project ID is a Google Unique identifier for the Google BigQuery enabled project. To determine project ID, login to your Google Cloud Console and navigate to your [Project]( page.

Query Configuration:

Query Config Params Comments
entityName Dataset Name Identifier
identifier A unique identifier for the dataset. Either identifier or entityName must be specified.
dsName Name of the datasource name configured in the datasource_XXX.json file to execute the query against. Required.
queryStr BigQuery query to execute. Required.
frequencyType One of minutes, hours, days,weeks,months. If this is not specified, this is treated as a one time query, executed upon Cloud9Agent startup (or when the query is first saved)
frequency Indicates the frequency, if frequencyType is defined. For example, if this value is 10 and the frequencyType is minutes, the query will be executed every 10 minutes
startTime Optional, can be used to specify when the query should be run for the first time. If set, the the frequency will be determined from that time onwards. For example, is a weekly run is scheduled to start at 07/01/2014 13:30, the first run will run on 07/01 at 13:30, with the next run at the same time on 07/08/2014. The time is based on the local time of the machine running the Agent. Supported Date Formats: MM/dd/yyyy HH:mm, MM/dd/yy HH:mm, MM/dd/yyyy, MM/dd/yy, HH:mm:ss,HH:mm,mm
c9QLFilter Optional post processing of the results using Cloud9QL. Typically uncommon against SQL based datastores.
overrideVals This enables data storage strategies to be specified. If this is not defined, the results of the query is added to the existing dataset. To replace all data for this dataset within Knowi, specify {"replaceAll":true}. To upsert data specify "replaceValuesForKey":["fieldA","fieldB"]. This will replace all existing records in Knowi with the same fieldA and fieldB with the the current data and insert records where they are not present.


Datasource Example:


Query Examples:

        "entityName" : "demoBigQuery",
        "dsName" : "demoBigQuery",
        "queryStr" : "select sum(Sent) as Sent, sum(Opened) as Opened, Week\nfrom\ngroup by Week\nlimit 1000",
        "c9QLFilter" : "",
        "overrideVals" : {
            "replaceAll" : true