How does Kondado replicate Stilingue data to BigQuery?

Kondado connects directly to your Stilingue account using your API credentials, then automatically extracts sentiment metrics from your selected pipelines. The platform loads this data into your specified BigQuery destination on the schedule you configure, handling the replication process without requiring manual exports or custom scripts.

What Stilingue sentiment metrics can I analyze in BigQuery?

You can access 139 fields across five pipelines including Sentiment by Terms, Sentiment by Themes, and Sentiment by Groups. Each pipeline provides total counts alongside positive, negative, and neutral classifications, plus total_polarity_classified metrics to measure sentiment distribution across your social listening campaigns.

How often does Stilingue data update in my BigQuery warehouse?

Updates occur on a configurable schedule that you set during pipeline configuration, ranging from every five minutes to daily intervals. This flexibility allows agency analysts to monitor breaking campaigns with frequent updates, while strategic reports can rely on daily aggregations to reduce query costs.

Can I combine Stilingue social listening data with other sources in BigQuery?

Yes, BigQuery serves as a central repository where Stilingue sentiment data can join CRM records from PostgreSQL , advertising spend from Google Ads, or sales data from MySQL. This unified approach enables correlation analysis between social sentiment and actual business outcomes.

What format does Stilingue data take when it arrives in BigQuery?

Data arrives as structured datasets with standard data types compatible with SQL analysis, ready for immediate querying or connection to visualization tools. Each pipeline creates distinct tables that preserve the original field names and metric structures from your Stilingue configuration.

Do I need SQL knowledge to use Stilingue data in BigQuery?

While SQL enables advanced analysis, you can also connect your BigQuery data to Looker Studio or Power BI for drag-and-drop visualization. Many users build automated reports by connecting their BigQuery tables directly to these business intelligence platforms without writing complex queries.

Can I send Stilingue data to BigQuery and other destinations simultaneously?

Absolutely, Kondado supports multiple destinations for the same Stilingue data source. You can replicate sentiment data to BigQuery for deep analysis while simultaneously sending subsets to Google Sheets for quick stakeholder updates or to PostgreSQL for operational dashboards.

Send data from Stilingue to BigQuery

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Send Stilingue Data to BigQuery Automatically

Kondado enables you to replicate Stilingue sentiment data directly into BigQuery without writing complex code or managing API connections manually. Configure your data source once, then let automated updates deliver comprehensive social listening metrics to your data warehouse on a schedule you control. Whether you need updates every five minutes for active campaigns or once daily for strategic reporting, your BigQuery datasets stay current with customer sentiment trends and brand perception shifts.

Kondado provides a direct integration between Stilingue and BigQuery, replicating sentiment metrics from five available pipelines on a configurable schedule to support analytics and reporting workflows.

Once your Stilingue data lands in BigQuery, you can combine social listening insights with CRM records, sales figures, or advertising data to understand the full customer journey. Build custom reports in Looker Studio or Power BI, or query directly using SQL to analyze brand perception alongside business performance metrics and conversion data.

Our prices start from $19 USD/month, and you can try Kondado for free for 14 days with no credit card required

Available Stilingue Pipelines for BigQuery

Replicate the Sentiment by Terms pipeline to track how specific keywords perform across social channels, enabling you to measure campaign effectiveness and brand mention trends directly in BigQuery. The Sentiment by Themes pipeline delivers categorized emotional responses, perfect for understanding brand health trends over time and identifying emerging issues before they escalate. With Sentiment by Hashtags data in your warehouse, you can correlate viral campaign moments with website traffic or conversion data stored in the same BigQuery environment to calculate true social ROI.

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Stilingue data available for BigQuery

available pipelines

139

extractable fields

Available integrations

Integration	Description
Sentiment by Terms	Analyzes sentiment towards specific terms, presenting metrics such as total, positive, negative, and neutral, along with total_polarity_classified.
Sentiment by Themes	Evaluates sentiment towards themes, including metrics such as total, positive, negative, and neutral, along with total_polarity_classified.
Sentiment by Hashtags	Examines sentiment associated with hashtags, featuring metrics such as total, positive, negative, and neutral, along with total_polarity_classified.
Sentiment by Tags	Investigates sentiment towards tags, presenting metrics such as total, positive, negative, and neutral, along with total_polarity_classified.
Sentiment by Groups	Analyzes sentiment towards groups, with metrics such as total, positive, negative, and neutral, along with total_polarity_classified.

Sentiment by Terms

Analyzes sentiment towards specific terms, presenting metrics such as total, positive, negative, and neutral, along with total_polarity_classified.

Sentiment by Themes

Evaluates sentiment towards themes, including metrics such as total, positive, negative, and neutral, along with total_polarity_classified.

Sentiment by Hashtags

Examines sentiment associated with hashtags, featuring metrics such as total, positive, negative, and neutral, along with total_polarity_classified.

Sentiment by Tags

Investigates sentiment towards tags, presenting metrics such as total, positive, negative, and neutral, along with total_polarity_classified.

Sentiment by Groups

Analyzes sentiment towards groups, with metrics such as total, positive, negative, and neutral, along with total_polarity_classified.

See all available data →

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How to send Stilingue data to BigQuery

Sync data automatically — no code, no manual exports.

Connect Stilingue as data source

Authenticate your Stilingue account in Kondado by entering your API credentials, then select the sentiment pipelines you want to replicate from the available options.

Configure BigQuery destination

Set up your BigQuery project and dataset details in Kondado, specifying the destination location for your replicated Stilingue data within your Google Cloud environment.

Select data and schedule updates

Choose specific pipelines like Sentiment by Terms or Sentiment by Hashtags, then define your update frequency to automate data delivery to BigQuery without manual intervention.

Try out all the features for free for 14 days