Automate TikTok Ads Data to BigQuery or Snowflake

[1col]Why Centralize TikTok Ads Data in BigQuery or Snowflake?

Marketing leaders must unify performance data across all paid channels to achieve accurate reporting, attribution, and optimization. If TikTok Ads data is siloed, marketers lose the ability to measure true cross-channel ROAS, optimize creative, and leverage advanced analytics like MMM (Marketing Mix Modeling) and LTV (Lifetime Value) analysis. By integrating TikTok Ads into BigQuery or Snowflake, organizations can:

• [connection]Unify marketing data: Compare TikTok alongside Google Ads, Meta, and more.
• [analytics]Enable advanced analytics: Support attribution, incrementality, MMM, and LTV calculations.
• [automation]Activate data: Use segmentation, ML audiences, and automate activation via CDPs or reverse-ETL.
• [clean]Standardize and govern: Adopt warehouse-native schemas and governance practices.

Architecture Overview: TikTok Ads → Keboola → Warehouse

With Keboola, you build a repeatable pipeline:

1. Extract TikTok Ads reports with customizable metrics, dimensions, and date ranges.
2. Store raw data in Keboola Storage (staging tables).
3. Transform data (optional, e.g., dedupe or curate with SQL/dbt).
4. Write to BigQuery or Snowflake using purpose-built connectors.
5. Orchestrate and schedule with flows, notifications, and versioning.

Step-by-Step: TikTok Ads to BigQuery

• Set up TikTok Ads Extractor:
• Go to Components → Data Source → TikTok Ads
• Authorize with OAuth
• Configure report: Set advertiser IDs, report type, service type, data level, metrics, dimensions, date range, and output table
• Enable incremental loads for append
• Optional Transformation for Upserts:
• BigQuery writer appends data; implement upserts with a SQL MERGE transformation
• Partition and cluster tables for performance
• Configure BigQuery Writer:
• Paste Service Account JSON and set dataset
• Map storage tables to BigQuery, select Full or Incremental load
• Orchestrate with Flows:
• Chain extraction, transformation (optional), and writing
• Schedule runs (hourly/daily) and configure notifications

Step-by-Step: TikTok Ads to Snowflake

• Set up TikTok Ads Extractor: Same as above
• Configure Snowflake Writer:
• Connect to your Snowflake (or use Keboola-provisioned)
• Map storage tables, select Incremental (with primary key for upserts) or Full load
• Set column types and naming conventions
• Orchestrate with Flows: As in BigQuery setup

[3col]Optional: AI-Assisted Pipeline Creation with Keboola MCP

Accelerate setup by connecting Keboola MCP to an AI assistant (e.g., Claude). After configuring credentials, simply instruct the AI to build and run a TikTok Ads → Warehouse pipeline, including incremental loads and deduplication transformations. The AI can:

• [automation]Auto-discover and configure connectors
• [flow]Build and schedule flows
• [analytics]Validate results in storage and warehouse

Data Modeling Best Practices

• [decision]Key selection: For daily campaign, use (campaign_id, stat_time_day); for adgroup or ad, use respective IDs plus date
• [database]Recommended tables: tiktok_campaign_daily, tiktok_adgroup_daily, tiktok_ad_daily
• [analytics]Metrics/dimensions: Typical metrics include spend, impressions, clicks, conversions, ROAS. Always align dimension/metric combos to report type
• [information]Warehouse nuances: BigQuery requires transformation for upserts; Snowflake supports upserts natively if PK is set

Orchestration, Scheduling & Operations

• [flow]Flows chain Extractor → (Transform) → Writer
• [clock]Schedules can be set to match data freshness needs
• [exclamation]Notifications (email/webhook/Slack/Teams) alert on failures/warnings
• [version]Versioning enables change tracking and rollback
• [tool]Workspaces let you safely explore data and author SQL

Real-World Use Cases

• [analytics]Unified marketing analytics: Track spend and performance across all channels
• [star]Creative testing: Analyze ad/creative IDs for downstream impact
• [data-science]Advanced modeling: Calculate ROAS, run MMM, and LTV/cohort analysis
• [automation]ML-powered segmentation: Use warehouse ML tools and activate via CDPs
• [tool]Templates: Speed up onboarding with prebuilt Keboola templates

Tips, Limits & Troubleshooting

• [question]Ensure metrics/dimensions match report type and data level
• [clock]Use small date windows for large accounts to avoid API rate limits
• [sql]Remember BigQuery writer is append-only; use MERGE transformation for upserts
• [information]Respect Snowflake case sensitivity in table/column naming
• [exclamation]Set up robust notification and retry policies

Where to Find Official Documentation

• Keboola TikTok Ads connector docs
• Keboola BigQuery writer docs
• Keboola Snowflake writer docs
• Keboola Flows and Orchestration
• Keboola MCP and AI assistant integration

Get More from Your Marketing Data

By leveraging Keboola’s native connectors, scheduling, and orchestration, you automate TikTok Ads data integration and can seamlessly combine it with data from Google Ads, Meta (Facebook & Instagram) Ads, LinkedIn, Bing Ads, and more. This enables unified analytics, increases reliability, and empowers advanced, actionable insights — all while reducing manual overhead. Whether you prefer UI-driven setup or AI-assisted automation via Keboola MCP, the process is robust, scalable, and designed for real-world marketing data teams seeking true cross-channel visibility.